VOLATILE AND SENSORY PROFILING TO MINIMIZE OFF-FLAVORS IN PULSES By Kaveri Ponkshe A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Food Science —Master of Science 2025 ABSTRACT Pulses are a nutrient-dense and sustainable protein source; however, only 17% of Americans consume them at or above the level recommended in dietary guidelines. Incorporating pulse flour into wheat-based products can promote consumption, but adoption remains limited due to undesirable flavors. Volatile organic compounds (VOCs) responsible for these off-flavors primarily include aldehydes, alcohols, ketones, acids, pyrazines, and sulfur compounds. Understanding the factors influencing VOC formation and their impact on sensory perception is critical for improving the quality of pulse-based products. To examine the effects of cultivar, processing (roasting and boiling), and harvest year on the volatile composition of eight pulse cultivars, headspace-solid phase microextraction gas chromatography-mass spectrometry (HS- SPME-GC-MS) was used. Processing trade-offs for mitigating undesirable flavors were assessed by pre-treating some of the pulses by roasting, followed by milling and cooking into model products (porridges), as well as soaking and boiling to evaluate changes in volatile concentrations. Additionally, cultivar differences were analyzed to identify variations in volatile profiles. The impact of crop year was assessed by comparing seven common bean (Phaseolus vulgaris) cultivars grown in Michigan in 2022 and 2023 to one commercially sourced chickpea (Cicer arietinum) grown in 2022. Roasted and non-roasted: flours and model product and boiled pulses prepared from each of the eight cultivars were analyzed by HS-SPME-GC-MS. Hierarchical clustering (HCA) and principal component analysis (PCA) revealed clustering based on harvest year and distinct volatile profiles among cultivars based on seed coat color. Roasting and boiling influenced VOC composition, with variations observed across different compound classes. To further investigate how variations in volatile profiles due to cultivar and processing treatments impact sensory perception, descriptive sensory analysis (DA) was conducted. Given the high cost of sensory panel testing, this study also evaluated the effectiveness of instrumental techniques such as GC-MS and electronic nose (e-nose) in predicting sensory attributes. PCA indicated that sensory variability among cultivars was driven by seed coat color which influenced both appearance and flavor, while HCA indicated that samples with shared sensory attributes clustered based on processing treatment. Pearson’s correlation analysis revealed stronger correlations of e-nose discriminant ions with DA than GC-MS. By integrating sensory and instrumental analyses, this research supports efforts to improve consumer acceptance and increase pulse flour consumption in food products. ACKNOWLEDGMENTS I would like to express my deepest gratitude to Dr. Emily Mayhew for her mentorship over the past two years. Your kindness, intelligence, and belief in me—even when I doubted myself—have been invaluable throughout my master’s journey. I have learned so much from you, not only about sensory science but also about professional growth and resilience. Your dedication to helping your students connect with industry professionals and advocating for us is truly inspiring. I am sincerely grateful to the FSHN department and Rohini Desai Mulchandani Graduate Fellowship for giving me the opportunity to study at Michigan State University. I hope to honor this legacy by following in her footsteps and giving back to society. A special thank you to Aislinn for patiently answering our endless questions with kindness. I also extend my appreciation to the Graduate Student Association (GSA) for their support, especially in providing valuable feedback on my presentations, which significantly built my confidence in sharing my research. A heartfelt thank you to Hannah for being my first friend in the U.S., a supportive lab mate, and now an incredible lab manager. Aubrey, for helping with coding challenges and offering support when I needed it most. Mariano and Somneang, for always lending a hand and lifting my spirits during difficult times. I am incredibly thankful to my undergraduate assistants, Lily and Anna, for their outstanding work in sample preparation, and to all the members of the Mayhew Lab for their reliability and collaboration. I deeply appreciate Dr. Randy Beaudry for training me in GC-MS and making me feel like part of his lab. I also extend my gratitude to Dr. Ilce G. Medina-Meza, Dr. Zeynep Ustunol, and Dr. Karen Cichy for their insightful guidance on my research and for helping me effectively communicate my results. A special thank you to my sister Aishwarya for being my home away from home. Your love has grounded me through the highs and lows of graduate school, and I’m endlessly grateful for your presence in my life. To Abhijeet—thank you for your constant support and kindness. A huge shoutout to my little niece Avani, whose bright smile never fails to make my day. iii To my parents—Mom and Dad—your sacrifices and endless belief in me have been the foundation of everything I’ve accomplished. I hope this milestone brings you as much pride as I feel gratitude. To Granny and Grandpa—your prayers and love have been with me every step of the way. Thank you for your quiet strength and for always believing in me. To my dearest friends—Vaishnavi, Renuka, and Anjali—thank you for being my safe space. From late-night vent sessions to spontaneous laughter and everything in between, you’ve brought light and laughter into my life exactly when I needed it. This journey would not have been the same without each of you. Thank you, from the bottom of my heart. iv TABLE OF CONTENTS Chapter 1: Introduction ................................................................................................................... 1 REFERENCES ................................................................................................................... 4 Chapter 2: Literature review ........................................................................................................... 7 REFERENCES ................................................................................................................. 38 Chapter 3: Effect of crop year, processing, and cultivar on volatile composition in pulses and pulse flours analyzed by headspace-solid phase microextraction gas chromatography-mass spectrometry .................................................................................................................................. 56 REFERENCES ................................................................................................................. 87 Chapter 4: Evaluating the impact of cultivar and processing on pulse off-flavor through descriptive analysis, GC-MS, and e-nose ..................................................................................... 93 REFERENCES ............................................................................................................... 123 Chapter 5: Conclusion................................................................................................................. 129 APPENDIX ..................................................................................................................... 131 v Rationale and Significance Chapter 1: Introduction Pulses, including dry beans, peas, chickpeas, and lentils, are nutrient-dense crops with significant potential to enhance food security, sustainability, and dietary health (Calles, 2016; Mitchell et al., 2009). They are an inexpensive source of protein and provide complex carbohydrates, fiber, and essential vitamins and minerals, making them a valuable component of plant-based diets (J. I. Boye & Ma, 2012). Additionally, replacing 50% of animal-based protein with plant-based sources, including legumes, could reduce the agricultural and food production greenhouse gas emissions by approximately 30%, supporting environmental sustainability in food systems (Springmann et al., 2018). Although pulses offer numerous benefits, their consumption in the U.S. remains low. The 2025 Dietary Guidelines Advisory Committee reported that 83% of Americans consume pulses below the recommended intake of 2.5 cups per week (2025 Dietary Guidelines Advisory Committee, 2024; Garden-Robinson & West, 2023) primarily due to a lack of knowledge on cooking as well as an aversion to the taste and texture of pulses (Doma et al., 2019; Winham et al., 2020). To increase consumption, expanding the use of pulse flour in convenience products offers a convenient, gluten-free alternative to wheat flour while reducing the lengthy cooking times required for dry pulses (Shevkani et al., 2022; Shukla et al., 2023). However, the undesirable flavor profile of pulses limits their widespread acceptance (Borsuk, 2011; Jeong et al., 2021; Kaya et al., 2018; Niva et al., 2017; Polat et al., 2020; Roland et al., 2017; Zare, 2011). Food flavor arises from the interaction of aroma, taste, and oral sensations, with volatile organic compounds (VOCs) primarily influencing aroma (Menis-Henrique et al., 2019). In pulses, off-flavors, often described as "beany," "green," or "earthy" are mainly associated with aldehydes, alcohols, and ketones, while bitterness and astringency result from sapid-glycosylated compounds such as saponins and phenolic compounds, including isoflavones, flavonols, and phenolic acids (Damodaran & Arora, 2013; MacLeod et al., 1988; Roland et al., 2017). The abundance and composition of volatile compounds in pulses are influenced by multiple factors, including cultivar, growing location, crop year, and processing treatments (Azarnia et al., 2011; Ma et al., 2016; N. Singh, 2017). Additionally, storage conditions significantly impact volatile formation, with exposure to heat, light, and oxygen accelerating the production of off- flavored volatile compounds (Azarnia et al., 2011). As a result, off-flavors vary widely across 1 cultivars and processing methods, imparting distinct sensory experiences depending on the species and treatment (Bassett et al., 2021; Ma et al., 2013; Mcwatters & Heaton, 1979). Despite the widespread production and consumption of common beans (Phaseolus vulgaris, L.) (FAO, 2024; White et al., 2022) much of the existing research on pulse volatiles has focused on peas, chickpeas and faba beans (Roland et al., 2017; Saffarionpour, 2024). Studies on common beans primarily examine their volatile composition in boiled form, with limited investigation into how processing methods such as milling into flour influence their volatile profile and sensory attributes. Furthermore, the impact of final cooking steps on volatile compound changes and sensory perception remains underexplored in common beans. This gap highlights the need for research on cultivar selection and processing to mitigate off-flavors while preserving pulse quality. Traditionally, sensory evaluation has been used to analyze taste and aroma attributes in food (Ashurst, 1999; Lopetcharat & McDaniel, 2005). However, sensory analysis is time-consuming, costly, and impractical for large-scale assessments. To address these challenges, analytical techniques such as gas chromatography-mass spectrometry (GC-MS) and Headspace-solid phase microextraction (HS-SPME) have been widely adopted for extracting volatiles, offering high sensitivity, robust identification capabilities and quantification (Jansen et al., 2011). While individual volatiles have distinct odors, overall aroma perception results from complex interactions within the volatilome, highlighting the need for more advanced profiling techniques. Electronic nose (e-nose) instruments have gained attention for their ability to rapidly and objectively analyze volatile profiles, making them valuable for flavor monitoring and large-scale sample screening (Ciosek et al., 2004, 2006; Deisingh et al., 2004; Rodríguez Méndez et al., 2010). However, comparative studies between traditional VOC extraction techniques and novel analytical tools in pulse-based products remain limited. Further research is needed to assess the ability of instrumental techniques to accurately represent the overall flavor perception of pulses. 2 Objectives Therefore, the objectives of this study were to: 1. Measure the effect of cultivar, processing (roasting, boiling), and crop year on the volatile composition of pulse samples using HS-SPME-GC-MS. 2. Characterize the effect of cultivar and processing on the sensory attributes of pulse samples through descriptive sensory analysis. 3. Identify chemical markers associated with off-flavors using instrumental techniques (HS- SPME-GC-MS and e-nose). By examining the impact of cultivar variation and processing methods, this research seeks to identify cultivars with milder flavor profiles and assess the sensory trade-offs involved in processing strategies for reducing off-flavors. Additionally, comparative studies between analytical techniques aim to explore rapid screening that can enhance the sensory quality of pulse- based food products, ultimately increasing consumer acceptance and consumption. 3 Ashurst, P. R. (1999). Food Flavorings (3rd ed.). Aspen Publishers. REFERENCES Azarnia, S., Boye, J. I., Warkentin, T., & Malcolmson, L. (2011). Changes in volatile flavour compounds in field pea cultivars as affected by storage conditions. International Journal of Food Science and Technology, 46(11), 2408–2419. https://doi.org/10.1111/j.1365- 2621.2011.02764.x Bassett, A., Kamfwa, K., Ambachew, D., & Cichy, K. (2021). Genetic variability and genome- wide association analysis of flavor and texture in cooked beans (Phaseolus vulgaris L.). Theoretical and Applied Genetics, 134, 959–978. Borsuk, Y. (2011). Incorporation of pulse flours with coarse and fine particle size milled from green lentils (Lens culinars), yellow peas (Pisum sativum L.), navy beans (Phaselous vulgaris L.), and pinto beans (Phaselous vulgari L.) into baked products. University of Manitoba (Canada). Boye, J. I., & Ma, Z. (2012). Finger on the pulse. Food Science & Technology (London), 26(2), 20–24. Calles, T. (2016). The international year of pulses: what are they and why are they important. Agriculture for Development, 26, 40–42. Ciosek, P., Brzózka, Z., & Wróblewski, W. (2004). Classification of beverages using a reduced sensor array. Sensors and Actuators B: Chemical, 103(1–2), 76–83. Ciosek, P., Brzózka, Z., & Wróblewski, W. (2006). Electronic tongue for flow-through analysis of beverages. Sensors and Actuators B: Chemical, 118(1–2), 454–460. Committee, 2025 Dietary Guidelines Advisory. (2024). Scientific Report of the 2025 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Health and Human Services and Secretary of Agriculture. https://doi.org/10.52570/DGAC2025 Damodaran, S., & Arora, A. (2013). Off-flavor precursors in soy protein isolate and novel strategies for their removal. Annual Review of Food Science and Technology, 4(1), 327–346. Deisingh, A. K., Stone, D. C., & Thompson, M. (2004). Applications of electronic noses and tongues in food analysis. International Journal of Food Science & Technology, 39(6), 587– 604. Doma, K. M., Farrell, E. L., Leith-Bailey, E. R., Soucier, V. D., & Duncan, A. M. (2019). Motivators, Barriers and Other Factors Related to Bean Consumption in Older Adults. Journal of 397–413. https://doi.org/10.1080/21551197.2019.1646690 Gerontology Geriatrics, Nutrition 38(4), and in FAOSTAT. (n.d.). Food and Agriculture Organization of the United Nations. FAOSTAT. Available Online: Https://Www.Fao.Org/Faostat/En/#data/QCL (Accessed on 11 November 2024). Garden-Robinson, J., & West, R. (2023). Medical Research Archives |https. https://doi.org/10.18103/mra.v 4 Jansen, R. M. C., Wildt, J., Kappers, I. F., Bouwmeester, H. J., Hofstee, J. W., & Van Henten, E. J. (2011). Detection of diseased plants by analysis of volatile organic compound emission. Annual Review of Phytopathology, 49(1), 157–174. Jeong, D., Hong, J. S., Liu, Q., Choi, H., & Chung, H. (2021). The effects of different levels of heat‐treated legume flour on nutritional, physical, textural, and sensory properties of gluten‐ free muffins. Cereal Chemistry, 98(2), 392–404. Kaya, E., Yılmaz Tuncel, N., & Tuncel, N. B. (2018). Utilization of lentil, pea, and faba bean hulls in Turkish noodle production. Journal of Food Science and Technology, 55, 1734–1745. Lopetcharat, K., & McDaniel, M. (2005). Sensory analysis of foods. Ma, Z., Boye, J. I., Azarnia, S., & Simpson, B. K. (2016). Volatile Flavor Profile of Saskatchewan Grown Pulses as Affected by Different Thermal Processing Treatments. International Journal of Food Properties, 19(10), 2251–2271. https://doi.org/10.1080/10942912.2015.1121494 Ma, Z., Boye, J. I., Fortin, J., Simpson, B. K., & Prasher, S. O. (2013). Rheological, physical stability, microstructural and sensory properties of salad dressings supplemented with raw and thermally treated lentil flours. Journal of Food Engineering, 116(4), 862–872. MacLeod, G., Ames, J., & Betz, N. L. (1988). Soy flavor and its improvement. Critical Reviews in Food Science & Nutrition, 27(4), 219–400. Mcwatters, K. H., & Heaton, E. K. (1979). Quality characteristics of ground beef patties extended with moist-heated and unheated seed meals. Journal of the American Oil Chemists’ Society, 56(1), A86–A90. Menis-Henrique, M. E. C., Janzantti, N. S., Andriot, I., Sémon, E., Berdeaux, O., Schlich, P., & Conti-Silva, A. C. (2019). Cheese-flavored expanded snacks with low lipid content: Oil effects on the in vitro release of butyric acid and on the duration of the dominant sensations of the products. Lwt, 105, 30–36. Mitchell, D. C., Lawrence, F. R., Hartman, T. J., & Curran, J. M. (2009). Consumption of Dry Beans, Peas, and Lentils Could Improve Diet Quality in the US Population. Journal of the American Dietetic Association, 109(5), 909–913. https://doi.org/10.1016/j.jada.2009.02.029 Niva, M., Vainio, A., & Jallinoja, P. (2017). Barriers to increasing plant protein consumption in Western populations. In Vegetarian and plant-based diets in health and disease prevention (pp. 157–171). Elsevier. Polat, H., Capar, T. D., Inanir, C., Ekici, L., & Yalcin, H. (2020). Formulation of functional crackers enriched with germinated lentil extract: A Response Surface Methodology Box- Behnken Design. LWT, 123, 109065. Rodríguez Méndez, M. L., Apetrei, C., Apetrei, I. M., Villanueva, S., & Saja Sáez, J. A. de. (2010). Combination of an e-nose, and e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Roland, W. S. U., Pouvreau, L., Curran, J., Van De Velde, F., & De Kok, P. M. T. (2017). Flavor aspects of pulse ingredients. In Cereal Chemistry (Vol. 94, Issue 1, pp. 58–65). American Association of Cereal Chemists. https://doi.org/10.1094/CCHEM-06-16-0161-FI 5 Saffarionpour, S. (2024). Off-Flavors in Pulses and Grain Legumes and Processing Approaches for Controlling Flavor-Plant Protein Interaction: Application Prospects in Plant-Based Alternative Foods. In Food and Bioprocess Technology (Vol. 17, Issue 5, pp. 1141–1182). Springer. https://doi.org/10.1007/s11947-023-03148-4 Shevkani, K., Singh, N., Patil, C., Awasthi, A., & Paul, M. (2022). Antioxidative and antimicrobial properties of pulse proteins and their applications in gluten-free foods and sports nutrition. In International Journal of Food Science and Technology (Vol. 57, Issue 9, pp. 5571–5584). John Wiley and Sons Inc. https://doi.org/10.1111/ijfs.15666 Shukla, V., Carlos-Martínez, A., Li, Y. O., & Davidov-Pardo, G. (2023). Optimization of gluten- free pasta formulation enriched with pulse protein isolates. Journal of Culinary Science & Technology, 21(1), 99–117. Singh, N. (2017). Pulses: an overview. Journal of Food Science and Technology, 54, 853–857. Springmann, M., Clark, M., Mason-D’Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., De Vries, W., Vermeulen, S. J., Herrero, M., & Carlson, K. M. (2018). Options for keeping the food system within environmental limits. Nature, 562(7728), 519–525. White, B. L., Howard, L. R., Uebersax, M. A., & Dolan, K. D. (2022). Processing and quality evaluation of canned dry beans. Dry Beans and Pulses: Production, Processing, and Nutrition, 191–223. Winham, D. M., Davitt, E. D., Heer, M. M., & Shelley, M. C. (2020). Pulse knowledge, attitudes, practices, and cooking experience of Midwestern US university students. Nutrients, 12(11), 3499. Zare, F. (2011). Supplementation of beverage, yogurt and probiotic fermented milk with lentil flour and pea flour and study of their microbial, physical and sensory properties after production and during storage. McGill University (Canada). 6 Chapter 2: Literature review Pulses: classification and importance Varieties of pulses Legumes encompass edible parts such as leaves, stems, pods, and seeds of a broad group of plants within the Fabaceae or Leguminosae family. They are typically categorized into oilseed legumes, such as soybean, (Glycine max (L.) Merr.); groundnut, (Arachis hypogaea L.), and non-oilseed legumes. Non-oilseed legumes can be further divided into vegetable legumes, like green peas (Pisum sativum L.) and green beans (Phaseolus vulgaris L.), which are consumed fresh and often include both the pod and seeds, and pulses which are left on the plant to dry naturally before harvesting solely for their dry, edible seeds. According to the Food and Agriculture Organization (FAO, 1994), the terms “pulses” exclude crops harvested green for food (green peas, green beans, etc.) which are classified as vegetable crops as well as oilseed legumes and leguminous crops e.g. seeds of clover (genus Trifolium L) and alfalfa (Medicago sativa L) that are used specially for sowing purposes (FAO, 1994). The FAO recognizes 11 types of pulses: dry beans (Phaseolus vulgaris L.), dry broad beans (Vicia faba), dry peas (Pisum sativum L.), chickpeas (Cicer arietinum), cowpeas (Vigna unguiculata), pigeon peas (Cajanus cajan), lentils (Lens culinaris), bambara beans (Vigna subterranea), vetches (Vicia sativa), lupins (Lupinus spp.), and pulses nes ("not elsewhere specified," representing minor pulses) (Vigna spp) (FAO, 1994). In the United States, commonly consumed pulses include varieties of dry beans such as pinto, black, and kidney beans, along with dry peas, chickpeas, and lentils (Mitchell et al., 2009). Pulses are essential food crops that hold significant potential for addressing future global food security and environmental challenges while promoting healthy diets (Calles, 2016). Nutritional benefits of pulses Pulses are nutrient-dense foods that offer substantial amounts of protein, dietary fiber, starch (Osorio‐Díaz et al., 2003), and essential minerals and vitamins (Kutoš et al., 2003). Their high protein content (20-25% by weight) is two to three times greater than that of cereals, making pulses a valuable and affordable protein source globally (Calles, 2016; Siddiq & Uebersax, 2022). Legumes enhance protein quality by providing essential amino acids, particularly lysine, which is deficient in cereal-based diets, especially in Asian countries. While cereals are rich in sulfur- containing amino acids, they lack lysine. Combining cereals with legumes creates a 7 complementary amino acid profile, resulting in a high-quality protein source—an especially valuable solution in developing regions worldwide (Ratnayake & Naguleswaran, 2022). A diet rich in beans may reduce the risk of chronic diseases, including conditions like certain cancers, type 2 diabetes, and heart disease—leading causes of death in the United States and globally (Bennink, 2002; Geil & Anderson, 1994). The benefits stem from both their macronutrient and bioactive compound composition. Pulses are particularly rich in water-soluble fiber, which effectively lowers blood cholesterol levels, and insoluble fiber, which aids digestion by increasing bulk and speeding food transit through the digestive tract. They also elicit a low glycemic response due to their high fiber and resistant starch content, which may aid in diabetes prevention and management and potentially lower the risk of colon cancer (Ludwig, 2002; Mathers, 2002; Michels et al., 2006). Additionally, common beans are naturally low in sodium (Augustin & Klein, 1989; Buttriss & Stokes, 2008), making them a suitable food choice for individuals following low- sodium diets. Studies suggest that regular consumption of dry beans could significantly improve dietary quality in the United States, where obesity rates are rising (Mitchell et al., 2009). Regular consumption of dry beans and other legumes has been shown to have positive effects in managing metabolic diseases (Dilis & Trichopoulou, 2009; Flight & Clifton, 2006; Raju & Mehta, 2008). The health benefits of dry beans in disease prevention, including cancer, stem not only from their fiber content but also from the combined effects of their nutritional and non-nutritional components. Anti-nutritional factors in dry beans, such as phytates, saponins, and oligosaccharides, may contribute to cancer prevention by fostering antioxidant activity, inhibiting the growth of cancer cells, and improving gut microbiota (Geil & Anderson, 1994). Polyphenols, for instance, in dry beans, act as antioxidants that help prevent free radical formation, potentially reducing disease risk (Boateng et al., 2008; Oomah et al., 2007). Pulses have been widely studied for their role in improving cardiometabolic health. A study conducted by Mitchell et al. (2009) suggested that increased levels of intake from dry beans and peas may result in higher intakes of fiber, folate, zinc, iron, and magnesium while lowering intakes of total fat and saturated fat in the diets of Americans. Systematic reviews and meta-analyses (SRMAs) of both prospective cohort studies and randomized controlled trials (RCTs) examining the effect of legume intake on cardiometabolic outcomes were reviewed by (Viguiliouk et al., 2017). Due to the absence of SRMAs focused solely on pulses, the review included studies on all legumes (e.g., pulses, soybeans, peanuts). Cohort studies suggested a reduced coronary heart 8 disease risk at intakes of four or more 100 g servings of legumes per week, though associations with cardiovascular, diabetes, and stroke risk were inconclusive. For RCTs specifically targeting pulses (dry beans, peas, lentils, and chickpeas), pulse intakes of 120–132 g/day (~one serving/day) were linked to reductions in cardiometabolic risk factors like HbA1c, LDL cholesterol, and body weight. Additionally, one SRMA reported blood pressure reductions with a higher dose of ~162 g/day (Jayalath et al., 2014). Promoting beans as a cost-effective and nutrient-rich food source could play a critical role in addressing dietary deficiencies, managing obesity, and reducing the prevalence of diet-related diseases, particularly in regions with limited access to high-quality protein sources (Guenther et al., 2006). These findings highlight the importance of incorporating beans into daily diets as a sustainable solution to improve global health outcomes. Environmental benefits of pulses Pulses offer significant ecological benefits and have the potential to contribute to sustainable agriculture and food systems. One key advantage is their ability to fix atmospheric nitrogen through symbiotic relationships with soil bacteria. This reduces the reliance on synthetic nitrogen fertilizers, which can have detrimental environmental effects like water pollution and greenhouse gas emissions (Reckling et al., 2016). Moreover, pulses play a crucial role in enhancing soil health and fertility by improving soil water retention, reducing erosion, and increasing nutrient availability for subsequent crops (Wezel et al., 2014). Additionally, when grown in rotation with other crops, pulses promoted microbial diversity and improved soil health compared to cereal- based systems (Gan et al., 2015). Pulses are a sustainable food choice due to their low carbon footprint, as they have a much lower global warming potential (GWP). The average GWP value for beef is approximately 29 kg carbon dioxide equivalents (CO₂-eq) per kg, for cheese around 9 kg CO₂-eq per kg, and for chicken about 4 kg CO₂-eq per kg. In contrast, dried pulses have an average GWP value of only 0.7 kg CO₂-eq per kg. This means that the GWP per kg of protein for pulses is far lower than that for animal- derived protein sources, making pulses a more climate-friendly option (Clune et al., 2017). Research has also revealed that the carbon footprint of pulses is just one-tenth that of beef per unit of protein produced (Poore & Nemecek, 2018). A study by Röös et al., (2020) demonstrated the potential impact of replacing 50% of the meat consumed in Sweden with domestically grown grain legumes. Despite some agronomic challenges, this transition was projected to reduce the climate 9 impact of the Swedish diet by 20% and land use by 23%. It would also reduce the need for nitrogen fertilizers, lower nitrogen loads from wastewater plants and significantly increase dietary fiber and folate intake in the population. Furthermore, a study by (Springmann et al., 2018) found that replacing 50% of animal-based protein with plant-based sources, including pulses, could reduce greenhouse gas emissions by approximately 30%. Pulses also stand out for their lower water footprint, making them a sustainable crop amid growing concerns over water scarcity. Agriculture accounts for a significant portion of global water use, yet pulses require considerably less water than other protein sources. A global assessment by (Mekonnen & Hoekstra, 2012) reported that the water footprint of pulses is 50% lower than that of chicken and pork and approximately 10 times lower than beef. Overall, the nitrogen-fixing ability, soil health benefits, and lower carbon footprint of pulses make them a valuable component of sustainable agricultural and food systems (Clune et al., 2017; Vasseur et al., 2013). Pulse consumption trends and challenges Low pulse production and consumption Despite these benefits, pulse consumption has stagnated or even declined in several regions, particularly in developing countries in Asia and sub-Saharan Africa, where they offer an affordable, protein-rich food source that is crucial for combating protein-energy malnutrition (Van Heerden & Schönfeldt, 2004). This trend reflects changing consumer preferences and, in some cases, governmental focus on cereal production over pulses to achieve self-sufficiency in staple grains. In Latin America, limited genetic improvements in pulse crops, such as beans, have allowed crops like corn and soybeans to outcompete them for arable land (Evenson, 2004). In regions where declines in pulse consumption were not offset by increased intake of livestock/animal products, overall diet quality has likely diminished, even as caloric intake has risen. In India, for instance, pulses are a primary protein source for the largely vegetarian population (Hopper, 1999). However, approximately 60% of dietary protein in India comes from cereals, which have lower digestibility and protein quality compared to pulses. Surveys conducted by the National Nutrition Monitoring Board (NNMB) over the past 25 years have assessed protein intake across various demographics, including urban, rural, slum-dwelling, and tribal populations. Findings indicate that disadvantaged groups, such as those in slums, tribal communities, and sedentary rural areas, consume about 1 g of protein per kg of body weight daily, mostly derived from cereals (Swaminathan et al., 2012). In 10 the future, pulse consumption in developing countries is expected to remain steady, with per capita annual intake projected to stay around 7–8 kg (Evenson, 2004). In the United States, pulse availability has also decreased, with the USDA Economic Research Service reporting an annual per capita availability of vegetables and pulses averaging 414 pounds from 2017 to 2022—a 4% drop compared to the previous decade (USDA , 2024). In the United States, approximately 2.9 million tons of common beans, peas, chickpeas, and lentils are produced annually, based on a five-year average from 2016 to 2020. Common beans (Phaseolus vulgaris, L.) are the most widely produced pulse crop globally and in the U.S. (FAO, 2024), with around one million tons harvested each year, primarily consumed as canned beans (White et al., 2022). The 2020–2025 Dietary Guidelines for Americans (DGA) recommend a weekly intake of 1.5 cups equivalents of beans, peas, and lentils for individuals on a 2000-calorie healthy U.S.-style or Mediterranean-style dietary pattern, and 3 cups equivalents per week for those on a 2000-calorie vegetarian diet (Haven, 2021a). Despite legumes being an affordable, nutritious, and sustainable protein source, per capita intake in the U.S. remains below recommended dietary guidelines. Lucier et al. (2000) reported that in the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII), only 14% of Americans consumed foods containing cooked dry beans over 2 days. Data from 1999-2002 National Health and Nutrition Examination Survey (NHANES), focused on adults aged 19 and older, showed that only 7.9% of adults consumed dry beans and peas, with an average intake of ~122 g/day (Mitchell et al., 2009). However, subsequent NHANES data from 2003–2014 for adults (≥19 years) showed an increase in the percentage of consumers, with 27% of adults consuming pulses over a 2-day intake period. Despite this increase in prevalence, the average intake declined to 70.9 ± 2.5 g/day, representing a notable reduction from the earlier ~122 g/day levels observed in 1999–2002 (Mitchell et al., 2021). Additionally, in the NHANES 2017–2018 24-hour dietary recall, only 20.5% of 4,741 adults reported consuming any legumes (dry, canned, or frozen) in the previous 24 hours (Semba et al., 2021). The 2025–2030 Dietary Guidelines for Americans recommended higher intakes from legumes or beans of 2.5 cups/week, and lower intakes from red/processed meats and sugar-sweetened beverages (2025 Dietary Guidelines Advisory Committee, 2024). Reasons for low pulse consumption Studies highlight several common barriers to pulse consumption in the U.S., including a general dislike of their sensory profile, lack of familiarity, and insufficient preparation knowledge. These 11 barriers vary across demographics. Older adults over 65 often cite concerns about flatulence, abdominal discomfort, and the limited incorporation of pulses into traditional diets (Doma et al., 2019). Beans contain antinutritional factors such as tannins, lectins, phytic acid, and oligosaccharides which interfere with nutrient metabolism in humans (Francis et al., 1999; Kan et al., 2017). For instance, raffinose-family oligosaccharides are indigestible carbohydrates that ferment in the large intestine, producing gases that cause flatulence (Bohn et al., 2008; Glahn et al., 2002). Phytic acid forms a complex with proteins and decreases protein solubility while lectins adversely affect the activity of digestive enzymes, thereby reducing the in vitro digestibility of proteins (Thompson et al., 1986). Additionally, phenolic acids, tannins, and flavonoids are linked to bitter taste and dark color in pulses (Y. Kumar et al., 2022). Insufficient time and knowledge about cooking pulses further reduce their appeal. In a study on younger populations, specifically Midwestern U.S. university students aged 18–30, barriers included limited time, lack of culinary skills, and unfamiliarity with the health benefits of pulses (Winham et al., 2020). Many students reported on not knowing how to prepare pulses, which contributes to their exclusion from daily diets. Similarly, low-socioeconomic women in Iowa, with an average age of 34.7 years, found dry pulses challenging to prepare, as shown in a study on socio-ecological influences on pulse consumption (Palmer et al., 2018). Across all age groups, the lack of knowledge related to pulse preparation and cooking, combined with aversion to taste and texture, remain significant deterrents. Sensory characteristics strongly influence food acceptability, particularly for plant-based proteins. Pulses differ in sensory characteristics from meat, which may further deter their consumption (Niva et al., 2017). Strategies to increase pulse consumption Although canned pulses are readily available, consumers might prefer incorporating pulses into familiar foods, as a more convenient source of protein (Doma et al., 2019; Winham et al., 2020). Milled pulse flour can address these challenges by offering an easy way to incorporate pulses into traditional food products. Pulse flour can replace or supplement wheat flour in products like bread, muffins, cookies, and pasta, providing convenient and ready-to-eat options for consumers (Shevkani & Singh, 2014; Shukla et al., 2023; R. Simons, 2011; Ziobro et al., 2013, 2016). This approach reduces the need for lengthy cooking processes and addresses barriers such as lack of knowledge and limited inclusion in traditional diets. Most of the anti-nutritional compounds responsible for bitter and astringent tastes in pulses are present in the seed coat and are sensitive 12 to heat and hence can be substantially reduced by milling, cooking, germination, fermentation and heat processing (Y. Kumar et al., 2022). Expanding the range of ready-to-eat processed pulse flour products—particularly plant-based and gluten-free options that are palatable, affordable, and widely available—could significantly boost pulse consumption, especially in Western countries (Niva et al., 2017). Consumption trend of pulse-based/gluten-free products Over the past decade, consumer adoption of plant-forward diets and the utilization of plant-based ingredients have positively influenced the incorporation of pulses in various food products (Chigwedere et al., 2022). These consumer choices are increasingly driven by considerations of human health, animal welfare, and the environmental impact of food value chains. Consequently, the food industry is exploring plant-based ingredients and products to meet these evolving consumer demands. This trend has spurred the development of various pulse-based products such as meat substitutes, snacks, pasta, flours, starches, and dairy alternatives (Davis & Lucier, 2021). Between 2016 and 2020, 1,666 new U.S. food products featured pulse flour, starch, or protein as ingredients, with peas comprising 65% of these formulations (Mintel Group Ltd. 2022; Sadohara et al., 2022). An online survey conducted by (Sadohara et al., 2022) among 75 food industry professionals involved in regular wheat and gluten-free product development revealed that chickpea and pea flours were the most preferred options due to their favorable functional and sensory characteristics. In contrast, common bean flour remains underutilized despite being the most widely produced pulse globally and in the U.S. (FAO, 1994; White et al., 2022). Over half of the industry professionals in the survey were unfamiliar with bean flour as an ingredient, highlighting a potential barrier to its adoption. Moreover, off-flavors associated with pulses continue to hinder their acceptance and limit market expansion (Karolkowski et al., 2021). Future research comparing the sensory properties of chickpea and bean flours could explain chickpea’s popularity as a preferred wheat flour alternative and provide valuable guidance for dry bean flour production and product development. According to a study by (M. B. Magrini et al., 2023; M.-B. Magrini et al., 2018) the market for pulses as ingredients in the French food industry is growing and estimated at 120,000 metric tons per year. A substantial portion of this market (over 80,000 metric tons per year) is attributed to protein-rich peas. In comparison, the market for direct consumption of pulses, including lentils, 13 beans, and chickpeas, is estimated at 100,000 metric tons per year, with more than half of this quantity being imported to France. Of the direct consumption market, over half of the pulses are processed into flour or canned products, while the remaining portion is sold as whole pulses for cooking. As a result, the market for food ingredients produced through advanced processing technologies has outpaced traditional pulse consumption. This shift can be attributed to advancements in nutritional knowledge and the recognition of other functional properties, which have influenced food-processing practices. A comprehensive review by Wijeratne & Nelson (1987) outlined traditional and regional techniques for utilizing legumes, summarizing common preparation methods such as decortication, boiling, grinding, roasting, frying, puffing, germination, fermentation, curdling, and pasta-making. For instance, dry-roasted pulses are widely consumed as snacks in Africa and India, where the roasting process imparts a high-temperature, short-time heat treatment, resulting in a nutty flavor that appeals to both children and adults. In India, pulses are incorporated into snacks like waddai and murukku, where ground legumes and cereal flours are mixed with water to form dough, which is then deep-fried in oil. Similar culinary applications include oil cakes made from dry bean paste in Brazil and ready-to-eat fried snacks produced from ground legume pallets in Nigeria. Another example is filafi, an oil-fried food made from chickpea paste. Among pulses, the common bean (Phaseolus vulgaris, L.) holds global prominence and is widely consumed in the form of canned beans. Pulses and their derivatives—such as whole flours, protein-rich flours, and starch-rich flours—are incorporated into food formulations to enhance nutritional value, fortify products, or partially or fully replace conventional starch- and protein-rich ingredients (Chigwedere et al., 2022). The functional properties of pulses, such as their ability to form emulsions, foams, and gels, make them versatile substitutes for other ingredients, particularly in dairy products. For instance, lupine flour is used in the baking sector as an egg replacement due to its emulsifying properties (KohaJdoVá et al., 2011). Similarly, faba bean flour has been utilized in bread-making to achieve a lighter crumb color (Awulachew, 2024). Studies have demonstrated the positive effects of pulse proteins in gluten-free baking. (Ziobro et al., 2013, 2016) reported that pea and lupine proteins improve bread quality by increasing the batter’s viscosity, which retains more air/gas bubbles during mixing and baking. This results in gluten-free muffins and cakes with enhanced aeration, springiness, and crumb volume. Similarly, Alvarez et al. (2017) created muffins by replacing 50% of wheat flour 14 with chickpea flour and found no significant difference in overall acceptability between the two products. Mancebo et al. (2016) found that incorporating pea proteins into gluten-free cookies increased dough hydration and consistency, resulting in reduced hardness and darker, more desirable cookies. A study by R. Simons, (2011) evaluated gluten-free cookies made with raw, cooked, and germinated pinto bean flours, finding that germinated flours improved sensory quality and increased the bioavailability of nutrients. Pulse proteins have also been shown to improve the quality of pasta, and noodles. In noodles, Sofi et al. (2020) observed that incorporating germinated chickpea proteins enriched rice noodles with antioxidative properties, better cooking performance, and improved dough elasticity. Similarly, Shukla et al. (2023) used pea and faba bean proteins to prepare gluten-free pasta, demonstrating that specific ratios (30:70 and 43:57) produced pasta comparable to those made with semolina. Faba bean proteins enhanced extrudability and water uptake while reducing cooking loss, whereas pea proteins contributed to harder pasta. Despite these advancements, pulse-based snacks and flours face challenges in reducing antinutritional factors and achieving consumer acceptance. The absence of gluten in baked pulse- based snacks often results in crumbly textures, poor color, and other quality defects (Naqash et al., 2017). Studies demonstrate that the overall flavor acceptability of products tends to decrease as the level of pulse-ingredient supplementation increases. This decline has been observed in various products, including bread supplemented with coarse navy bean flour (Borsuk, 2011), muffins containing cowpea flour Jeong et al. (2021), noodles formulated with pea, lentil, or faba bean flour Kaya et al. (2018), crackers with germinated lentil extract Polat et al., (2020), and yogurt fortified with pea and lentil flour (Zare, 2011). Hence, although commercial production and usage of pulse flour has increased, their market presence remains limited due to sensory barriers, particularly the "beany" off-flavors (Karolkowski et al., 2021; Sadohara et al., 2022). Off-flavors in pulses Consumers possess specific sensory memories of preferred flavor experiences; therefore, any deviation in taste, aroma, or overall flavor is often perceived as less acceptable. Off-flavors are defined as unpleasant sensory characteristics, including undesirable taste, aroma, and other sensory effects, that deviate from the expected flavor profile. Since Western diets are predominantly cereal- based, the incorporation of pulses into familiar food products such as breads, muffins and pasta 15 may result in sensory differences that consumers perceive as off-flavors. Interestingly, extruded snacks made with pulse flour received higher liking scores from assessors with lower food neophobia, highlighting the role of individual preferences and familiarity in shaping flavor acceptance (Proserpio et al., 2020). While the popularity of pulses is rising, their full potential as whole foods or ingredients remains underutilized due to sensory challenges, including their inherent tastes, aromas, flavors, and trigeminal sensations. Off-notes in pulses are commonly characterized by sensory descriptors such as beany, green, pea-like, earthy, hay-like, fatty, pungent, and metallic (Roland et al., 2017). In a comprehensive review, Chigwedere et al., (2022) analyzed the frequency of sensory descriptors as a percentage of the total terms from 71 studies on pulse-based products. The findings revealed that beany (43%) and bitter (12%) sensations were the most commonly reported olfactory and basic taste perceptions, respectively. Examples of less acceptable flavors include the beany and grassy notes of pinto bean flour in cookies C. W. Simons & Hall, (2018) and the bitterness of lupin and cowpea in beverages (Nawaz et al., 2022). Flavors with low threshold values are the primary contributors to the perception of off-flavors in foods (Roland et al., 2017). The "beany" flavor is a distinct yet challenging characteristic of dry beans and other legumes, often regarded as an off-flavor in products incorporating pulses as ingredients (Bott & Chambers IV, 2006; Hooper et al., 2019; Kinsella, 1979). The beany or legume-y attribute is a defining characteristic of pulse-based products and can be described as notes of raw or cooked pulses and legumes in general, though it remains difficult to clearly define (Mkanda et al., 2007; Plans et al., 2014; Troszyńska et al., 2006). Pea protein isolate used as an egg replacer in cake formulations was reported to impart a distinct off-flavor characteristic of pea (Hoang, 2012). Similarly, brownies made with 100% dry bean flour, or a mixture of dry bean flour (75%) and wheat flour (25%) were described as having bitter, sour, nutty, bland, and beany flavor notes (English et al., 2019). Research on pulse flavor describes beany characteristics using similarity/resemblance to pulse variety. For instance, aqueous slurries of raw pea flour (10% by weight in water) Price et al. (1985), lupin protein isolate Schlegel et al. (2019) and aqueous slurries of whole faba bean flour (5% by weight in water) Hinchcliffe et al. (1977) demonstrated a distinct “pea-like” flavor. This beany attribute encompasses several sub-character notes and has been widely reported in various pulses. Vara-Ubol et al. (2004) identified sub-notes such as musty/earthy, musty/dusty, sour aromatics, green/pea pod, nutty, and brown in processed chickpea and dry bean products. Similarly, simmered 16 pastes of fermented water-cooked bambara groundnut cotyledons exhibited beany and nutty aromas (Akanni, 2017). Distinct off-notes have also been identified in lupin flour, with GC-O analysis detecting meaty, woody, green, mushroom, and soil-like aromas, depending on processing (Kaczmarska et al., 2018). Interestingly, Xu et al. (2019) reported similar mushroom-like and green descriptors for treated and untreated pea, chickpea, and lentil products, further underscoring the overlapping sensory attributes across different pulses. In addition to these aroma descriptors, certain pulses exhibit astringency. Astringent attributes were identified in aqueous slurries of raw pea flour (Price et al., 1985), fresh lentil sprouts Troszyńska et al., 2011), and water-cooked dry bean cultivars (Koehler et al., 1987). Other studies have also shown that although all basic tastes have been associated with pulses, bitterness is the most frequently reported, especially in boiled pulses, particularly in cultivars such as pea (Malcolmson et al., 2014), dry bean (Bassett et al., 2021; Mkanda et al., 2007) and cowpea (Penicela, 2010). Research has shown that off-odors, off-tastes, and off-flavors in pulses vary widely across cultivars and processing methods, imparting distinct sensory experiences depending on the species and treatment. For example, differences in sensory profiles were observed among cultivars of the same pulse type. Boiled white-colored beans were described as starchy and sweet, while darker-colored cultivars, including dark red kidney, light red kidney, and red mottled genotypes, exhibited more intense vegetative and earthy flavors (Bassett et al., 2021). In low-fat pork bologna formulations, whole chickpea or pea flours were incorporated at equal substitution levels; however, products containing pea flour exhibited stronger off-flavors, whereas those made with chickpea flour were rated higher in flavor desirability (Sanjeewa et al., 2010). The off-flavors in pulses are influenced not only by species but also by market class, cultivar, crop year, growing location, and storage conditions (Malcolmson et al., 2014). This challenge in sensory acceptability highlights the need for further exploration in cultivar selection and innovation in processing techniques to mitigate undesirable flavors while retaining the nutritional benefits of pulses. For industrial applications, pulses with milder flavors and limited flavor variability are often preferred. Pretreatment methods, such as roasting or moist heat application, can significantly alter the flavor and aroma profiles of pulses in final products. For instance, Mcwatters & Heaton, (1979) demonstrated that treating pea flour with moist heat before incorporating it into ground beef patties reduced the beany flavor in the final product. Similarly, Ma et al., (2013) found that roasted lentil flour provided the highest flavor scores when used in salad dressings, compared to 17 dressings supplemented with roasted seeds or pre-cooked spray-dried lentil flour. These findings emphasize the importance of cultivar selection and pretreatment methods in managing and enhancing the sensory properties of pulse-based products. Studies focusing on the sensory profiles of pulse flour are limited and primarily emphasize off- flavors in pulse-based products. To maximize the potential of pulse flours derived from different pulse types and pre-treatments, understanding their sensory profiles is essential. This knowledge can streamline product development by identifying the optimal pulse types with mild flavors and processing treatments that minimize off-flavors, ultimately enhancing consumer acceptability of pulse-based products. Volatile organic compounds in pulses Volatile organic compounds responsible for off-flavors in pulses and their origin Off-flavors in pulses originate from volatile organic compounds (VOCs) (Menis-Henrique et al., 2019). VOCs serve various purposes in plants, including defense mechanisms to combat pathogen invasions and herbivore attacks (Castro et al., 2017; Dicke & Loreto, 2010; Maffei et al., 2007; Mutyambai et al., 2016). In pulse crops, volatile emission naturally occurs in leaves, flowers, seeds, and roots (Fineschi & Loreto, 2012; Loreto & Schnitzler, 2010; Mumm & Hilker, 2006) and can continue even after harvesting (Karolkowski et al., 2021). However, this process is often amplified under stress conditions such as elevated temperatures (Centritto et al., 2011; Fares et al., 2011), water scarcity (Centritto et al., 2011; Holopainen & Gershenzon, 2010), or herbivore and pathogen attacks, serving as a protective mechanism (López et al., 2011). VOCs responsible for off-flavors in pulses often develop due to lipid oxidation, a process that not only generates undesirable flavor compounds but also leads to the degradation of bioactive compounds and fat-soluble vitamins. Lipid oxidation is a complex phenomenon that can occur via enzymatic (lipoxygenase-mediated) or non-enzymatic pathways, including autoxidation and photooxidation, leading to the formation of primary oxidation products like hydroperoxides. These hydroperoxides subsequently decompose into volatile secondary lipid oxidation products such as ketones, alcohols, and aldehydes, which are primarily responsible for off-flavors. Polyunsaturated fatty acids (PUFAs) are particularly susceptible to oxidation, making them a significant contributor to off-flavor development (Saffarionpour, 2024). Even though individual volatile compounds possess distinct odor characteristics, naturalistic aroma perception generally results from a complex mixture of aroma notes produced by multiple 18 molecules (Guichard, 2012). While some volatile compounds are responsible for undesirable odors, others contribute to pleasant and desirable flavors. Understanding the types and concentrations of these compounds is crucial for improving sensory quality and developing innovative pulse-based food products. The presence of specific volatiles at concentrations above their threshold levels—the minimum detectable concentration—can significantly influence the sensory profile, often imparting unpleasant aromas and off-flavors to foods (Saffarionpour, 2024). The aroma threshold, typically measured in parts per billion (ppb) or parts per million (ppm), varies widely among compound types. For instance, the thresholds for saturated, monounsaturated, and diunsaturated aldehydes range from 0.014–1 ppm, 0.04–2.5 ppm, and 0.002–0.6 ppm, respectively. Similarly, the thresholds for alcohols, furans, and ketones range from 0.001–3 ppm, 1–27 ppm, and 0.0002–5.5 ppm, respectively (Shahidi & Abad, 2019). Notably, heterocyclic compounds derived from Maillard reactions, which contain sulfur and nitrogen, exhibit much lower thresholds (≤ 1 µg/L) than lipid-derived volatiles. As a result, higher concentrations of lipid-derived components are required to contribute noticeable aroma characteristics (Kerth & Miller, 2015). Hexanal, 3,5-octadien-2-one, 1-penten-3-ol, and benzaldehyde have been identified as volatile marker compounds in common beans (Buttery et al., 1975). Additionally, hexanol and 2-pentyl furan have been frequently cited in the literature as contributors to the beany aroma and flavor in pulses. These compounds are part of the complex mixture responsible for the beany odor, with earlier studies (Arai et al., 1967; Chiba et al., 1979; Hoffmann, 1962; HSIEH et al., 1982; Z. H. Wang et al., 1997; Wilkens & Lin, 1970). Using sensory analysis and Headspace Solid-Phase Microextraction coupled with Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS– MS), Vara-Ubol et al. (2004) reported that hexanol, 1-octen-3-ol, and 2-pentyl furan were associated with musty and earthy odors, while hexanal was described as having a green pea pod- like aroma. Additionally, 1-octen-3-one, 1-octen-3-ol, pentanol, and acetophenone were also linked to beany notes (Roland et al., 2017; Vara-Ubol et al., 2004). A range of other volatile flavor compounds including n-hexanal, 3-cis-hexenal, 2-pentyl furan, 1-penten-3-one, n-pentanol, n- hexanol, n-heptanol, 1-octen-3-ol, trans,trans-2,4-nonadienal, trans,trans-2,4-decadienal, trans-2- nonenal, trans,cis-2,4-nonadienal, butyric acid, 2-methyl butyric acid methyl ester, 2-pentyl- pyridine, pentanal, and acetophenone have also been attributed to beany and other off-flavors (C. W. Simons & Hall, 2018). These compounds are typically formed through processes such as the Maillard reaction, Strecker degradation, lipid oxidation, ethanol fermentation, degradation of 19 phenolic acids, carotenoids, or thiamine, as well as caramelization or the decomposition of carbohydrates (Bicas & Rodriguez-Amaya, 2021; Sharan et al., 2022). Additionally, pyrolysis of amino acids and peptides contributes to the formation of compounds responsible for grassy, beany, and bitter notes in final food products (Bicas & Rodriguez-Amaya, 2021; Damodaran & Arora, 2013; Leonard et al., 2023; Shahidi & Abad, 2019). Aldehydes. Hexanal (C₆H₁₂O), also known as hexanaldehyde or caproaldehyde, is an alkyl aldehyde commonly associated with off-flavors characterized by "green," "grassy," or "fresh" (Leonard et al., 2023; Roland et al., 2017). It is identified as a principal aldehyde in navy beans, red kidney beans, green lentils, and yellow peas (Ma Zhen et al., 2016). Hexanal is frequently reported in faba beans, soybeans, and peas, where it is formed via Strecker degradation of amino acids or through lipoxygenase (LOX)-catalyzed oxidation of unsaturated fatty acids (Chang et al., 2019; Gao et al., 2020; Sharan et al., 2022; C. Zhang et al., 2020). In raw pulse seeds, linoleic acid undergoes oxidation to hydroperoxides in the presence of oxygen, and hexanal is generated through the cleavage of 13-hydroperoxylinoleic acid by lyases (Belitz, 1999). This process is particularly prominent in seeds that are physically disrupted during processing, resulting in hexanal contributing to the characteristic green and grassy flavor of legumes. Another compound, (E)-2- hexenal, is a medium-chain unsaturated aldehyde (6–12 carbon atoms) and is classified as a fatty aldehyde. It is commonly formed in grain legumes, imparting "grassy" or "green" odors (Sharan et al., 2022; Vaughn & Gardner, 1993; Y. Wang et al., 2020). Additional fatty aldehydes in grain legumes, such as heptanal, octanal, nonanal, (E,E)-2,4-decadienal, and (E,E)-2,4-nonadienal, are typically produced through the degradation of polyunsaturated fatty acids (Leonard et al., 2023; X. Zhang et al., 2020). These aldehydes contribute a variety of off-flavors, including grassy, beany, earthy, fishy, or fatty notes, which are commonly considered undesirable in pulse-based food products. Alcohols. In leguminous grains, the presence of alcohol oxidoreductase plays a key role in the conversion of LOX pathway products, such as aldehydes and ketones, into alcohols (Fischer et al., 2022). The formation of n-hexanol, including 1-hexanol and 3-hexanol, is typically facilitated by the transformation of n-hexanal in the presence of alcohol oxidoreductase (Matoba et al., 1989). Among these, 3-hexanol was identified as the most abundant volatile compound, with the highest relative peak area (RPA) observed in navy beans, red kidney beans, green lentils, and yellow peas (Ma Zhen et al., 2016). Alcohol oxidoreductase activity, reported to range from 0.0031 to 0.0064 20 units/mg protein, has been detected in green beans at a pH of 8.8 (De Lumen et al., 1978). A similar pathway leads to the formation of 1-penten-3-ol: linolenic acid is oxidized to form 16- hydroperoxide, which undergoes enzymatic isomerization to produce 1-penten-3-one. This intermediate is subsequently reduced to 1-penten-3-ol in the presence of alcohol oxidoreductase (Fischer et al., 2022). Alcoholic off-flavors, such as 1-hexanol and 1-pentanol, have been associated with fresh or grassy notes in chickpeas. This is attributed to the activity of three isoenzymes of alcohol dehydrogenase that catalyze their formation (Gomes et al., 1982; Khrisanapant et al., 2019). Other alcoholic off-flavors include 1-octen-3-ol and 1-penten-3-ol, which impart earthy, mushroom-like, or bitter notes, and nonanol, which contributes fatty or floral flavors. The latter is formed from the respective aldehyde, nonanal (Fischer et al., 2022; Khrisanapant et al., 2019; Leonard et al., 2023). Ketones. Ketones are carbonyl compounds formed through LOX activity, resulting from the breakdown of unsaturated fatty acid hydroperoxides. They are also produced via the conversion of aldehydes by alcohol dehydrogenase (ADH) (Fischer et al., 2022). Methyl ketones, such as 2- heptanone and 2-hexanone, can be generated through aldol condensation of aldehydes like hexanal or (Z)-2-butyl-2-octenal, with an aldol intermediate (Leonard et al., 2023). Additionally, they can form conventionally from saturated fatty acids and the decarboxylation of 3-oxo-acids (Grebenteuch et al., 2021). Ketones have been reported in navy beans, red kidney beans, lentils, yellow peas, and black beans, highlighting their widespread presence in pulses (Ma Zhen et al., 2016; Oomah et al., 2007). Major ketones found in leguminous grains include 2-butanone, 2- heptanone, 2-hexanone, 2-nonanone, 1-octen-3-one, and acetophenone. These compounds contribute to a variety of odors, such as fruity, soapy, green, beany, and pungent notes, which can significantly influence the flavor profiles of pulses (Sharan et al., 2022; Trindler, Annika Kopf- Bolanz, et al., 2022). The flavor impact of these ketones varies with their carbon chain length, affecting their potency and sensory characteristics. Aromatic Compounds. In pulses, aromatic compounds and furans, characterized by their cyclic structures, are present in small quantities. These are primarily formed through the oxidation of unsaturated fatty acids in pulse seeds (Oomah et al., 2007). Furans are also commonly produced via the Maillard reaction and the thermal degradation of sugars, amino acids, carotenoids, and polyunsaturated fatty acids (PUFAs) such as linoleic acid (Izzotti & Pulliero, 2014; Min et al., 2003). Specific furans, such as 2-methyl-furan, have been detected in navy beans, red kidney 21 beans, green lentils, and yellow peas (Azarnia et al., 2011). Additionally, lipid-derived aromatic compounds such as o-xylene and p-xylene have been identified in beans, split peas, and lentils (Del Rosario et al., 1984; Lovegren et al., 1979; Oomah et al., 2007). Other aromatic compounds, including 2-ethylfuran and 2-pentylfuran, have been reported in peas, faba beans, and soybeans. These compounds contribute earthy, green, or beany notes, which are characteristic of leguminous grains (Sharan et al., 2022; Trindler, Annika Kopf-Bolanz, et al., 2022; C. Wang et al., 2021). Nitrogenous compounds. During heating processes such as cooking and roasting, nitrogenous compounds including alkylated pyrazines and pyrroles, are typically formed or significantly increased. Their formation is primarily attributed to the Maillard reaction, which involves the condensation of amino-carbonyl compounds to produce dehydropyrazines. These dehydropyrazines lose hydroxyl groups during dehydration, leading to the generation of pyrazines (Yu et al., 2021). Pyrazines are generally formed through the reaction between amino acids and reducing sugars, but they can also result from the dry-thermal degradation of proteins (Kato et al., 1981). However, protein isolates typically do not produce pyrazines under wet heat conditions, such as high-moisture treatments (Stevenson & Chen, 1996). Pyrazines are associated with desirable sensory properties, such as chocolate or roasted nut flavors, and a sharp taste (Azarnia et al., 2011). Alkylated pyrazines have been shown to form or increase markedly during the roasting of soybeans (Kato et al., 1981). Similarly, Ma Zhen et al. (2016) reported significant increases in compounds such as 2-ethyl-5-methyl-pyrazine, 1H-pyrrole-2,3,5-trimethyl-, and pyrimidine-5- methyl-pyrazine in roasted and cooked samples of navy beans, green lentils, and yellow peas. Among alkylated pyrazines, mono-ethyl-mono-methyl-pyrazines exhibit the lowest odor detection threshold, making them potent contributors to aroma (Koehler et al., 1971). The formation of compounds like 2-ethyl-5-methyl-pyrazine, 2-ethyl-6-methyl-pyrazine, 2-methyl-6-propyl- pyrazine, and 2-methyl-5-propyl-pyrazine during roasting can effectively mask the undesirable beany flavor of pulses (Buttery et al., 1971; X. Wang et al., 1998). On the other hand, common pyrazines such as 2-isobutyl-3-methoxypyrazine, 2-isopropyl-3-methoxypyrazine, and 2- methoxy-3-isopropyl-(5 or 6)-methyl pyrazine contribute green, earthy, or bell pepper aromas to pulses and legumes (Trindler, Annika Kopf-Bolanz, et al., 2022; B. Wang et al., 2021; C. Zhang et al., 2020). The low odor detection thresholds (≤ 1 µg/L) of these compounds make them particularly perceptible even at low concentrations (Gao et al., 2020; Lin et al., 2019; Roland et al., 2017; Y. Zhang et al., 2022). 22 Sulfurous compounds. Sulfurous compounds are a significant source of off-flavors in pulses and grains, contributing to unpleasant aromas and tastes (Saffarionpour, 2024). These compounds can occur naturally in foods and are also formed during heat processing and storage (Ma Zhen et al., 2016). Sulfur-containing compounds are highly flavor-active due to their extremely low flavor thresholds (<1 µg/L) and distinctive odors (Gao et al., 2020; Y. Zhang et al., 2022). Dimethyl disulfide and methanethiol are notable sulfur compounds associated with pulses. Methanethiol, characterized by its intense onion-like odor, is the predominant sulfur compound identified in navy beans, red kidney beans, green lentils, and yellow peas. Dimethyl disulfide is believed to form from the decomposition of methanethiol, which is produced through the Strecker degradation of methionine. Methionine undergoes further oxidation to generate methional, leading to the formation of dimethyl disulfide (Leonard et al., 2023; Mishra et al., 2019). The presence of dimethyl disulfide has been reported in both raw green peas and cooked French beans, highlighting its relevance across different processing stages (Azarnia et al., 2011). These sulfur compounds play a crucial role in shaping the sensory profile of pulses, often contributing undesirable notes that challenge their use in food products. The abundance of volatile compounds in pulses is influenced by various factors, including storage conditions, cultivar, growing location, and crop year (Azarnia et al., 2011; N. Singh, 2017), and the processing treatments applied (Ma Zhen et al., 2016). Factors affecting volatile profiles and off-flavors in pulses Effect of cultivar selection The volatile profile of pulses varies significantly due to differences in chemical precursor composition, environmental stress during cultivation, storage conditions, and LOX activity (Akkad et al., 2019). These variations influence flavor development, with specific volatile classes dominating different pulse types. Aldehydes are one of the predominant volatile classes in pulses; however, their concentration varies by pulse type. In general, whole peas and dehulled peas contain fewer aldehydes, whereas pea protein concentrates, isolates, and faba beans exhibit higher aldehyde levels. In contrast, common beans, such as black beans, pinto beans, and dark red kidney beans, contain higher percentages of aromatic hydrocarbons instead of aldehydes (Karolkowski et al., 2021). Ma Zhen et al. (2016) reported that among common beans, untreated navy bean flour had the highest total aldehyde content, while red kidney beans had the lowest. Among pulses, chickpeas 23 contain the highest relative percentage of acetic acid, which imparts a vinegar-like odor, compared to high- and low-tannin faba beans (Akkad et al., 2019; Azarnia et al., 2011). Faba beans tend to have lower concentrations of styrene, cumene, and p-xylene and lack volatile compounds such as 1,2,3-trimethyl benzene and 1,3,5-trimethyl benzene, which are often found in common beans (Oomah et al., 2007, 2011). Geosmin, an oxygenated hydrocarbon responsible for a musty off- flavor, has been detected in dry white navy beans at concentrations above the sensory threshold (Buttery et al., 1975). Alcohol, alkane, and ester content also varies by pulse cultivar. Among common beans, AC Harblack (black beans) and Redhawk (dark red kidney beans) showed alcohol percentages between approximately 3.6% and 5.4%, while other pulses such as pea, chickpea, and faba bean presented significantly higher percentages, ranging from 15.4% to 19.3% (Akkad et al., 2019; Murat et al., 2013; Oomah et al., 2007, 2014; Zhao et al., 2021). High alkane levels have been reported in AC Pintoba and Maverick (pinto beans) and CDC Rio and Onyx (black beans) (Oomah et al., 2007). Regarding terpene content, AC Harblack (black beans) and Redhawk (dark red kidney bean) cultivars have the lowest percentages, whereas CDC Rio, Onyx (black beans), and Maverick (pinto beans) cultivars exhibit similar terpene profiles (Burdock, 2016; Karolkowski et al., 2021; Oomah et al., 2007). Chickpea cultivars also differ in their volatile composition. The Kabuli cultivar demonstrates a higher relative percentage of esters, such as 5-isobutylnonane and 4- dodecanoyloxybutyl dodecanoate, compared to the Desi cultivar (Zhao et al., 2021). Additionally, differences were observed in the percentages of octanal, nonanal, and (Z)-2-decenal, with (E,E)- 2,4-nonadienal absent in the Desi cultivar (Zhao et al., 2021). Faba bean flours prepared from different cultivars showed significant variations in 1-octen-3-ol (mushroom-like) aromas. Thus, understanding the role of pulse genetics is essential for optimizing volatile profiles, which is critical for enhancing sensory quality and promoting the use of pulses in diverse food applications. Impact of harvest year and storage The harvest year significantly influences the volatile profiles of pulses due to variations in environmental factors such as soil composition, temperature, precipitation, and crop management practices. When comparing the volatile profile of pulse samples from consecutive years, distinguishing the effects of harvest year from storage duration before analysis is challenging, and most research 24 primarily focuses on peas. Manouel et al., (2024) reported that the newer 2022 harvest and 2020 harvest sample exhibited the higher concentrations of unsaturated fatty acids, particularly linolenic and linoleic acids, and elevated LOX activity, while the oldest sample from 2018 showed the lowest activity. The higher LOX activity observed in newer pulse harvests, compared to older crops, is likely due to the enzymatic degradation that occurs over time, while differences in fatty acid compositions among pea seeds across harvest years and locations are primarily influenced by varying weather conditions. Trindler, Kopf-Bolanz, et al. (2022) observed that enzymes such as LOX and peroxidase lose their activity during storage. These changes are more pronounced in older harvests stored under suboptimal conditions, as seen in studies on faba beans and chickpeas (Akkad et al., 2021; Noordraven et al., n.d.). Beany volatile compounds such as aldehydes, alcohols, and aromatic compounds were more closely associated with older seeds from 2018 and 2019 harvests. Manouel et al. (2024) found that hexanal concentration in pea flours followed the order 2018 > 2019 > 2020 > 2022, demonstrating that seed age plays a critical role in volatile compound formation. Similarly, the Eclipse and CDC Minuet pea cultivars grown in 2006 had the greatest total RPA of aromatic compounds, sulfur compounds, and ketones, whereas the 2005 crop exhibited the highest RPA of alcohols and aldehydes (Azarnia et al., 2011). This highlights the importance of studying variations in volatile profiles in pulses stored at different periods to better understand the storage period within which pulse flours can be utilized before off-flavors develop, potentially affecting their sensory quality. Moreover, while pea flour has been extensively studied in the context of crop year variations, there is a lack of literature addressing this phenomenon in other pulse types. Storage conditions also critically influence the evolution of VOCs in pulses, significantly affecting their sensory quality and the development of off-flavors over time. According to Gao et al. (2020), compounds responsible for the beany flavor are either inherent or emerge from the degradation of fatty acids during storage and processing. Key factors such as temperature, oxygen exposure, moisture levels, and storage duration determine the extent of lipid oxidation and protein degradation, which drive the production of volatile compounds. Pea flours and protein isolates stored at a moisture content of about 13.5% and 30°C for one year were reported to change from a fresh pea odor to a fishy odor, while samples with moisture levels below 10% did not develop unpleasant odors (A. K. Sumner et al., 1979). 25 Additionally, elevated storage temperatures accelerate lipid oxidation and Maillard reactions, resulting in the formation of aldehydes (e.g., hexanal, nonanal), ketones, sulfur compounds, and furans. Akkad et al. (2021) reported significantly higher concentrations of aldehydes in faba bean flour stored at room temperature compared to refrigerated or frozen conditions. Elevated temperatures and moisture levels intensify the production of unpleasant odor-active molecules, as noted by (Pattee et al., 1982). In contrast, low-temperature storage at 4°C slows lipid oxidation and amino acid degradation, reducing the formation of aldehydes and sulfur compounds in pea seeds stored for 12 months compared to those stored at 22 °C (Azarnia et al., 2011). Other volatile families, such as alcohols, ketones, and furans, are stable at 4°C due to reduced degradation and volatilization rates. High-temperature storage at 22°C and 37°C accelerates lipid oxidation and the breakdown of sulfur precursors, resulting in elevated concentrations of aldehydes and sulfur compounds, while destabilizing other volatile families (Azarnia et al., 2011). In contrast, storage at 4°C slows enzymatic activity, although LOX remain active to some extent, but gradually lose activity over time (Liagre et al., 1996). Freezing unblanched peas at –18°C reduces LOX activity by approximately 80%, maintaining stability for up to a year. Similarly, peroxidase activity decreases continuously, dropping to about 50% over the same period (Gökmen et al., 2005). Despite these reductions in enzymatic activity, freezing temperatures alone may not be sufficient to preserve optimal pea quality. For instance, unblanched peas stored at –10°C for eight months were deemed inedible, and storage at –26°C for 1–2 months did not fully inhibit the development of off-flavors (Bengtsson & Bosund, 1964). Destructive enzymatic processes, including lipid hydrolysis, can persist even at low temperatures (Bengtsson & Bosund, 1966; Mattick & Lee, 1961). Storage at –30°C appears to significantly suppress volatile formation, offering improved preservation of sensory quality (Bengtsson & Bosund, 1964). To achieve long-term reduction of off-odors, a heat pre-treatment prior to storage at –20°C is essential. While raw peas are prone to developing off-odors during storage, blanched or cooked peas are better preserved, retaining an acceptable odor profile (Rhee & Watts, 1966). Oxygen exposure during storage promotes the formation of sulfur-containing volatiles and ketones, which are associated with rancid and cabbage-like off-notes. Anaerobic storage conditions can mitigate these changes, preserving sensory quality (Noordraven et al., n.d.). Hence, it is important to study how storage conditions impact the volatile profiles of pulses to identify optimal methods for preserving sensory quality. While research on cooked pea products 26 and faba beans is extensive, more studies are needed on other pulse flours. Exploring low- temperature, humidity-controlled storage and anaerobic packing solutions could significantly reduce lipid oxidation and off-flavor generation in pulses. Processing strategies to mitigate off-flavors in pulses Germination and fermentation Traditionally, germination and fermentation have been key methods for enhancing the nutritional and sensory properties of pulses by generating aroma compounds and sugars. Germination, commonly known as sprouting, has gained popularity in health foods due to its ability to improve both taste and nutritional value. The germinated grains can be consumed as sprouts or further processed through drying or roasting. Sprouting has long been used to reduce antinutritional factors such as trypsin inhibitors and phytic acid in pulses. Additionally, it breaks down raffinose family oligosaccharides (ROFs) into shorter carbohydrates (Bourré et al., 2019) and reduces phytates, while also mitigating unpleasant beany off-flavors through the degradation of lipids and LOX activity influenced by factors such as light (Eum et al., 2020; Nam et al., 2005) and temperature (V. Kumar et al., 2006) during germination (Cabej, 2019; Roland et al., 2017; P. Singh et al., 2022). Vidal-Valverde et al. (1994) investigated the effects of soaking, cooking, and germination on antinutritional factors in lentils (Lens culinaris var. vulgaris) using distilled water, citric acid, and sodium bicarbonate solutions. They found that soaking reduced phytic acid content without affecting trypsin inhibitor activity, while also increasing tannins and catechins. Subsequent germination and cooking significantly decreased trypsin inhibitor activity and phytic acid levels while further elevating tannin and catechin contents, potentially influencing bitterness and astringency in lentils. Similarly, Fernández et al. (1996) observed increased tannins and catechins in faba beans after soaking in similar solutions, suggesting these compounds may impact the sensory profiles of legumes. Germination improves protein solubility and water-holding capacity by generating free amino acids (Setia & others, 2019). Kaczmarska et al. (2018) demonstrated its positive influence on the flavor profile of soybean seeds by increasing methoxypyrazine content and sweet notes through elevated levels of 2,3-butanedione, guaiacol, and (E, Z)-2,6-nonadienal. Similarly, germination of faba bean flour effectively reduced bitter compounds and beany flavors, such as hexanal, nonanal, 2-heptanone, and 2-pentyl furan (Akkad et al., 2021). Studies on pea varieties by Xu et al. (2019) confirmed that germination reduced off-flavors such as hexanal, (E, E)-2,4-nonadienal/decadienal, 3-methyl-1-butanol, 1-hexanol, and 2-pentylfuran in chickpeas. 27 Rajhi et al. (2022) found that germination in different faba bean cultivars increased aldehydes, decreased phenols and esters, and formed new flavor compounds such as ketones and alkenes. Using gas chromatography/mass spectrometry-olfactometry (GC-MS/O) Xu et al. (2019), demonstrated that flour from yellow peas and lentils exhibited similar aroma attributes before germination but differed significantly after germination. Flour from germinated seeds showed more pronounced meaty, sweet, and sulfur-like aromas compared to non-germinated lupin seeds (Kaczmarska et al., 2018). Fermentation is commonly carried out using solid-state or submerged methods, often in the presence of fungi or bacteria that produce protease enzymes (Çabuk et al., 2018; Rahate et al., 2021). The production of protease enzymes partially degrades proteins, enhances digestibility, and inhibits the activity of digestive enzyme inhibitors like trypsin and chymotrypsin (Çabuk et al., 2018; Rahate et al., 2021). Microbial fermentation has been extensively employed to reduce beany flavor components in pulses through two main approaches. The first approach utilizes microbial pathways to degrade beany flavor compounds or their precursors, reducing these compounds below their odor thresholds (Zhu & Damodaran, 2018). The second approach focuses on generating new aromatic compounds during fermentation, which not only mask the original beany flavors but also modify the overall aromatic profile. For instance, Pei et al. (2022) utilized Lactobacillus rhamnosus L08 to ferment pea flour, resulting in a significant reduction of unpleasant odorants such as nonanal, decanal, octanal, 1-hexanol, and 2-ethyl-1-hexanol. The fermentation process also increased the diversity of acids and esters while enhancing the amino acid content, emulsion stability, and foam stability of the fermented pea flour. Similarly, El Youssef et al. (2020) observed a significant reduction in the leguminous and green sensory properties of pea protein when co-cultured with lactic acid bacteria (VEGE 047 LYO) and yeast strains (Kluyveromyces lactis Clib 196, Kluyveromyces marxianus 3810, Torulaspora delbrueckii TD 291). This co-culturing also introduced new sensory characteristics. (Sun et al., 2022) demonstrated that fermenting soybeans with Naematelia aurantialba increased the levels of aldehydes such as pentanal and benzene acetaldehyde, which introduced fruity and sweet aromas that masked the beany and grassy notes associated with hexanal. Additionally, the fermentation process elevated the concentration of 1-octen-3-ol, contributing to a distinct mushroom-like aroma. In agreement with these findings, (Yang et al., 2021) reported that yogurt made from fermented peas reduced beany and grassy off-flavors caused by 1-hexanol and (E)-2-hexenal, while 28 generating sweet, cheesy, and buttery aromas from volatile compounds like acetoin and 2- pentanone. Overall, these studies highlight that germination and fermentation represent promising strategies to enhance the sensory and nutritional attributes of pulses by mitigating off-flavors, reducing antinutritional factors, and introducing desirable flavor compounds, thereby broadening their appeal and versatility in various food applications. Thermal treatment Heat treatments are widely used to modify the sensory and structural properties of pulses, offering an economical and efficient method to mitigate off-flavors. Elevated temperatures facilitate lipid and amino acid degradation reactions, leading to the formation of odor-active molecules that influence flavor profiles (Murat et al., 2013). Common thermal processes, including boiling, roasting, blanching, UHT treatment, and spray drying, induce structural changes in pulse proteins. These changes involve partial denaturation, reducing α-helix structures while increasing β-sheets, β-turns, and random coils, which can enhance protein interaction with flavors (Tang et al., 2019). However, excessive heating can lead to protein aggregation or further denaturation, which may decrease flavor-binding capacity by increasing the content of β-sheets in the protein structure. (K. Wang & Arntfield, 2015) demonstrated that extended heating increased the interaction of aldehydes, such as hexanal, heptanal, and octanal, with canola protein isolate, while reducing ketones like 2-octanone due to protein aggregation. Similarly, hexanal was observed to bind irreversibly to pea protein isolate, likely due to the availability of hydrophobic binding sites, thereby enhancing flavor retention. Heat treatments also impact enzyme activity in pulses. While both wet and dry heat treatments influence volatile composition, they differ in their mechanisms and resulting flavor profiles. Wet heat treatments, such as boiling and autoclaving, primarily function by the development of stable lipo-protein complexes, and leaching of volatiles, whereas dry heat treatments, like roasting inactive enzymes and promote Maillard reactions that generate pyrazines, leading to more complex aroma development. Wet heating Wet heating methods, such as cooking pulses in water (boiling), autoclaving, and steaming are widely utilized to reduce off-flavors in pulses by protein denaturation and inactivating key enzymes like LOX and peroxidase, which contribute to fatty acid oxidation and the formation of undesirable aromas. Ma Zhen et al. (2016) examined the effects of wet heating on the volatile 29 flavor profiles of various pulses, including navy beans, red kidney beans, green lentils, and yellow peas. The study found that cooked samples produced through soaking and boiling exhibited a reduction in total volatile concentration compared to untreated samples. This reduction was attributed to protein denaturation, which led to the formation of lipoprotein complexes and altered the intensity of flavor compounds. Studies indicate that blanching at 90–100°C for 60 seconds deactivates LOX and reduces peroxidase activity to below 2% (Gökmen et al., 2005; RHEE & WATTS, 1966). Further research by (Gökmen et al., 2005) indicated that blanching at 80°C for 2 minutes eliminated LOX activity while reducing peroxidase activity to below 10%. However, Williams et al. (1986) observed that certain LOX and peroxidase enzymes exhibited high thermal stability, particularly in whole peas compared to homogenized ones, emphasizing the importance of optimizing blanching conditions. Wet heating techniques such as blanching and steaming have also been particularly effective in mitigating off-flavors in peas and navy beans (Bourré et al., 2019). Despite the reduction in LOX activity and overall volatile concentrations, wet heating often increases sulfurous compounds, contributing to undesirable sensory attributes. Compounds such as dimethyl sulfide and dimethyl trisulfide can impart metallic, cabbage, egg, and onion-like off- flavors, which are highly disliked by consumers (Chigwedere et al., 2022; Vurro et al., 2024). Mishra et al. (2017) reported that autoclaved red kidney beans developed an aroma characterized as earthy and beany. This was attributed to the increased presence of methanethiol, methional, diethyl sulfide, dimethyl disulfide, and dimethyl trisulfide, which were identified as key contributors to the “cooked kidney bean” aroma. Overall, wet heating methods effectively inactivate LOX and reduce total volatiles, but they may also increase sulfurous volatiles, requiring further optimization to minimize their impact on sensory quality. Dry Heating While boiling effectively reduces total volatile concentrations and mitigates off-flavors, dry heating methods, particularly roasting, offer additional advantages. Roasting is often favored as a convenient pre-treatment method prior to milling pulses and incorporating them into various plant- based, gluten-free products. The effectiveness of roasting depends significantly on the method and technology employed. For instance, Revtech roasters provide advantages such as low energy consumption, uniform heating, and minimal maintenance (Revtech, 2015). Infrared (IR) roasting 30 further enhances efficiency by preventing overheating and oxidation, reducing energy costs, and improving product quality (Rahimi et al., 2018). For instance, Shariati‐Ievari et al. (2016) examined the impact of infrared heat treatment on LOX activity and volatile compounds in green lentils during micronization. The study revealed a significant reduction in LOX activity at 130°C, with further decreases observed at 150°C compared to untreated flour. This reduction was accompanied by lower levels of hexanal and 2-hexenal. Burgers made with heat-treated lentil flour exhibited good overall flavor and acceptability, whereas those made with untreated flour retained a pronounced beany off-flavor (Der, 2010). Similarly, Navicha et al. (2018) found that soybeans roasted at 110–120°C for extended durations exhibited a marked reduction in beany flavors due to LOX inactivation. Bi et al. (2021) also highlighted the effectiveness of roasting in transforming the odorants of raw adzuki beans from "green" and "grassy" to "roasted" and "nutty," further solidifying its suitability as a processing technique for enhancing flavor properties. Roasting not only reduces undesirable flavors but also enhances the concentration of volatile compounds, particularly pyrazines, which mask beany flavors and contribute to a more appealing flavor profile (Kato et al., 1981). However, it can also increase sulfur compounds that intensify beany off- flavored notes. Ma Zhen et al. (2016) observed that roasting navy beans, red kidney beans, green lentils, and yellow peas significantly increased total volatile concentrations compared to non- roasted samples. This increase was characterized by a reduction in alcohols and the emergence of pyrazines, which add to the flavor complexity of pulse flours. However, sulfur compounds increased, including methanethiol, which has an objectionable odor reminiscent of decomposing cabbage or garlic, particularly in navy beans, red kidney beans, and yellow peas. Roasting has been identified as a potential method to mitigate beany off-flavors in lupin seeds (Lupinus albus cv. Multolupa). Yáñez et al. (1986) examined the effects of roasting lupin seeds at temperatures of 80–90°C for varying durations. While longer roasting times (20–40 minutes) significantly reduced protein quality, a shorter roasting time of 10 minutes was recommended for off-flavor reduction. However, sensory evaluations were not conducted to confirm this finding. Roasting has been shown to reduce aldehydes, alcohols, and ketones while increasing the concentrations of pyrazines and furanoids would in soybeans roasted at temperatures between 140 and 230°C as demonstrated by (Cai et al., 2021). Similarly, Frohlich et al. (2021) found that roasting navy beans, yellow peas, and faba beans significantly diminished beany and bitter flavors in pita bread made with pulse flours. Additionally, Young et al. (2020) reported that bread made with roasted pea flour exhibited 31 less intense pulse aromas and off-flavors compared to bread made with untreated peas. In summary, careful control of roasting conditions is necessary to minimize the formation of sulfurous notes and preserve overall product quality. In conclusion, processing methods like germination, fermentation, wet heating, and dry heating significantly influence the sensory and nutritional profiles of pulses. While these methods reduce off-flavors and enhance desirable attributes, challenges such as the formation of sulfur-like notes persist. Future research should focus on optimizing processing parameters to balance flavor improvement with the minimization of undesirable compounds, thereby expanding the utility of pulses in diverse food applications. Methods of off-flavor analysis Traditionally, the profiling of taste and aroma attributes has been conducted using sensory evaluation (Ashurst, 1999; Lopetcharat & McDaniel, 2005). Sensory analysis still remains one of the most frequently used methods and is considered the benchmark for food quality evaluation. However, this method has several limitations (Ashurst, 1999; Shurmer & Gardner, 1992) including high cost and time required, and lack of direct information on causal molecules. Furthermore, because sensory analysis is time-consuming and costly, it is unsuitable for real-time or online monitoring. To address these limitations, analytical techniques have emerged as more efficient methods for evaluating the VOCs responsible for flavor in pulses and other food products. Chromatographic techniques, particularly GC-MS, are widely utilized for identifying and characterizing volatile compounds in food due to the high separation power of the GC system, which is complemented by the high sensitivity and identification capability of MS. The combination of gas chromatography and olfactometry (GC-O), introduced by Fuller et al. (1964) marked a significant breakthrough in aroma research by enabling the identification of specific odor-active compounds. In addition to GC-O, instruments based on electronic sensors, such as electronic noses (e-noses) and electronic tongues (e-tongues), are increasingly used as alternatives. These techniques not only complement traditional sensory methods but also provide practical solutions for high-throughput and real-time flavor evaluation in food research and development. Traditional VOC analysis methods GC is the most used method for characterization and quantification of individual VOCs within complex blends, particularly in studies involving pulses (Jansen et al., 2011). During VOC analysis it accurately samples reactive compounds that are difficult to detect directly, while maintaining 32 sensitivity even at low concentrations (Dudareva et al., 2006; Materić et al., 2015; Qualley & Dudareva, 2009; Tholl et al., 2021). In GC-MS, an inert carrier gas, typically helium, acts as the mobile phase, facilitating VOC transport through a column containing a stationary phase made of a polymer-coated solid support. The properties of the column, including its length, diameter, and stationary phase composition, are crucial in determining the separation efficiency of volatiles (X. Liu et al., 2012). VOCs are separated based on their retention times as they elute from the column and are subsequently identified and quantified using a mass spectrometer or another detector (Materić et al., 2015). A fundamental step in volatile analysis is the extraction of volatiles from the sample, which can be achieved through chemical extraction or headspace collection. Chemical extraction methods utilize solvents to isolate volatiles, whereas headspace techniques collect volatile compounds directly from the gas phase above the sample. Solvent-Assisted Flavor Evaporation (SAFE) is a commonly used chemical extraction technique that combines vacuum distillation and solvent extraction, enabling the isolation of volatile compounds with minimal thermal degradation (Engel et al., 1999). While SAFE allows for quantification using an internal standard, it requires an extended extraction time and the use of organic solvents, which may introduce additional complexity (Murat et al., 2012). Previous studies have used SAFE in combination with GC-MS to assess volatile profiles in dehulled pea flour (Murat et al., 2013). Headspace sampling methods are generally categorized into two main types: static and dynamic. Static sampling uses Solid Phase Microextraction (SPME), which has been widely utilized due to its simplicity, rapidity, and effectiveness in preventing impurities from continuous air streams. SPME is particularly well-suited for detecting low-abundance VOCs without requiring solvents, as the molecules are concentrated on the SPME fiber. SPME allows for the rapid and efficient collection of VOCs, achieving detection limits in the parts-per-billion by volume (ppbv) range. SPME is particularly valued for its portability and its ability to integrate collection, concentration, and VOC introduction into a single stage, significantly reducing preparation time while enhancing sensitivity compared to other methods (Papet et al., 2010; Rering et al., 2020; Vangoethem, 2017; Vas & Vekey, 2004; Z. Zhang & Li, 2010). SPME involves the adsorption and subsequent thermal desorption of volatile compounds from an inert fiber coated with adsorbents of varying polarity and thickness, tailored to the specific type and concentration of the targeted compounds (Tholl et al., 2021). These adsorbent phases include diverse polymers like polydimethylsiloxane (PDMS), 33 polyacrylate (PA), or polyethylene glycol (commonly referred to as CW or carbowax), as well as porous polymers such as divinylbenzene (DVB) or carboxen (CAR) (Jansen et al., 2011). PDMS, DVB, and CAR are among the most commonly used materials for extracting volatiles from pulses (Murat et al., 2012). On the other hand, dynamic headspace sampling (DHS) allows for a more exhaustive extraction of VOCs compared to static techniques. In DHS, an actively pumped air stream entrains VOCs and directs them toward a trap via a packed cartridge, enabling a greater capture of volatiles (Stierlin, 2020). During this process, volatiles adsorb onto a polymer within a closed chamber featuring continuous air circulation. The trapped volatiles can then be eluted from the adsorbent matrix using solvent extraction or thermal desorption for subsequent GC analysis (Tholl et al., 2006). A significant advancement in aroma research was the introduction of gas chromatography- olfactometry (GC-O) by Fuller et al. (1964), a technique that combines the resolution power of capillary GC with the selectivity and sensitivity of the human nose (Plutowska & Wardencki, 2008, 2012). GC-O employs the human nose as a detection device, operating in parallel with standard chromatographic detectors such as flame ionization detectors (FID) or mass spectrometers. This technique enables the rapid identification of odorant zones in a chromatogram. During analysis, a trained evaluator or panel detects the aromatic impressions of the eluate from the column and correlates these impressions to retention times. For instance, Xu et al. (2019) utilized HS-SPME- GC-MS/O to characterize changes in the volatile components of germinated chickpea, lentil, and yellow pea flours over six days of germination. The study revealed that lentil and yellow pea flours exhibited similar aromatic profiles, while chickpea flours showed a decrease in beany flavor compounds alongside the emergence of unpleasant flavors. Six beany flavor markers—hexanal, (E, E)-2,4-nonadienal, (E, E)-2,4-decadienal, 3-methyl-1-butanol, 1-hexanol, and 2-pentyl- furan—were identified and used to quantify beany flavor formation during germination. This highlights the utility of GC-O in identifying key odorants and monitoring flavor changes during food processing. Novel VOC sensing methods While traditional methods for VOC detection offer numerous advantages and a broad range of applications, they are often associated with drawbacks such as high cost, bulky equipment, and the need for specialized expertise and training. As a result, there is a growing demand for inexpensive, 34 portable, and user-friendly alternatives. One promising approach involves the use of gas-sensing technologies to detect VOCs (Fang & Ramasamy, 2015; Liu et al., 2012; Tisch & Haick, 2010). Increasingly, novel equipment based on electronic sensors is being utilized. Instruments such as the electronic nose (e-nose) and electronic tongue (e-tongue) have gained traction for their ability to perform rapid and objective analyses, making them invaluable for flavor profiling in food products (Ciosek et al., 2004, 2006; Deisingh et al., 2004; Rodríguez Méndez et al., 2010). The e- nose is designed for the analysis of volatile compounds in the gaseous phase without separating the individual components, while the e-tongue focuses on medium- and low-volatility compounds in the liquid phase, complementing the capabilities of the e-nose (Leake, 2006). Both devices utilize arrays of non-selective gas or liquid sensors paired with a pattern recognition system, enabling the identification of both simple and complex taste and aroma profiles (Rodríguez Méndez et al., 2010). The e-nose is particularly noteworthy for its speed, ease of operation, and non-invasive nature, making it a practical alternative to sensory analysis (Mielle, 1996). The sensors, under the influence of an odor stimulus, generate a unique "fingerprint" that can be classified and identified using a database and trained pattern recognition systems (Martı́ et al., 2005; Shurmer & Gardner, 1992). Recent advancements have introduced complementary technologies, including e-nose systems based on mass spectrometry or fast gas chromatography (Wilson & Baietto, 2009). Depending on their operational principles, e-nose sensors can be categorized into three groups: conductivity sensors, gravimetric sensors, and optical sensors (Plutowska & Wardencki, 2012). Conductivity sensors operate based on changes in conductivity or resistance when exposed to gases. They often use materials such as conducting polymers (CP) or metal oxide semiconductors (MOS). Gravimetric sensors detect mass changes in the piezoelectric sensor coating caused by gas absorption, leading to alterations in resonant frequency when exposed to VOCs. Optical sensors rely on changes in chemical properties, such as reactivity, redox potential, and acid-base interactions. They incorporate a wavelength-selectable light source, a light detector, and sensor materials that interact with gases. Techniques such as colorimetry and fluorometry are commonly used to analyze signals from optical sensors. The analysis of signals from artificial nose and tongue systems involves signal processing and pattern recognition, often through comparison with a standard. Preliminary analysis focuses on smoothing sensor signals, averaging responses, and minimizing carryover effects from previous measurements (Ortega et al., 2000). The raw sensor 35 responses, often noisy, are refined during feature extraction, which reduces dimensionality and enhances data usability. Feature extraction methods can be divided into quantitative techniques, which construct databases of known samples, and pattern analysis methods like principal component analysis, as well as discriminant function analysis and canonical correlation analysis (Ortega et al., 2000; Pearce et al., 2003; Röck et al., 2008). Signal analysis techniques for e-nose data fall into three categories: graphical, multi-variable, and network analysis. Among these, partial least squares regression (PLS) stands out as a robust method in chemometrics, combining features of multiple linear regression and PCA to handle collinear data and reduce noise. PLS has been widely applied to estimate sensory panel indicators from e-nose data, demonstrating its utility in reducing calibration efforts while maintaining accuracy (Fujioka, 2021; Geladi & Kowalski, 1986; Lozano et al., 2007). While PLS is not inherently unique to e-nose applications—a key advantage of e-nose over HS-SPME-GC-MS lies in its ability to rapidly classify and correlate sensor activation patterns with sensory attributes, offering a time-efficient approach for flavor profiling and sensory evaluation. Comparison of traditional and novel methods Cai et al. (2021) compared the utility of e-nose and HS-SPME-GC-MS techniques in a study investigating the effects of roasting levels on the physicochemical, sensory, and volatile profiles of soybeans. They utilized a commercial PEN3 e-nose equipped with 10 semiconductor metal oxide chemical sensors designed to detect specific volatile substances in the headspace gas of roasted and unroasted soybean flours. This rapid screening approach identified overall volatile profile differences and provided holistic aroma insights. Additionally, HS-SPME-GC-MS analysis was performed using a DVB/CAR/PDMS fiber needle for VOC extraction, followed by GC-MS analysis. This method enabled precise identification and quantification of 41 volatile compounds, including 2,5-dimethylpyrazine, the most abundant compound detected. While GC-MS offers high sensitivity and selectivity for complex mixtures, it is time-intensive due to sample preparation, separation, and data processing. Conversely, e-nose provided rapid, high-throughput screening with simpler operation and lower costs but lacked selectivity due to overlapping sensor responses and reliance on pattern recognition. Similarly, (Asikin et al., 2018) compared the ripening stages of dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa) using HS-SPME-GC-MS and an MS-based E-nose. HS-SPME-GC-MS identified key VOCs, including 3-methylbutanal, acetaldehyde, and sulfurous compounds like 1,2,4-trithiolane, which increased significantly during 36 ripening. These results provided detailed chemical profiles and insights into the aroma changes. In contrast, the E-nose used discriminant ion masses (e.g., m/z 41, 43, 58, 78, and 124) to produce overall aroma profiles and rapidly differentiate ripening stages through principal component analysis (PCA). The GC-MS approach highlighted specific marker compounds, while e-nose demonstrated its strength in rapid screening and quality control by analyzing overall aroma profiles. Traditional methods such as GC-MS and GC-O enable simultaneous qualitative and quantitative evaluation of individual aromas after chromatographic separation (Plutowska & Wardencki, 2008). They can determine whether a compound exceeds the sensory detection threshold, its odor characteristics, sensory activity duration, and odor intensity (Van Ruth, 2001). Despite its advantages, results may be unreliable due to its focus on individual compounds rather than the overall sensory experience. This limitation underscores the need for instruments like e-nose, which combines high sensitivity and correlation with human sensory panel data. E-noses offer several advantages, including mobility, short analysis times, lower costs, and ease of use, making them suitable for industrial applications far from well-equipped laboratories and specialized expertise. They enable rapid, high-throughput analysis with high sensitivity, effectively detecting complex odor mixtures without requiring separation. However, their accuracy may be limited by sensor aging, sensitivity to moisture, and partial specificity. In contrast, GC-MS and GC-O excel in providing compound-specific data and detailed insights into the relevance of individual volatiles to aroma (Wardencki et al., 2013). 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Food Hydrocolloids, 32(2), 213–220. 55 Chapter 3: Effect of crop year, processing, and cultivar on volatile composition in pulses and pulse flours analyzed by headspace-solid phase microextraction gas chromatography- mass spectrometry Abstract America faces a challenge of low pulse consumption due to barriers such as their lengthy cooking times, lack of knowledge on preparation, and aversion to their taste or texture. Incorporating pulse flours into convenience products presents a promising approach to increasing consumption; however, off-flavors hinder their widespread adoption in food formulations. Understanding the factors influencing volatile organic compounds (VOCs) responsible for off-flavor formation is critical for improving the sensory quality of pulse-based products. This study aimed to investigate and quantify the effects of cultivar, harvest year, and processing (roasting and boiling) on the volatile composition of eight pulse cultivars using targeted headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). Pulse samples were produced by boiling whole or milling into flour, with a subset roasted before milling. The resulting flours were also cooked into model products (porridge: roasted and non-roasted) to assess volatile changes due to roasting and cooking. Results showed significant differences in total estimated volatile concentration across processing treatments and harvest years. Processing significantly reduced aldehydes and alcohols while increasing nitrogenous and sulfurous compounds. Boiling resulted in the lowest in total volatile concentration (1.09e-08 mol/L), whereas the non-roasted product exhibited the highest concentration (3.51E-07 mol/L), followed by the roasted product (1.06E-07 mol/L), milled roasted flour (1.03E-07 mol/L), and milled non-roasted flour (5.33E-08 mol/L). Hierarchical clustering and principal component analysis revealed separation of samples by harvest year and distinct volatile profiles across cultivars, suggesting that environmental and post- harvest conditions influence volatile composition over time. These findings highlight the influence of cultivar selection, harvest year, and trade-offs due to processing on pulse volatile profiles, providing insights that can mitigate off-flavor formation and support the development of more widely accepted pulse-based products. 56 Introduction Pulses are dry, edible seeds of leguminous crops, including dry beans, peas, chickpeas, and lentils. Their incorporation in food products is driven by increasing consumer demand for plant-based diets, influenced by health and environmental concerns (Chigwedere et al., 2022). Pulses and pulse flours are rich in protein (17%–30%) and complex carbohydrates (60%–67%), including both soluble and insoluble fiber, and provide a valuable source of essential vitamins and minerals (J. Boye et al., 2010; Campos-Vega et al., 2010; Tosh & Yada, 2010; Vaz Patto et al., 2015; N. Wang & Daun, 2004). In addition to their nutritional benefits, pulses contribute to sustainable agriculture by improving soil health through nitrogen fixation and phosphorus release (Nulik et al., 2013). Their genetic diversity also supports climate change adaptation, allowing for the selection and breeding of varieties suited to diverse environmental conditions (Russel, 2015). Milling pulses into flour has facilitated their incorporation into cereal-based products, enhancing nutritional value and fortifying food formulations (Chigwedere et al., 2022). While the popularity of pulses is rising, their full potential as whole foods or ingredients remains underutilized due to sensory challenges such as off-flavors, described as “beany” and “grassy” in pinto bean flour- based cookies (C. W. Simons & Hall, 2018) or “bitter” in lupin and cowpea-based beverages (Nawaz et al., 2022), limiting wider consumer acceptance. Consequently, as the food industry continues to explore plant-based ingredients, improving the sensory properties of pulses is essential to meet consumer expectations. Volatile organic compounds, particularly those with low odor thresholds, are key contributors to off-flavors in foods (Roland et al., 2017). Volatile compounds in legumes primarily originate from three sources. The first and most significant source is the oxidation of free fatty acids (FFAs). Lipases hydrolyze lipids into FFAs like oleic, linoleic, and linolenic acids (Dundas et al., 1978), which are then oxidized by lipoxygenase (LOX) or through auto-oxidation in the presence of heat, light, or metal ions (Frankel, 1980). This process generates hydroperoxides, which degrade into various volatile compounds, including aldehydes, alcohols, ketones, and furans (Clemente et al., 2000; Karolkowski et al., 2021). The second source is the degradation of free amino acids (AAs) into aldehydes, alcohols, acids, and pyrazines (Jakobsen et al., 1998; Spinnler, 2011). This can occur via plant metabolism, microbial degradation (Ehrlich-Neubauer pathway), or Maillard reactions at different temperatures (Bader et al., 2009; Rizzi, 1990). The third source is the breakdown of carotenoids, leading to the formation of terpenes (Maccarrone et al., 1994). 57 The abundance and composition of volatile compounds in pulses are influenced by multiple factors, including cultivar, growing location, crop year, storage conditions, and processing treatments (Azarnia et al., 2011; Ma et al., 2016; N. Singh, 2017). Environmental conditions such as soil composition, temperature, and precipitation impact volatiles across different harvest years. For example, pea cultivars grown in 2006 exhibited the highest total relative peak areas (RPA) of aromatic compounds, sulfur compounds, and ketones, whereas the 2005 crop had the highest RPA of alcohols and aldehydes (Azarnia et al., 2011). Similarly, storage conditions significantly impact volatile formation, with exposure to heat, light, and oxygen accelerating the production of undesirable volatile compounds (Azarnia et al., 2011). Additionally, volatile profiles vary among legume cultivars based on the predominant chemical classes. Rajhi et al. (2021) reported that fenugreek was characterized by high levels of apocarotenes and nitrogen/sulfur derivatives, while faba beans, lentils, and chickpeas exhibited the highest concentrations of non-terpene derivatives, including aldehydes, alcohols, ketones, phenols, and hydrocarbons. In contrast, dry bean cultivars such as black and red beans contained elevated levels of oxygenated monoterpenes and phenylpropanoids. Characterization of the volatile profiles of dry beans could be very important in selecting and marketing the right cultivars for targeted food applications. Several processing techniques have been explored to improve the flavor of pulses by mitigating the formation of off-flavor compounds. Processing techniques such as soaking, blanching, steam heating, and dry heating have been widely studied in peas and chickpeas to reduce off-flavor compounds by inactivating LOX and other enzymes (Roland et al., 2017). Additional studies on processing methods, including roasting, boiling, spray drying, and freeze drying, demonstrated that new flavor compounds, such as pyrazines and alkylated pyrazines, develop during roasting and cooking, potentially masking the beany flavors associated with aldehydes, alcohols, and sulfur compounds (Ma Zhen et al., 2016). Although processing plays a key role in shaping flavor development of pulse flour, the changes in volatile compounds that occur during the final cooking step remain insufficiently explored, particularly in dry beans. Headspace-solid phase microextraction (HS-SPME) is one of the most commonly applied techniques to extract volatile compounds. This technique can be used in combination with Gas Chromatography-Mass Spectrometry (GC-MS) where volatiles in the vapor phase are adsorbed onto a fused-silica fiber and then desorbed into the GC injector, where they are separated and identified using MS (Makhlouf et al., 2024). GC-MS has been applied in studies on various pulses, 58 including faba beans (Akkad et al., 2019, 2021; Oomah et al., 2014), peas (Azarnia et al., 2011), chickpeas (Zhao et al., 2021), and common beans such as black beans, pinto beans, and dark red kidney beans (Oomah et al., 2007) due to its powerful separation abilities and robust identification capabilities. Therefore, the specific objectives of this study were to: (1) compare the volatile profile of eight selected pulse cultivars, (2) evaluate the effect of processing (roasting, and boiling), and (3) crop year on volatile compounds in the pulses using HS-SPME-GC-MS. Materials and Methods Germplasm selection and seed production To assess the impact of cultivar and crop year on volatile profiles, seven bean cultivars grown during two different years (2022 and 2023) and one chickpea cultivar obtained from the market were studied. The Kabuli chickpea (Sierra) cultivar grown in 2022, obtained commercially, was chosen because of its importance in commercial production in the western U.S., while the other seven bean varieties chosen for their adaptation to Michigan dry bean agricultural conditions, and favorable agronomic characteristics and competitive seed yields. The eight pulse varieties chosen for this study are listed in Table 3.1 Table 3.1: Market class, abbreviations, and genotypes of the eight pulse varieties included in this study, with genotypes grown during the 2022 and 2023 crop years. Market Class Abbreviation Genotypes grown Navy bean Otebo bean Great Northern bean White Kidney bean Mayacoba bean Manteca bean Cranberry bean Chickpea N O GN WK MY MN CR CHKP in 2022 Alpena Samurai Powderhorn WK 1601-1 Y 1802-9-1 Y 1608-7 CR 1801-2-2 Sierra Genotypes grown in 2023 Alpena Samurai Powderhorn WK 1601-1 Y 1802-11-2 Y 1608-14 CR 2111-1 - The seven dry bean varieties were grown at the Michigan State University Montcalm Research Center in Entrican, Michigan in 2022 and 2023. The seeds were planted in a randomized complete block design with three field replications on June 10, 2022, and June 14, 2023, respectively. The plot consisted of 4 rows that were each 6.1 m long, with the center two rows containing the experimental lines and the outer two rows a standard kidney bean border. Recommended field 59 maintenance practice was followed for weed and insect control and fertilization. Supplemental overhead irrigation was provided when needed. On September 29, 2022, and on October 11, 2023, respectively, the seeds were directly harvested with a Hege 140 plot combine harvester. Seed samples were cleaned by hand to remove gravel and damaged or foreign seeds, and cleaned samples were stored in paper bags at room temperature (22°C) until volatile analysis. The light seed coat color of specific market classes such as white colored beans- Navy, Otebo, Great Northern, and White kidney were selected due to their potential for easier adoption as flour. Sample preparation Five types of samples were prepared for HS-SPME-GC-MS analysis from each of the eight pulses, namely, non-roasted pulse flour (NRF), roasted pulse flour (RF), non-roasted pulse flour porridge (NRP), roasted pulse flour porridge (RP), and boiled pulses (BP). The dry pulses were cleaned by rinsing under distilled water and then laid out on a sheet tray lined with a paper towel. A portion of the sample for each pulse type was subjected to dry heat roasting in an oven (Fisher Scientific Isotemp Gravity Oven, 100 L) at 110°C for 70 minutes, followed by a 4-hour cooling period. Both the non-roasted and roasted pulses were milled into flour using a hammer mill (Kinematica PX-MFC 90 D, Bohemia, NY) with a 0.5 mm sieve to obtain non- roasted pulse flour (NRF) and roasted pulse flour (RF), respectively. The NRF and RF samples were stored in resealable polyethylene plastic bags under refrigeration at 2°C to minimize volatile loss (Akkad et al., 2022). For pulses harvested in September 2022, milling into flour occurred in March 2023 (6 months post-harvest), and GC-MS analysis was conducted in April 2024 (18 months post-harvest). Similarly, for pulses harvested in October 2023, milling into flour occurred in April 2024 (6 months post-harvest), with GC-MS analysis performed in September 2024 (12 months post-harvest). The NRF and RF samples were transferred from refrigeration to room temperature (22 °C) thirty minutes prior to porridge preparation and analysis by HS-SPME-GC-MS. To investigate the effects of cooking on volatile compounds of pulse flours, model products in the form of porridges were prepared from both NRF and RF samples using a standardized procedure. Porridges were selected as the model system due to their simplicity, as water is the only added ingredient. For porridge preparation, 50 g of NRF or RF flour was mixed with 250 mL and stirred for 7 minutes on an MSE PRO LCD 4-Channel Digital Magnetic Hotplate Stirrer to ensure uniform dispersion of the flour in the water and to prevent the formation of lumps. Subsequently, 300 mL 60 of distilled water was added, and the mixture was cooked at an average temperature of 150°C and 1500 rpm for approximately 25 minutes. This resulted in two types of well-mixed porridges: non- roasted porridge (NRP) and roasted porridge (RP). In addition to the porridge samples, cleaned pulses were cooked by boiling to create boiled pulse (BP) samples. Pulses were soaked in distilled water for 12 hours at room temperature using a 1:3 seed-to-water ratio. After draining the soaking water, pulses were boiled in distilled water, maintaining the same seed-to-water ratio (1:3), using a Duxtop 1800W Portable Induction Cooktop Countertop Burner. Cooking times were determined using a Mattson pin drop cooker and varied by cultivar: Otebo (16 min), Navy (24 min), Great Northern (23 min), White Kidney (30 min), Chickpea (45 min), Manteca (20 min), Mayacoba (33 min), and Cranberry (50 min). 61 Figure 3.1: Flowchart of sample preparation methods for five types of samples, namely NRF, RF, NRP, RP, and BP. Each sample type was prepared from each of the 7 beans, namely Navy, Otebo, Cranberry, Manteca, Mayacoba, White Kidney, and Great Northern grown in the years 2022 and 2023, as well as Chickpea (market sample grown in 2022 acquired commercially), for GC-MS analysis. Solid phase microextraction (SPME) NRP, RP, and BP samples were prepared fresh on the day of analysis, immediately transferred to 20mL glass headspace vials to minimize volatile loss, and analyzed within 8 hours of preparation. The following sample quantities for each of the eight pulses were individually placed into vials: 2g of NRF, 2g of RF, 5g of mashed BP, 5g of NRP (mixed with 1g NaCl), and 5g of RP (mixed with 1g NaCl). Adding salt to the sample matrix during solid phase micro-extraction (SPME) induces a "salting-out" effect, which lowers the partitioning coefficient (K) for some analytes and increases their concentration in the headspace, thereby enhancing extraction efficiency for polar compounds and organic volatiles (Westland, 2021). For increased volatilization of compounds, the samples were first held at 50 °C for 30 min in a water bath. Following this period, the SPME fiber 62 (carboxen/polydimethylsiloxane/divinylbenzene (CAR/PDMS/DVB) 2 cm, 30/50 µm, Supelco, Sigma–Aldrich) was introduced into the headspace of each sample and exposed for an additional 30 minutes at 50 °C in the same water bath. Gas chromatography-mass spectrometry (GC-MS) analysis A gas chromatograph and mass spectrometer were used to separate and detect the headspace aroma compounds and to collect detection frequency data on separated aroma compounds. Absorbed volatiles were desorbed for 20 seconds from the fiber coating by inserting the SPME fiber through a predrilled septum (Thermogreen LB-2, Supelco Co., Bellefonte, PA) and into a glass-lined, split/spitless injector port (200 °C) of a gas chromatograph (Agilent 6890 Gas Chromatograph, Hewlett-Packard Co., Wilmington, DE). Volatiles were separated on a 30 m × 0.25 mm i.d. capillary column (HP-5, Hewlett-Packard) having a film thickness of 0.25 µm. Ultra-purified helium (99.999%) was used as carrier gas at a ramped flow with an initial flow rate of 1.2 mL/min held for 1 minute and then increased at a rate of 1 mL/min to a final flow rate of 1.8 mL/min. The initial linear velocity was 44 cm/s. The initial temperature of the GC oven was 32°C; it was held for 0.25 min, increased to 60°C at a rate of 20 °C/min, and again increased to 150 °C at a rate of 50 °C/min, and finally increased to 280 °C at a rate of 70 °C/min, and held for 2 min. The total analysis time was 7.4 min. Volatile detection was done using Time of Flight Mass Spectrometry (TOFMS) with an electron ionization source (LECO Pegasus III Mass Spec, Leco Corp, St. Joseph, MI). For detection with mass spectrometry, the ion source was held at 200 °C with electron energy at 70 eV and a scan range of 29–400 mass units; the scan rate was 20 spectra per second with an acquisition voltage of 1500 to 1600V. Preliminary identification of volatiles was performed by comparison of their mass spectra with those of authenticated chemical standards. During the experiment, each volatile compound of interest was identified either by the National Institute of Standards and Technology (NIST) database (V.05) through a mass spectra library search or by comparing retention times (RT) and the mass spectra of the compounds with those of the pure commercial standards (as listed in the following section). The identified volatile compounds were classified into eight chemical classes: aldehydes, alkanes, alcohols, ketones, terpenoids, sulfurous compounds, nitrogenous compounds, and aromatic compounds. Volatile compound identification was performed using two levels of annotation. Level 1 identification involved comparing the retention times of volatile compounds with those of 63 authentic chemical standards. Level 2 identification involved putative annotation of metabolites based on spectral similarity to public or commercial spectral libraries without the use of chemical reference standards (L. W. Sumner et al., 2007). The metabolites identified by level 2 annotation were quantified by calculating the peak areas of each volatile based on the average area under the curve (AUC) from triplicate measurements and reported for a single m/z (mass-to-charge ratio) using the unique mass. The quantification of volatiles identified by level 1 annotation was achieved by estimating the volatile concentration of each compound in a sample using the area ratio method. The AUC of volatiles in a sample was compared to the peak areas of a 25-component external standard mixture prepared at 0.2 𝜇𝐿 in 4.4 L. For a volatile compound with known density ρ in g/mL and molar mass M in g/mol, the molar concentration of the standard 𝐶𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 in mol/L was calculated as: 𝐶𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 = ρ × 0.2μL M × 4.4L × 25 The estimated concentration of the volatile in the sample 𝐶𝑠𝑎𝑚𝑝𝑙𝑒 in mol/L was determined by the area ratio of the sample to the standard as follows: 𝐴𝑈𝐶𝑠𝑎𝑚𝑝𝑙𝑒 𝐴𝑈𝐶 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 The final estimated concentration of a volatile compound was calculated by taking the average of ) × 𝐶𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐶𝑠𝑎𝑚𝑝𝑙𝑒 = ( the triplicate estimated volatile concentrations in a sample. Standards Authenticated pure commercial standards of 2-butanone, 2-methyl butanal, butanol, 2-ethylfuran, 3-methylbutanol, dimethyl disulfide, 1-pentanol, hexanal, (E)-2-hexenal, 1-hexanol, o-xylene, 2- heptanone, styrene, heptanal, methional, 2,5-dimethyl pyrazine, benzaldehyde, 1-octen-3-ol, 6- methyl-5-hepten-2-one, octanal, decane, L-limonene, nonanal, decanal, and geosmin purchased from Sigma-Aldrich (St. Louis, MO, United States) were combined in equal volume aliquots to create a twenty five-component mixture. Every week, 0.2 𝜇𝐿 of the mixture was injected on a glass microfiber filter and placed in a glass volumetric flask of 4.4 L fitted with a specially made ground glass stopper containing a gastight Mininert valve (Alltech Associates, Inc., Deerfield, IL). The flask was held at 22 °C until the liquid standards were fully volatilized (Song, et al., 2009). 64 Statistical Analysis The GC-MS AUC and estimated volatile concentration data were analyzed using R statistical computing software (version 4.2.2; R Core Team, 2022) to assess sample differences. Analysis of Variance (ANOVA) was conducted using the agricolae v. 1.3.5 (de Mendiburu, 2021) package, followed by Least Significant Difference (LSD) post-hoc multiple comparisons tests (α = 0.05). For multivariate analysis, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted using the FactoMineR v. 2.8 (Lê et al., 2008) package. Visualization of PCA results was carried out using ggplot2 v. 3.5.1 (Wickham, 2016).Heatmaps were generated using the pheatmap v. 1.0.12 (Kolde, 2018) package. A 3-factor ANOVA was used to evaluate the effects of cultivar, processing, and year on the total estimated volatile concentrations of all pulse samples from harvest years 2022 and 2023. The model included two-way interactions (cultivar:year, cultivar:processing, and year:processing) and a three-way interaction (cultivar:year:processing), enabling the evaluation of how these factors and their interplay influenced volatile profiles. For all statistical tests, an α of 0.05 was used to determine statistical significance. To further evaluate the impact of processing treatments on volatile concentrations across key chemical classes, a separate ANOVA model was applied. The volatiles were grouped by their chemical class for each pulse type from its respective harvest year, and the total estimated volatile concentration per class was calculated by summing all volatiles for each of the chemical classes. One-way ANOVA was conducted with processing as the independent variable, followed by an LSD post hoc test to identify pairwise differences. To investigate broader patterns in volatile content across two growing seasons from 2022 and 2023, estimated volatile concentration data were mean-centered and normalized prior to PCA analysis. HCA analysis was also applied to cluster samples into subgroups with shared volatile profiles. For heatmap visualizations, the mean-centered AUC of volatiles identified through level 1 and level 2 annotation was log-transformed to emphasize differences in volatile compound profiles across cultivars in NRF and NRP samples from the 2022 harvest year. 65 Results and Discussion A total of 32 volatile compounds were identified across the pulse samples, 25 of which were annotated as level 1 and quantified using authentic chemical standards and visualized in Figures 2, 3, and 5. (E)-2-Hexenal and 1-hexanol were the most abundant volatiles. The identified volatiles included alcohols (5), aldehydes (8), ketones (3), aromatics (4), terpenoids (1), alkanes (1), nitrogenous compounds (1), and sulfurous compounds (2) (Table 3.3). Consistent with previous studies, targeted GC-MS identified alcohols (18.2%), aldehydes (51.7%), and ketones (21.6%) as the most abundant chemical classes as an average across all samples (Khrisanapant et al., 2022; Mishra et al., 2017; Oomah et al., 2007). Volatiles significantly varied across samples, mainly driven by the effects of processing (p=2.2E-17) and harvest year (p=3.3E-11) (Table 3.2). Table 3.2: Summary of analysis of variance (ANOVA) results in a 3-way ANOVA evaluating the effects of cultivar, year, processing, and their interactions on the total volatile concentrations from HS-SPME-GC-MS analysis. Df = degrees of freedom, SS = sum of squares, MS = mean sum of squares. Interaction cultivar year processing cultivar:year cultivar:processing year:processing cultivar:year:processing Residuals Df 7 1 4 7 28 4 28 160 SS 1.2E-13 5.2E-13 1.1E-12 1.7E-13 7.6E-13 6.0E-13 8.7E-13 1.6E-12 MS 1.7E-14 5.2E-13 2.8E-13 2.4E-14 2.7E-14 1.5E-13 3.1E-14 1.0E-14 F-value 1.7 50.8 27.5 2.4 2.6 14.6 3.0 p-value 0.113 3.3E-11 2.2E-17 2.5E-02 8.1E-05 3.4E-10 7.0E-06 Effect of processing on volatile profiles Figure 3.2 and Figure 3.3 illustrate how the thermal treatment of pulses affects the distribution of volatile compounds. The ANOVA results indicated that processing significantly affected total volatile concentration (p = 2.2E-17) (Table 3.2). 66 Figure 3.2: Total estimated volatile concentration of pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN) grown in Michigan during the harvest years 2022 and 2023, and a market sample of Chickpea (CHKP) obtained commercially, harvested in 2022. Samples marked 2022 and 2022a were analyzed in April 2024; samples marked 2023 and 2022b were analyzed from August through September 2024. Volatile concentrations are presented for five processing treatments: non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP), and boiled pulses (BP). Results represent the average values from triplicate measurements. Mean values for each pulse type that do not share a letter are significantly different (p < 0.05) as determined by the LSD post hoc comparison test. 67 Figure 3.3: Effect of processing (roasting; boiling) on the estimated volatile concentration of (A) alcohols; (B): aldehyde; (C): ketone; (D): nitrogenous compound; (E): sulfurous compound of eight cultivars grown in 2022: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), 68 Figure 3.3 (cont’d) Manteca (MN), Mayacoba (MY), White Kidney (WK), and Great Northern (GN) in non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP) and boiled pulses (BP). Results are the average value from triplicates. For each type of pulse, mean values that do not share a letter are significantly different (p < 0.05) as per the LSD post hoc comparison test. Effect of roasting Roasted flour Alcohols and ketones were the most abundant volatiles in roasted flour, accounting for an average of 37% and 30% of the total estimated volatile content, respectively. Generally, ketones and alcohols increased slightly, and pyrazines increased substantially in RF compared to NRF, depending on cultivar type (Figure 3.3A, 3C). Consequently, the total estimated volatile concentration also increased significantly (p<0.05) after roasting in RF samples of (CHKP, CR) and white-colored (O) pulses compared to NRF (Figure 3.2). Interestingly, this increase could be attributed to the formation of a new group of volatile compounds, characterized by a significantly higher concentration of pyrazines due to roasting, in RF samples (Figure 3.3D). These compounds were absent from NRF samples, indicating that pyrazines are primarily formed during roasting. Roasting significantly increased (p<0.05) the concentration of nitrogenous compounds in RF samples of yellow-colored (MN, MY), white- colored (O, WK) and other (CHKP, CR), pulse cultivars, compared to NRF (Figure 3.3D). A five- fold increase in nitrogenous compounds was observed in CHKP, MY, and O cultivars (Table 3.3). Nitrogenous compounds, particularly pyrazines, often impart chocolate, roasted nutty, and sharp flavors to pulses (Azarnia et al., 2011). Particularly, 2,5-dimethyl pyrazine, characterized by a nutty, roasted, musty, and grassy aroma (The Good Scents Company) was the most abundant in RF samples of yellow-colored (MN, MY), white-colored (O, WK) and other pulses (CHKP, CR) (Table 3.3). Additionally, roasting significantly increased (p<0.05) total ketones in RF samples of all pulses except MY and CHKP compared to their NRF counterparts (Figure 3.3C). The identified ketones included 2-butanone, 2-heptanone, and 6-methyl-5-hepten-2-one. 2-butanone (camphoreous, acetone, fruity, ethereal) odor was the most abundant ketone in RF samples across all cultivars except MY, CHKP (Table 3.3). Additionally, 2-heptanone, known for its fruity, spicy, cinnamon, 69 green, and banana-like odor (Burdock, 2016) also increased after roasting in the RF samples of all cultivars compared to NRF (Table 3.3). Furthermore, roasting also led to a slight but significant increase (p < 0.05) in total alcohol content across all cultivars except GN (Figure 3.3A). The alcohols identified in the pulse samples were 1- pentanol, 1-hexanol, 1-octen-3-ol, 1-butanol, and 3-methylbutanol. Among these, roasting increased the concentration of 1-butanol and 3-methyl butanol in RF samples compared to NRF across all cultivars, with the highest concentration of 1-butanol in Otebo and 3-methyl butanol in Great Northern beans (Table 3.3). Although, in our study, no significant differences in aldehyde concentration were found between NRF and RF samples for any of the pulse cultivars (Figure 3.3B), previously, Ma Zhen et al. (2016) reported higher aldehyde concentrations in roasted navy and red kidney bean flours and Lee et al. (2023) reported elevated levels of hexanal and benzaldehyde in roasted soybeans compared to their raw counterparts. Our results align with Akkad et al. (2023), who reported a higher relative abundance of pyrazines and ketones in heat-treated faba bean flour crackers than in untreated flour. Similarly, Ma Zhen et al. (2016) found higher alcohol concentrations in roasted green lentils and yellow peas flour than in untreated flour. These findings indicate that roasting fundamentally alters the volatile profile of pulses by inducing several chemical reactions between sugars, proteins, and minerals, alongside the breakdown of hydroxyl amino acids and the degradation of pigments. As a result, roasting leads to the formation of various volatile compounds, including sulfur compounds, pyrazines, pyridines, pyrroles, oxazoles, aldehydes, ketones, phenols, and carbon dioxide (Bhattacharya, 2014). Ketones in legumes primarily form through the oxidation of saturated fatty acids at high temperatures and decarboxylation of 3-oxo-acids (Grebenteuch et al., 2021). Lee et al. (2023) further reported that 2-heptanone, absent in untreated soybeans, appears after roasting. This suggests lipid oxidation (Oomah et al., 2014), and Maillard reactions contribute to its formation. Additionally, alcohol dehydrogenase activity can convert lipoxygenase pathway products, transforming aldehydes or ketones into alcohols (Fischer et al., 2022). The Maillard reaction, a non-enzymatic browning process driven by amino acids and reducing sugars, is a key driver of pyrazine formation during heat treatment (Yu et al., 2020). A GC-O study by Bi et al. (2020) found that roasted pea flour contained high levels of pyrazines such as 2-ethyl- 3,5-dimethylpyrazine and 2,6-dimethylpyrazine, which contributed to nutty and caramel-like 70 aromas. In contrast, raw pea flour was dominated by 3-methylbutanoic acid and hexanal, which impart fatty, green, and grassy notes. Similarly, Kato et al. (1981) observed that D-methyl- and 2- ethyl-5-methyl-pyrazines increased in roasted soybeans, masking beany flavors produced from aldehydes and alcohols. This suggests that roasting alters the volatile profile of pulses by generating pyrazines with roasted and nutty aromas that may help mask the grassy, green, and beany notes produced from alcohols and aldehydes. Overall, roasting significantly increased ketone and alcohol concentrations due to lipid oxidation while also driving pyrazine formation through Maillard reactions in roasted pulse flours. Roasted model product In the roasted products (RP) made from RF, alcohols and aldehydes were significantly lower (p<0.05) while, sulfur concentrations were significantly (p<0.05) higher in white-colored (GN, N, O, WK) beans compared to NRP (Figure 3.3E). Additionally, the total volatile concentration of targeted compounds reduced significantly (p<0.05) by 70-80% in the RP samples of white-colored beans (GN, N, O, WK) compared to their NRP counterparts (Figure 3.2). This decrease may also be attributed to the targeted GC-MS approach used in this study, which primarily quantified alcohols and aldehydes, leading to an overall reduction in the total estimated volatile concentration. Roasting and subsequent cooking into the model product significantly (p < 0.05) reduced the total alcohol concentration by an average of 57% in RP samples of white-colored (GN, N, O, WK) and by 62% in yellow-colored (MN, MY) beans compared to their NRP counterparts (Figure 3.3A). The high standard deviations observed in total volatile concentrations of MN could be attributed to instrumental variation between different days of GC-MS runs and potential inconsistencies in porridge preparation. Aliphatic alcohols such as 1-hexanol, 1-octen-3-ol, and 1-pentanol were markedly reduced, while 1-butanol and 3-methyl butanol were almost absent in RP samples (Table 3.3). Several of these alcohols have been identified as key contributors to beany flavors: 3-methyl- 1-butanol, which imparts an alcohol-like odor (Gao et al., 2020); 1-pentanol and 1-octen-3-ol, associated with grassy, beany, and mushroom-like odors (Xu et al., 2019). Additionally, 1-hexanol contributes grassy, green, or leafy odors (Bott & Chambers IV, 2006; Vara-Ubol et al., 2004; Xu et al., 2019). Thus, reducing alcohol content through roasting may help mitigate common off- flavors in pulses. The aldehydes detected in pulse samples included 2-methyl butanal, hexanal, (E)-2-hexenal, heptanal, benzaldehyde, octanal, nonanal, and decanal. Among these, (E)-2-hexenal, which 71 contributes to grassy, green, and herbal flavors characteristic of the beany flavor in pulses, was the most abundant volatile and exhibited the highest concentration in the NRP samples of GN, N, and O cultivars (Oomah et al., 2014; Park et al., 2011; Sharan et al., 2022; Y. Wang et al., 2020) (Table 3.3). Roasting specifically decreased the concentration of hexanal (grassy, green, and herbal aroma), (E)-2-hexenal (green, grassy aroma), benzaldehyde (roasted, hazelnut, and almond odors), 2-methyl butanal (pungent, fresh, fruity aroma), decanal (bitter gourd), heptanal (fatty, herbal, green odor) and nonanal (fatty, citrus, green aroma) in RP samples compared to their NRP counterparts (Table 3.3) (Burdock, 2016; The Good Scents Company, 2021; Viana & English, 2021). Aldehydes have been previously reported to produce green off-flavors in peas and beany off-flavors in soybeans (Roland et al., 2017; Sessa & Rackis, 1977). Akkad et al. (2021) identified aldehydes (nonanal, octanal, hexanal, decanal, and 3-methyl butanal) as key contributors to beany flavors in faba bean flours. Hexanal has also been identified as a source of off-flavors in peas; the more hexanal was present, the less the peas were liked (Bengtsson & Bosund, 1966). Thus, roasting may be a valuable pre-treatment method as it significantly reduced (p < 0.05) total aldehyde concentrations by an average of 83% in white-colored (GN, N, O, and WK) beans, particularly for those volatile compounds typically perceived as off-flavors (Figure 3.3B). These observed reductions in alcohol and aldehydes may result from the inactivation of alcohol oxidoreductase and lipoxygenases during roasting (Akkad et al., 2023; De Lumen et al., 1978). On the other hand, roasting significantly increased (p<0.05) the concentration of sulfurous compounds such as dimethyl disulfide in RP samples of white-colored (GN, WK, N, O) and yellow-colored (MN, MY) beans compared to their NRP counterparts (Figure 3.3E). Additionally, methional exhibited the highest concentration in RP samples of WK, N, and MY cultivars (Table 3.3). Our targeted GC-MS approach identified a fifty-fold increase in the total sulfur concentration due to roasting and cooking in RP compared to NRP (Table 3.3). Previous research has demonstrated that thermal processing leads to sulfur compound formation. Mishra et al. (2017) detected dimethyl sulfide, diethyl sulfide, methanethiol, dimethyl disulfide, and dimethyl sulfone in kidney beans exclusively after cooking (Chin & Lindsay, 1994). Similarly, Bi et al. (2020) identified dimethyl sulfide, which imparts cabbage, sulfur, and sickly odors, as unique to roasted pea flour. These sulfurous compounds primarily arise from the degradation of methionine and cysteine amino acids during roasting and cooking. Methionine undergoes Strecker degradation during the final stages of the Maillard reaction, converting into methional, which has a low odor 72 detection threshold (0.2 μg/L) and contributes to sulfurous and beany aromas in cooked kidney beans (Mishra et al., 2019). Further oxidation of methional produces methanethiol, which subsequently forms dimethyl disulfide and dimethyl trisulfide (Chin & Lindsay, 1994). Our results align with previous studies that have reported reductions in aldehydes, alcohols, and terpenes after cooking, alongside increases in sulfur-containing compounds and pyrazines (Mishra et al., 2017). Similarly, Shariati-Ievari, (2013) demonstrated that burgers made with non- micronized chickpea/lentil flours were characterized by higher concentrations of ‘beany’ alcohols and aldehydes such as hexanol, 2-hexenal, heptanal, hexanal, octanal, and nonanal compared to micronized flour at 130 ̊C. These shifts indicate that roasting alters the volatile composition by increasing ketones, alcohols, and pyrazines in flours, but their subsequent cooking further modifies these profiles by decreasing alcohol and aldehydes but increasing sulfurous compounds, resulting in a net decrease in total volatile content for roasted and cooked samples. 73 Table 3.3: Estimated concentration in mol/L of volatiles quantified using authentic chemical standards across non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP), and boiled pulses (BP) from the pulse cultivars (Cranberry, Great Northern, Navy, Otebo, White Kidney, Manteca and Mayacoba) grown in harvest year 2022 from Michigan and a market sample of Chickpea obtained commercially (harvested in 2022). These samples were analyzed in April 2024. Values represent the average of triplicate measurements grouped by chemical class. nd: not detected. Odor descriptions reflect the top three odor notes as reported by The Good Scents Company (2009). White colored beans (estimated volatile concentration in mol/L) White kidney bean NRP Great northern bean NRP NRF RF RP BP RF RP BP Odor Description NRF nd 1.2E-09 1.1E-08 1.7E-07 1.7E-09 5.3E-10 malty, musty, fermented 1.6E-09 3.9E-09 1.0E-09 2.6E-09 8.9E-10 4.8E-09 6.2E-09 3.4E-10 3.2E-10 5.0E-09 7.4E-09 9.1E-09 4.7E-10 1.2E-10 vegetable, aldehydic, clean 1.5E-09 2.2E-09 3.0E-07 6.0E-08 3.2E-09 3.2E-09 3.9E-09 2.2E-08 3.1E-08 1.4E-10 3.2E-10 2.0E-10 3.9E-11 4.2E-10 9.8E-10 4.2E-10 3.9E-11 nd 1.8E-10 5.0E-10 1.5E-09 5.8E-10 5.8E-10 5.2E-10 9.1E-10 6.8E-10 4.0E-10 2.1E-10 1.7E-10 2.6E-10 6.6E-11 2.0E-11 3.9E-10 7.8E-10 6.1E-11 2.6E-11 7.4E-12 1.1E-09 1.1E-09 3.7E-10 1.4E-10 1.6E-10 2.1E-09 3.1E-09 5.9E-10 1.6E-10 7.9E-11 8.1E-11 9.7E-11 5.0E-11 2.1E-11 2.9E-11 2.4E-10 3.3E-10 6.4E-11 1.9E-11 sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral nd nd nd nd nd 8.4E-10 7.0E-10 5.4E-09 1.4E-09 1.2E-08 5.4E-09 3.4E-09 3.1E-10 8.2E-10 7.2E-09 8.7E-10 6.0E-10 6.0E-10 musty, vegetable, cocoa sweet, fermented, yeasty 2.1E-09 1.8E-09 7.0E-10 9.5E-11 1.1E-09 1.4E-09 3.4E-10 1.9E-11 4.1E-11 9.5E-10 5.4E-10 3.1E-07 1.1E-07 3.8E-10 4.4E-10 3.5E-10 1.7E-09 2.3E-09 1.7E-09 sweet, pungent, herbal 1.3E-10 1.6E-10 2.0E-09 5.7E-10 5.7E-11 1.1E-10 8.3E-11 4.0E-10 1.1E-09 1.7E-10 vegetable, mushroom, chicken 1.1E-09 1.0E-09 5.1E-09 3.2E-10 1.2E-09 sweet, fermented, oily nd nd 1.3E-09 1.7E-08 1.2E-10 3.2E-09 9.9E-11 3.1E-10 8.2E-12 nd 2.1E-11 nd nd nd nd nd nd 8.1E-09 2.5E-08 7.2E-10 4.7E-10 3.1E-09 9.1E-12 7.7E-11 nd 2.0E-10 4.4E-10 camphoreous, acetone, fruity sweet, spicy, banana 4.1E-11 4.6E-11 nd 1.6E-11 nd musty, banana, fruity 6.6E-11 2.9E-10 1.3E-10 1.4E-11 3.5E-10 4.2E-10 1.5E-09 3.6E-10 2.1E-10 1.7E-10 malty, cocoa, nutty 7.6E-09 8.2E-09 7.1E-10 6.1E-10 4.1E-10 3.7E-08 1.3E-08 1.9E-09 1.2E-09 nd nd nd 1.4E-09 nd nd nd nd nd geranium sweet, plastic, floral musty, earthy, fresh nd nd nd nd nd nd nd nd nd nd Compound Name ALDEHYDE 2-Methyl butanal Hexanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 1-Octen-3-ol KETONE 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2- one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin TERPENOIDS nd nd 74 Table 3.3 (cont’d) L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd nd Compound Name ALDEHYDE 2-Methyl butanal Hexanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 1-Octen-3-ol KETONE 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2- one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin 7.8E-11 7.7E-12 5.4E-11 nd nd nd nd nd nd 1.2E-11 1.4E-11 nd nd nd camphoreous, herbal, terpenic nd 3.4E-12 1.9E-12 nd 2.0E-12 6.2E-12 unknown 1.5E-10 6.2E-12 3.3E-09 6.0E-10 nd nd nd nd nd nd nd nd nd nd 4.3E-10 3.0E-11 1.0E-08 7.6E-10 vegetable, onion, cabbage nd nd 3.3E-11 nd cabbage, pungent nd 1.4E-11 nd 6.6E-12 nd nutty, peanut, musty White colored beans (estimated volatile concentration in mol/L) Navy bean NRP RF RP BP NRF RF Otebo bean NRP RP BP Odor Description NRF 1.7E-09 4.0E-09 5.8E-11 7.5E-12 4.3E-09 1.1E-08 3.2E-09 3.0E-10 2.5E-10 1.6E-09 6.2E-09 4.9E-09 1.6E-09 1.8E-10 vegetable, aldehydic, clean 2.6E-09 2.5E-09 3.6E-09 1.0E-10 malty, musty, fermented nd nd nd nd 6.7E-07 8.9E-08 nd 3.2E-10 3.0E-10 3.9E-07 6.1E-08 nd 4.9E-10 1.1E-09 2.6E-10 2.2E-11 4.7E-11 1.9E-10 1.1E-09 3.1E-10 1.3E-10 9.4E-12 3.4E-10 8.1E-10 4.3E-10 2.5E-10 9.2E-11 1.6E-10 9.0E-10 8.2E-10 8.0E-10 5.7E-11 3.6E-10 6.4E-10 2.7E-11 8.9E-12 6.1E-11 1.4E-10 2.7E-10 1.1E-10 8.6E-11 1.2E-09 2.7E-09 1.2E-09 3.5E-10 1.0E-10 8.9E-10 2.1E-09 5.2E-10 5.7E-10 4.5E-11 8.6E-11 1.9E-10 2.8E-11 9.9E-12 1.2E-11 6.9E-11 1.0E-10 3.6E-11 4.5E-11 8.0E-12 nd sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral nd 4.4E-10 4.0E-10 5.8E-11 1.8E-11 3.7E-10 2.1E-08 1.3E-10 nd 5.2E-10 1.9E-09 6.5E-11 1.7E-10 nd 2.3E-09 2.4E-10 1.9E-09 5.4E-10 1.1E-09 3.2E-10 2.1E-10 2.5E-10 2.5E-10 3.0E-10 4.6E-11 nd 1.3E-10 1.7E-10 1.9E-08 8.3E-09 8.4E-11 3.5E-10 1.1E-08 1.6E-07 4.9E-08 6.2E-11 2.1E-10 1.2E-09 6.3E-10 3.5E-11 7.1E-11 8.0E-10 1.3E-09 6.5E-10 nd nd nd nd nd nd sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken 1.0E-08 2.3E-08 3.3E-10 2.3E-09 2.4E-09 2.4E-09 1.6E-08 1.2E-10 1.7E-10 1.9E-11 4.8E-12 9.9E-12 2.4E-10 9.1E-12 nd nd 1.3E-10 1.3E-10 2.0E-11 7.0E-11 nd nd 1.6E-12 nd nd nd nd nd nd nd camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity 1.4E-10 3.3E-10 2.2E-09 4.3E-10 6.7E-11 8.5E-11 1.1E-09 1.6E-10 3.4E-11 2.9E-11 malty, cocoa, nutty 2.8E-09 4.8E-11 5.8E-09 6.9E-09 4.6E-12 4.6E-10 3.4E-10 3.2E-10 nd nd nd nd nd nd nd nd nd geranium sweet, plastic, floral musty, earthy, fresh nd nd nd 7.7E-09 nd nd nd nd nd nd nd nd nd 75 Table 3.3 (cont’d) TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd nd nd Compound Name ALDEHYDE 2-Methyl butanal Hexanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 1-Octen-3-ol KETONE 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2- one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene 5.4E-11 1.3E-11 nd nd nd nd nd nd nd 2.4E-11 nd 4.2E-11 nd camphoreous, herbal, terpenic nd 3.0E-11 nd nd nd unknown nd 2.7E-11 1.7E-10 8.8E-09 7.1E-12 5.0E-11 3.4E-10 2.5E-12 1.7E-09 8.9E-12 vegetable, onion, cabbage nd nd nd 1.2E-12 nd nd 4.3E-12 nd nd 9.9E-12 nd nd 2.8E-10 nd nd nd nd cabbage, pungent nd nutty, peanut, musty Yellow colored beans (estimated volatile concentration in mol/L) Manteca NRP RF RP BP NRF RF Mayacoba NRP RP BP Odor Description NRF 2.6E-09 4.0E-09 7.7E-10 1.6E-09 1.2E-09 1.2E-08 5.3E-09 1.3E-09 6.6E-09 5.0E-10 malty, musty, fermented 9.0E-09 6.3E-09 4.5E-09 2.0E-09 1.8E-09 4.7E-09 8.5E-09 1.9E-09 8.4E-10 7.2E-10 vegetable, aldehydic, clean 2.5E-10 1.4E-10 4.7E-07 2.3E-07 5.4E-10 2.5E-08 4.4E-10 5.0E-09 2.6E-08 6.9E-10 4.9E-10 1.3E-10 1.3E-10 1.5E-10 4.8E-10 1.1E-09 8.0E-11 9.3E-11 9.9E-11 6.7E-10 7.7E-10 4.8E-10 5.7E-10 1.1E-09 1.1E-09 1.2E-09 3.0E-09 2.2E-09 8.2E-10 2.3E-10 2.5E-10 5.1E-11 5.9E-11 1.2E-10 2.1E-09 4.3E-10 2.4E-10 4.1E-10 7.7E-11 1.0E-09 1.3E-09 2.8E-10 3.9E-10 9.8E-10 1.2E-09 1.7E-09 1.4E-10 3.1E-10 6.0E-10 4.3E-11 1.1E-10 3.2E-11 4.6E-11 8.0E-11 6.0E-11 1.2E-10 1.4E-11 4.3E-11 3.5E-11 sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral nd nd 8.9E-09 1.8E-08 2.6E-09 2.0E-10 2.9E-09 1.5E-09 3.5E-10 5.9E-10 musty, vegetable, cocoa 1.2E-10 4.6E-09 7.7E-10 1.2E-09 sweet, fermented, yeasty 1.3E-10 2.7E-10 3.5E-10 4.1E-11 1.2E-10 1.3E-10 3.8E-10 1.7E-10 5.6E-11 9.9E-09 1.6E-08 7.2E-09 3.0E-09 2.4E-10 1.6E-09 1.3E-08 6.2E-09 1.5E-09 1.0E-09 sweet, pungent, herbal 2.3E-09 7.5E-10 1.6E-09 1.2E-09 1.1E-10 4.1E-10 1.5E-09 1.7E-09 4.9E-10 1.5E-10 vegetable, mushroom, chicken 3.3E-11 2.1E-09 1.5E-08 3.3E-10 8.7E-10 3.5E-10 sweet, fermented, oily nd nd 1.5E-08 1.8E-08 3.4E-10 2.7E-10 1.6E-09 8.8E-08 3.1E-08 6.7E-09 3.6E-09 9.8E-09 2.5E-11 2.4E-10 2.2E-11 1.3E-11 4.5E-11 1.8E-10 2.3E-10 1.3E-11 1.4E-11 nd camphoreous, acetone, fruity sweet, spicy, banana nd nd 5.9E-11 2.1E-11 nd 2.7E-11 nd nd nd nd musty, banana, fruity 6.6E-10 1.7E-09 3.5E-10 3.1E-10 2.6E-10 2.3E-09 1.3E-09 6.8E-09 5.3E-10 2.7E-10 malty, cocoa, nutty 1.6E-09 3.5E-11 4.0E-11 2.1E-11 1.5E-09 1.8E-09 1.9E-11 2.5E-10 5.9E-10 1.1E-10 geranium sweet, plastic, floral 4.5E-11 nd nd nd nd nd nd nd nd nd 76 Table 3.3 (cont’d) Geosmin TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd Compound Name ALDEHYDE 2-Methyl butanal Hexanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 1-Octen-3-ol KETONE 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2- one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene nd nd 1.1E-10 1.8E-11 nd nd nd 1.0E-11 nd nd nd nd nd nd nd nd nd 1.4E-10 2.6E-11 nd nd nd nd musty, earthy, fresh nd camphoreous, herbal, terpenic nd 4.5E-12 nd 5.1E-12 nd 5.5E-12 unknown 6.5E-11 6.4E-10 1.1E-11 6.9E-09 1.1E-10 6.6E-11 3.4E-10 1.9E-10 2.1E-09 3.6E-09 vegetable, onion, cabbage 3.1E-12 nd nd nd nd nd nd 8.1E-12 nd cabbage, pungent 4.9E-11 nd 4.4E-12 nd nd 3.1E-10 nd 5.6E-11 nd nutty, peanut, musty Other pulses (estimated volatile concentration in mol/L) Cranberry NRP Chickpea 2022a NRP NRF RF RP BP RF RP BP Odor Description NRF 3.1E-09 1.7E-09 3.4E-09 7.1E-10 1.6E-09 4.9E-08 4.5E-08 8.5E-10 8.2E-09 1.2E-08 9.7E-10 4.7E-10 3.7E-09 vegetable, aldehydic, clean 8.7E-11 1.8E-09 9.2E-09 2.0E-10 4.4E-10 1.2E-09 malty, musty, fermented nd nd nd nd nd 5.0E-10 4.4E-10 5.1E-09 3.8E-09 9.4E-08 3.0E-08 9.7E-09 2.0E-10 4.5E-10 4.7E-10 1.2E-10 5.1E-10 6.9E-10 2.4E-11 2.8E-11 4.4E-11 6.4E-10 6.4E-10 4.3E-10 3.8E-10 3.5E-10 5.5E-10 6.3E-10 3.4E-10 4.0E-10 2.0E-10 1.5E-10 9.9E-11 1.4E-10 1.4E-10 5.5E-11 6.2E-10 6.5E-10 1.3E-11 1.8E-11 3.3E-11 8.0E-11 6.0E-10 1.5E-09 1.6E-09 2.5E-10 2.9E-09 3.6E-09 6.4E-10 6.6E-10 4.2E-10 9.9E-12 5.4E-11 4.5E-11 5.4E-11 1.3E-08 2.7E-10 3.4E-10 1.4E-11 2.2E-11 2.1E-11 sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral nd nd 1.5E-09 9.3E-09 1.4E-10 4.4E-10 6.5E-10 1.4E-09 2.9E-09 1.9E-09 7.9E-11 3.1E-09 4.2E-09 3.6E-10 3.4E-11 2.2E-08 2.6E-07 5.7E-10 6.3E-10 5.1E-10 7.8E-10 3.7E-10 1.4E-09 1.4E-09 3.2E-10 3.7E-10 9.7E-10 6.6E-10 4.4E-10 1.2E-10 1.5E-10 1.1E-10 1.8E-09 1.1E-09 2.3E-10 vegetable, mushroom, chicken 9.6E-10 1.0E-09 5.9E-09 1.0E-09 1.0E-09 2.8E-10 musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal sweet, fermented, oily 8.7E-10 9.1E-10 nd nd nd nd nd nd nd 2.2E-08 1.0E-08 nd 1.5E-11 1.5E-10 5.5E-11 7.5E-11 3.6E-11 nd nd 1.3E-08 6.0E-08 2.9E-10 1.9E-10 3.4E-09 3.6E-11 1.5E-10 1.2E-11 nd nd camphoreous, acetone, fruity sweet, spicy, banana 2.4E-12 2.3E-10 nd nd 8.9E-12 5.9E-11 8.7E-10 2.7E-11 1.7E-11 nd musty, banana, fruity 5.1E-10 4.4E-10 1.2E-10 1.1E-10 3.3E-11 4.2E-10 4.4E-10 5.2E-10 1.3E-10 1.3E-09 malty, cocoa, nutty 2.8E-09 3.5E-11 1.2E-08 1.8E-08 1.3E-09 3.2E-09 geranium nd nd nd nd 77 Table 3.3 (cont’d) Styrene 2.5E-11 1.1E-10 Geosmin TERPENOIDS L-limonene ALKANE Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd 1.8E-11 3.2E-11 9.4E-13 nd 1.5E-10 1.1E-11 nd nd nd nd nd nd nd nd nd 1.4E-10 nd nd nd nd nd nd nd 4.8E-10 1.8E-09 nd nd nd nd nd nd 7.9E-12 sweet, plastic, floral nd musty, earthy, fresh nd 1.3E-11 3.6E-10 nd nd 2.2E-11 camphoreous, herbal, terpenic nd nd 9.3E-12 1.4E-12 nd nd unknown 6.5E-12 3.0E-11 nd nd nd nd nd nd 4.4E-10 5.8E-09 2.4E-09 5.9E-10 vegetable, onion, cabbage nd nd nd nd cabbage, pungent nd 3.2E-11 nd 7.9E-12 nd nutty, peanut, musty 78 Figure 3.4: Heatmap of log-scaled GC-MS peak areas for volatile compounds varying according to pulse variety from the following eight cultivars grown in 2022- Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), and Great Northern (GN) for non-roasted flour (NRF) and non-roasted porridge (NRP) samples. The asterisks (*) refer to volatile compounds not authenticated using chemical standards. Pulse samples and volatile compounds are clustered according to hierarchical clustering analysis. Non-roasted model product A heatmap plot (Figure 3.4) visualizes the variation in volatile compounds between NRF and NRP samples. The flour samples are clustered on the right half of the figure, while non-roasted model products are clustered on the left, illustrating the impact of the final cooking step before consumption on volatile composition of the model product. Volatile compounds were categorized into three distinct clusters based on their response to heat treatment. The top cluster included 79 volatiles that increased after cooking, with higher concentrations in NRP compared to NRF. These compounds—hexanal, nonanal, 2-pentyl furan, and 2-heptanone—are known contributors to beany flavor (Akkad et al., 2023; Jiang et al., 2016). In contrast, the middle cluster consisted of long-chain aldehydes and ketones that showed moderate variations, while the bottom cluster included aromatics and terpenoids that were more abundant in NRF but decreased or disappeared after cooking into NRP. Cooking NRF into NRP led to the highest significant (p<0.05) increase in total volatile concentration for white-colored beans (GN, N, O, WK) (Figure 3.2). Unlike the RP samples, cooking NRF into NRP increased the abundance of aldehydes in the top cluster of Figure 3.4 in white-colored beans (GN, N, O, WK). Additionally, cooking NRF into NRP also increased alcohols like maltol, 1-hexanol, and 3-methyl butanol in all white-colored (GN, N, O, WK) and yellow-colored (MY, MN) beans. Cooking also increased the total aromatic concentration in NRP samples of N, CR, and MY compared to NRF samples (Table 3.3). These aromatics, such as furans, are primarily produced through the Maillard reaction and the thermal degradation of sugars, amino acids, carotenoids, and polyunsaturated fatty acids (PUFAs) like linoleic acid (Izzotti & Pulliero, 2014; Min et al., 2003). On the other hand, cooking significantly reduced as the concentration of the terpene limonene, which was notably absent in the NRP samples of white-colored beans (N, O, WK, GN) but present in their NRF counterparts. Mishra et al. (2017) previously reported a significant reduction in terpenes of red kidney beans upon cooking. Similarly, Ma Zhen et al. (2016) observed a reduction in limonene content in navy and red kidney beans after cooking. Overall cooking NRF into NRP influenced volatile formation pathways by increasing total volatiles, alcohols, and aldehydes while decreasing terpenoids in white-colored beans. Boiling Boiling significantly reduced total volatile concentrations across all cultivars, with BP samples exhibiting the lowest levels compared to NRP (Figure 3.2). This effect was particularly pronounced in white-colored pulses (GN, N, O, WK), where boiling led to an average 95% decrease in total volatile concentration, while CHKP and CR showed a 75% reduction. Notably, boiling effectively reduced alcohol and aldehyde content, key contributors to beany flavors in pulses (Gao et al., 2020; Roland et al., 2017; Sessa & Rackis, 1977; Xu et al., 2019). Alcohol concentrations dropped significantly (p < 0.05) in nearly all cultivars, averaging an 83% reduction (Figure 3.3B). Likewise, aldehyde concentrations declined by an average of 90% (Figure 3.3C). 80 Our findings align with previous research demonstrating significant reductions in volatile compounds during boiling. Ma Zhen et al. (2016) observed an average 61.75% reduction in targeted total volatile concentrations in boiled bean slurries of navy bean, red kidney bean, green lentil, and yellow pea compared to untreated flours. Azarnia et al. (2011) reported significantly reduced volatile concentrations in cooked peas and pea slurries, while Barra et al. (2007)found similar reductions in cooked French beans. Whitfield & Shipton, (1966) also reported a decline in volatiles in blanched peas. Similarly, Del Rosario et al. (1984) found decreased alcohol concentrations in soybean and winged bean headspace samples upon heating, and Ma Zhen et al. (2016) reported reduced alcohol and aldehyde content in boiled bean slurries of navy beans, red kidney beans, green lentils, and yellow peas. These findings suggest that boiling and extended thermal treatments cause a loss or reduction of volatile compounds, particularly aldehydes and alcohols. The denaturation of proteins during wet heating exposes active sites in proteins, such as the α-amino group of lysine and the thiol group of cysteine. These sites bind oxygenated lipid decomposition products, forming stable lipoprotein complexes that reduce the olfactory impact of volatile compounds (Beyeler & Solms, 1974). As a result, the overall volatile concentration declines significantly in BP samples (Ma Zhen et al., 2016). The impact of thermal processing on volatiles varies depending on the processing method (roasting vs. boiling), pulse type, and final product (flour vs. model product vs. boiled whole pulse). While boiling effectively reduced key beany flavor markers (aldehydes, alcohols) in pulses, roasting may offer a more practical pre-treatment strategy because it 1) is easier to apply in the production of pulse flour used in convenience gluten-free products 2) preserves nutritional quality better than boiling 3) is more energy-efficient than other thermal treatments such as boiling and spray drying. For instance, Chukwuma et al. (2016) reported that roasting preserved the nutritional value of quality protein maize by retaining higher lysine and methionine content, while boiling led to greater nutrient loss. Roasted maize also retained significantly higher crude protein, crude fat, crude fiber, ash, and carbohydrate content compared to both boiled and raw maize. From an industrial perspective, manufacturers aim to improve product quality while reducing energy consumption. Okada et al. (1980) found that spray-drying was the most energy-intensive process, requiring 5,040 kJ/kg IC, whereas roasting required only 890 kJ/kg IC. 81 Thus, while boiling significantly reduces key contributors to beany volatiles, roasting, on the other hand, offers a more energy-efficient and scalable solution for processing pulse flours while preserving nutritional integrity. Figure 3.5: Principal Component Analysis (PCA) biplot to visualize the effect of harvest year on concentrations of volatiles grouped by chemical classes in non-roasted flour (NRF) from pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN), grown in harvest years (2022 and 2023) from Michigan, and a market sample of Chickpea (CHKP) (harvested in 2022). *CHKP_2022a: analyzed in April 2024 with 2022 samples; CHKP_2022b: analyzed in September 2024 with 2023 samples. Circles (●) represent harvest samples from 2022 and 2022a while triangles (▲) denote samples from 2023 and 2022b harvest respectively. Hierarchical cluster analysis assigned colors and grouped samples into clusters with similar volatile profiles. 82 Effect of Cultivar on Volatile Profiles Harvest year and seed coat color drove key differences in the volatile profiles of pulse cultivars. Principal component analysis (PCA) revealed that PC1, PC2, and PC3 accounted for 32.4%, 21.1%, and 17.4% of the variance, respectively (Figure 3.5). Hierarchical cluster analysis (HCA) further highlighted distinct clustering patterns based on harvest year and pulse type. Cultivars from the 2022 harvest grouped into clusters 1, 2, and 3, while samples from the 2023 harvest showed distinct clustering based on seed coat color such that white (GN, N, O, WK) and yellow (MN, MY) colored beans formed cluster 4, whereas CR and CHKP grouped into clusters 5 and 6, respectively. Yellow-colored (MY, MN) beans, CR, and CHKP exhibited higher concentrations of sulfurous compounds compared to white-colored cultivars (Figure 3.4). Among NRP samples, CR had the highest total sulfur concentration, particularly dimethyl disulfide. Similarly, CHKP and MY contained the highest levels of methanethiol (Figure 3.4). These findings align with Ma Zhen et al. (2016) who reported greater concentrations of dimethyl disulfide and methanethiol in untreated red kidney beans than in white colored navy beans. Future sensory studies should investigate whether the increased sulfurous concentration in darker-colored and pigmented pulses influence their sensory perception compared to lighter-colored varieties. In contrast, white-colored beans (GN, N, O, WK) contained higher concentrations of aldehydes, and alcohols. They exhibited elevated levels of alcohols like 1-hexanol, maltol, 3-methyl butanol and aldehydes such as hexanal, (E)-2-hexenal, benzaldehyde, and nonanal compared to CHKP and CR (Figure 3.4). (E)-2-hexenal and 1-hexanol were particularly abundant in NRP samples of white-colored cultivars in GN, N, and O (Table 3.3). Consequently, among all cultivars, the NRP samples of white-colored beans (GN, N, O, WK) had the highest total estimated volatile concentration, surpassing both yellow-colored pulses (MY, MN), CHKP, and CR. Specifically, NRP samples of Navy beans exhibited the greatest volatile concentration, followed by Great Northern, Otebo, and White Kidney beans (Figure 3.2). Navy beans also had the highest aromatic content among all cultivars, with 2-ethyl furan and 2-pentyl furan dominating its NRP sample (Figure 3.4). These findings align with previous research by Ma Zhen et al. (2016) which reported that untreated navy bean flour contained the highest volatile abundance among Saskatchewan pulse varieties, whereas untreated red kidney bean flour had the lowest. Previous research suggests that carotenoid degradation contributes to the formation of terpenoids and hydrocarbons (Murray et al., 1976; K. Wang & Arntfield, 2017). Olumide O. Fashakin et al., 83 (n.d.) found that pigmented NRF samples of yellow-colored beans (MY, MN) contained the highest carotenoid concentrations compared to white-colored beans (GN, N, O, WK). Consequently, our study showed that NRF samples of yellow-colored beans (MY, MN) exhibited the highest concentrations of terpenoids, particularly limonene, compared to white (GN, N, O, WK) and other (CHKP, CR) pulses (Figure 3.4). Previous research has also demonstrated that terpene content varies significantly by cultivar in common beans. Pinto beans, for instance, contain approximately 16 times more terpenes than black beans, while dark red kidney bean cultivars contain the lowest terpene content (Karolkowski et al., 2021; Oomah et al., 2007). Overall, darker-colored pulses were characterized by higher concentrations of sulfurous compounds, yellow-colored beans contained the most terpenoids, and white-colored beans were abundant in alcohols and aldehydes. Effect of year The ANOVA results demonstrated that harvest year had a significant effect on total volatile concentrations across all samples (p = 3.3E-11) (Table 3.2). HCA and PCA further revealed distinct volatile profiles based on harvest year. Specifically, NRF samples from 2022 clustered in quadrants 2 and 3, while those from 2023 grouped in quadrants 1 and 4 (Figure 3.5). Within these clusters, samples from the 2022 harvest—N, WK, CR, and MN—grouped in cluster 1, while GN formed a distinct cluster 3 in quadrant 2. For the 2023 harvest, N, WK, GN, and MN clustered together (cluster 4) in quadrant 1, while MY and CR formed cluster 5. Notably, CHKP, analyzed at two time points, formed cluster 6 in quadrant 3, while the O cultivar from the 2022 and 2023 harvests grouped in cluster 2. This difference in clustering patterns across harvest years may also be attributed to the varying time intervals between harvest and volatile analysis, as pulses harvested in 2022 were analyzed 18 months post-harvest, whereas those from 2023 were analyzed 12 months post-harvest. NRP and NRF from the 2022 harvest year exhibited substantially higher total volatile concentrations compared to those from 2023 (Figure 3.2). This suggests seed maturity due to a prolonged storage period (18 months post-harvest) in the mature 2022 harvest year samples could have influenced the accumulation of volatiles, whereas 2023 samples were analyzed after 12 months. The NRF from the 2022 harvest showed higher concentrations of alcohols, ketones, and aromatics such as xylene and styrene across all cultivars (Table 3.3, Table S1), while NRF from the 2023 harvest exhibited higher concentrations of aldehydes than mature 2022 samples (Figure 84 3.5). This contrasts with previous studies by Manouel et al. (2024) where the concentration of hexanal in pea flours followed the order 2018 > 2019 > 2020 > 2022, indicating that increased seed age significantly increased hexanal content. Interestingly, in our study, hexanal concentrations in NRF followed the order 2023> 2022, except for CHKP (Table 3.3, Table S1). Since CHKP was commercially sourced, it was grown and harvested in a different location in 2022 compared to the other dry bean cultivars, although it was processed and analyzed within the same overall time frame. For instance, CHKP_2022a was analyzed after 18 months of storage in the same batch as the 2022 dry bean samples, and CHKP_2022b was analyzed after 30 months, alongside the 2023 dry bean samples. Despite this difference in storage time, both mature CHKP_2022b and newer CHKP_2022a flours exhibited comparable volatile profiles and concentrations. This suggests that growing year and environmental conditions a pulse crop endures in a specific harvest year may have a greater influence on volatile profiles than storage time alone. The influence of storage time and temperature on volatile profiles was studied by Akkad et al. (2022), who observed that volatiles like hexanal, nonanal, 2-pentyl furan, and 2-heptanone increased with prolonged storage in faba beans. However, our finding of lower hexanal content with increased seed age in the 2022 harvest NRF reinforces that other factor related to harvest year, rather than storage duration, likely contributed to the differences observed. Environmental conditions such as temperature, light exposure, water availability, and soil composition play a significant role in lipid metabolism, as plants under stress often produce more saturated fatty acids to stabilize cellular membranes. Additionally, genetic and biochemical responses specific to the growing environment may alter the activity of enzymes responsible for fatty acid synthesis, further impacting volatile profiles. Other differences in fatty acid composition due to location, harvest year, and storage duration, could further influence the production of volatile compounds (Manouel et al., 2024). These combined factors likely explain the differences in volatile profiles observed between the two years. These insights are crucial for determining optimal storage time and temperature while considering crop year variations and the environmental and soil conditions during cultivation. Conclusion This study examined how cultivar, harvest year, and processing methods influenced the volatile composition of pulses. Cultivar differences were primarily driven by seed coat color, which played 85 a key role in shaping volatile profiles. Understanding these differences can aid in selecting pulses for targeted food applications. The distinct clustering pattern of CHKP flours despite variation in seed maturity suggests that environmental and growing conditions may have a greater influence on volatile profiles across harvest years than prolonged storage period alone. Processing methods altered VOC composition, with major differences observed between non-roasted and roasted samples. Cooking roasted flour was more effective in reducing key volatiles compared to direct cooking of non-roasted flours. Since these targeted volatiles have been cited as beany flavor markers, roasting may serve as an effective pre-treatment strategy to reduce these flavors in cooked pulse-based products. Further research is needed to optimize roasting conditions based on seed size and color to minimize sulfur compound formation and to identify specific volatile markers associated with off-flavors in pulses. Additionally, investigating the role of nitrogenous compounds generated during heat-treatment is essential to identify if they mask or intensify off- flavors in pulses. Future research should incorporate sensory analysis to better understand how volatile compounds influence odor perception and acceptability in pulse-based products. 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Springer-Verlag New York. https://ggplot2.tidyverse.org Xu, M., Jin, Z., Lan, Y., Rao, J., & Chen, B. (2019). HS-SPME-GC-MS/olfactometry combined with chemometrics to assess the impact of germination on flavor attributes of chickpea, lentil, and yellow pea flours. Food Chemistry, 280, 83–95. Zhao, X., Sun, L., Zhang, X., Wang, M., Liu, H., & Zhu, Y. (2021). Nutritional components, volatile constituents and antioxidant activities of 6 chickpea species. Food Bioscience, 41, 100964. 92 Chapter 4: Evaluating the impact of cultivar and processing on pulse off-flavor through descriptive analysis, GC-MS, and e-nose Abstract Pulses are nutrient-dense and have a low carbon and water footprint but remain underutilized in the United States. A potential strategy to boost pulse consumption involves milling pulses into flour and incorporating them into convenience products traditionally made from wheat flour. However, addressing off-flavors—commonly described as beany, green, musty, or vegetative—is essential for sustained adoption. This study evaluated the impact of cultivar selection and processing methods (boiling, roasting) on off-flavor reduction in eight pulse cultivars using Descriptive Analysis (DA) and rapid volatile profiling with gas chromatography-mass spectrometry (GC-MS) and electronic nose (e-nose). DA revealed significant differences (p < 0.05) across cultivars and processing treatments for 20 sensory attributes, with roasting reducing green/vegetative and earthy/mushroom/musty off-flavors but increasing beany characteristics, especially in roasted navy bean flour. We identified 8 key volatiles via GC-MS, including ketones, aldehydes, and alcohols that were strongly correlated to vegetative and mushroom flavors. However, GC-MS had limitations in predicting beany off-flavors, likely due to the chosen targeted analytical approach. In contrast, the untargeted e-nose approach effectively distinguished non- roasted and roasted flours, identifying discriminant ions that correlated with sensory attributes like toasted and beany odors. E-nose data aligned better with DA results, highlighting its potential as a first screening tool for rapid flavor profiling. Findings highlight the importance of refining pre- treatment methods and selecting cultivars with milder flavors. E-nose and GC-MS can be used to optimize the sensory quality of pulse flour, supporting increased consumer acceptance of pulse- based products. 93 Introduction Pulses are edible seeds of plants in the legume family (Fabaceae), harvested specifically for their dry grain, excluding oilseeds. Common pulse types include Phaseolus vulgaris (common beans such as kidney beans, navy beans, and pinto beans), Lens culinaris (lentils), Cicer arietinum (chickpeas), and Pisum sativum (peas) (FAO, 1994). Research highlights extensive health benefits from incorporating pulses into daily diets. Pulses have demonstrated their ability to prevent heart disease (Geil & Anderson, 1994) and reduce colon cancer risk due to their rich content of protein, fiber, and folate (Michels et al., 2006). The high fiber and resistant starch content of pulses induces a low glycemic response, which aids in diabetes prevention and management (Ludwig, 2002). Additionally, the nitrogen-fixing ability, soil health benefits, and lower carbon footprint of pulses make them a valuable component of sustainable agricultural and food systems (Reckling et al., 2016). For instance, despite the similar protein contents in pulses and meats (typically between 18 and 26%), it was observed that pulses have a significantly lower global warming potential of 0.7 kg carbon dioxide equivalents (CO₂ eq)/kg compared to animal-derived sources such as boneless beef of 29 kg CO₂ eq/kg of (Clune et al., 2017). Despite their numerous benefits, pulse consumption in the U.S. remains notably low. While annual production reaches 2.9 million tons, National Health and Nutrition Examination Survey (NHANES) (2003–2014) revealed that only 27% of adults (≥19 years) reported consuming pulses, with an average intake of just 70.9 ± 2.5 g/day over two days—equivalent to less than 0.5 cup equivalents per day. The 2025 Dietary Guidelines Advisory Committee (2024) reported that 83% of Americans consume pulses below the recommended dietary intake level. The 2025–2030 Dietary Guidelines for Americans propose increasing the recommended intake of beans, peas, and lentils to 2.5 cups/week, up from the previous recommendation of 1.5 cups/week in the 2020–2025 guidelines (2025 Dietary Guidelines Advisory Committee; Garden-Robinson & West, 2023; Haven, 2021a; Mitchell et al., 2021; Sadohara et al., 2022). Common barriers to pulse consumption include a general dislike of their taste and texture, lack of familiarity and preparation knowledge, and limited interest among specific demographics, such as Midwestern U.S. university students aged 18–30 and adults over 65 (Doma et al., 2019; Winham et al., 2020). To encourage greater pulse consumption, milling pulses into flour and using them in products typically made with wheat flour can be an effective approach (Sadohara et al., 2022). Pulses are particularly suited for the growing gluten-free market, offering superior nutritional profiles 94 compared to traditional gluten-free alternatives like corn, rice, and potato flour. However, maintaining the acceptable taste and texture of gluten-free pulse-based products remains a challenge for their sustained adoption (Sozer et al., 2017). Adding to this difficulty is the presence of off-flavors, often described as "beany," which further limits the appeal of pulse flour in convenience products (Sadohara et al., 2022). This broad term encompasses sub-character notes such as musty, earthy, green, and pea pod aromas (Chigwedere et al., 2022; Vara-Ubol et al., 2004), which aligns with previously reported findings on undesirable flavors in pulse-based products (Troszyńska et al., 2011; Vara-Ubol et al., 2004). In this study, these sub-character notes are collectively referred to as "known off-flavors" (Roland et al., 2017; Sadohara et al., 2022). However, consumer acceptance studies are needed to determine whether "known off-flavors," such as vegetative/green and earthy/musty notes, negatively influence consumer perception of pulses. Additionally, while the beany flavor has often been classified as an off-flavor, its impact on consumer liking and acceptability may vary depending on the product context and hence in this study isn’t referred as an off-flavor (Chigwedere et al., 2022). Off-flavors arise from chemical and biochemical reactions, primarily the oxidation of unsaturated fatty acids like linoleic and linolenic acids through enzymatic lipoxygenase (LOX) activity or non- enzymatic pathways to generate hydroperoxides that decompose into volatile compounds (MacLeod et al., 1988; Rackis et al., 1979). The concentration and intensity of these volatiles vary between pulse types and cultivars largely due to differences in macronutrient composition (N. Singh, 2017). Additionally, pre-treatments such as roasting, boiling, spray drying, freeze drying, and germination can alter the volatile abundance, depending on the pulse variety (Akkad et al., 2019; Azarnia et al., 2011; Chang et al., 2019; Ma et al., 2016). Volatile compounds responsible for off-flavors in pulses, including aldehydes, alcohols, ketones, acids, pyrazines, and sulfur, can be minimized through cultivar selection and process optimization (Roland et al., 2017). LOX- derived volatiles, including hexanal, 3-cis-hexenal, n-pentyl furan, 2-(1-pentenyl) furan, and ethyl vinyl ketone, have been identified as key contributors to grassy, green, and beany off-flavors (Rackis et al., 1979). It is essential to identify the specific volatile compounds most responsible for off-flavors to reduce their impact on the overall perception of pulses. Hence an ideal approach to studying off-flavors in pulses would involve combining instrumental analysis with sensory evaluation for a more comprehensive understanding (Viana & English, 2021). However, time- intensive panel training and the high costs associated with sensory evaluation make it less practical 95 for mild-flavored cultivar selection and rapid process optimization to reduce off-flavors in pulses (Shurmer & Gardner, 1992). To address these limitations, instrumental analytical methods have become integral for efficiently evaluating volatile organic compounds (VOCs) that drive flavor in pulses and other foods. The most commonly used method for analyzing volatile compounds in pulses is Headspace Solid- Phase Microextraction (HS-SPME) Gas Chromatography coupled with Mass Spectrometry (GC- MS). It is particularly effective for identifying and characterizing individual volatile compounds due to its high sensitivity and resolution (Karolkowski et al., 2021; Khrisanapant et al., 2019). For instance, Murat et al. (2012) reported that SPME and solvent-assisted flavor evaporation (SAFE) offered a better representation of yellow pea flour odors than dynamic headspace techniques like the Purge and Trap method. New methodologies integrating electronic sensors, such as electronic noses (e-noses) and electronic tongues (e-tongues), have emerged as promising alternatives. Over the past decade, e-nose systems integrating mass spectrometry or fast gas chromatography have been developed (Wilson & Baietto, 2009). These systems operate at higher temperatures and flow rates for rapid volatile analysis. Volatile compounds are separated via chromatographic columns and detected using surface acoustic wave (SAW) sensors or flame ionization detectors (FID), producing a profile of volatile constituents (Wardencki et al., 2013). Discriminant ions from e-nose, such as m/z 78 and 124, have been previously used as markers for distinguishing ripening changes in legumes based on the increased relative concentration of sulfuric compounds, particularly 1,2,4-trithiolane, in matured legumes (Asikin et al., 2018). However, the application of e-nose technology to pulse-based products remains limited. Efforts are needed to develop calibrated models capable of identifying discriminant ions responsible for off-flavors in pulses. Additionally, while e-nose shows potential for rapid flavor monitoring, its ability to represent overall odor perception in pulse products accurately requires further investigation, alongside comparative studies with traditional extraction techniques. Hence, this study aims to: 1) characterize the sensory attributes of pulses through descriptive sensory analysis, and 2) identify chemical markers associated with off-flavors using instrumental techniques. By examining the effects of cultivar variation and processing methods (boiling and roasting), the study seeks to identify cultivars with milder flavor profiles and evaluate the sensory trade-offs involved in processing to reduce off-flavors. These findings aim to enhance the sensory quality and consumer acceptance of pulse-based food products. 96 Materials and Methods Germplasm selection and seed production The dry bean market classes selected for this study with their respective abbreviations and cultivar (cv.) or genotypes are listed as follows: Navy (N, cv. ‘Alpena’); Otebo (O, cv. ‘Samurai’); Great Northern (GN, cv. ‘Powderhorn’); White Kidney (WK, cv. ‘WK 1601-1’); Mayacoba (MY, cv. ‘Y 1802-9-1’); Manteca (MN, cv. ‘Y1608-07’); and Cranberry (CR, cv. ‘CR1801-2-2’) (Figure 4.1). The rationale for selection of these beans was based on their adaptation to Michigan’s agricultural conditions, seed yield potential, and representation across market classes. For the potential higher acceptance of pulse flour, cultivars with white or lighter seed coat colors, such as Navy, Otebo, Great Northern, and White Kidney, were chosen. These beans were cultivated at the Michigan State University Montcalm Research Center in Entrican, Michigan, during the year 2022. The seeds were sown in a randomized complete block design with three field replicates, with plots consisting of four 6.1 m rows, where the center rows contained the experimental lines, and the outer rows were standard bordered with kidney beans. Field maintenance practices included weed control, fertilization, and insect management, with supplemental irrigation as needed. The seeds were harvested on September 29 using a Hege 140 plot combine harvester. Post-harvest, the seeds were cleaned manually to remove debris and stored in paper bags at room temperature for further analysis. Additionally, a Kabuli Chickpea (CHKP, cv. ‘Sierra’) obtained commercially, grown in 2022 on a Montana commercial farm was chosen in this study for its industrial significance in U.S. production. Non-roasted pulse flour (NRF), non- roasted pulse flour porridge (NRP), roasted pulse flour (RF), roasted pulse flour porridge (RP), and boiled pulses (BP) were produced from each of the eight pulse genotypes (Figure 4.2). 97 Figure 4.1: Image of the eight cultivars included in this study, arranged by market class, abbreviation, and corresponding genotypes (shown in parentheses). Pulse flour production The pulses were rinsed under distilled water, spread on a tray lined with paper towels, and allowed to air dry for 12 hours. Some of the cleaned and dried pulses were roasted by dry heat in an oven (Fisher Scientific Isotemp Gravity Oven, 100 L) at 110°C for 70 minutes, then allowed to cool for 4 hours. Once dried, the non-roasted and roasted seeds from each of the eight pulse varieties were milled into flour using a hammer mill (Polymix® Laboratory Grinding Mills, PX-MFC 90 D, Kinematica), fitted with a 0.5 mm sieve to produce NRF and RF samples. Pulse porridge and boiled pulse preparation Both NRF and RF samples were used to prepare porridges for sensory and volatile analyses using the same procedure to understand the cooked properties of the pulse flour. To prepare the porridge, 50 g of pulse flour (non-roasted or roasted) was mixed with 250 mL of water to form a slurry and stirred for 7 minutes. An additional 300 mL of distilled water was then added, and the mixture was cooked at 150°C and mixed at 1500 rpm for 25 minutes using an MSE PRO LCD 4-Channel Digital Magnetic Hotplate Stirrer, producing NRP and RP samples. BP samples were prepared by soaking pulses in distilled water for 12 hours at room temperature, followed by boiling on a Duxtop 98 1800W Portable Induction Cooktop until fully cooked (Figure 4.2). Cooking times were determined using a Mattson pin drop cooker as follows Otebo (16 min), Navy (24 min), Great Northern (23 min), White Kidney (30 min), Chickpea (45 min), Manteca (20 min), Mayacoba (33 min), and Cranberry (50 min). NRP, RP, and BP samples were prepared fresh on the day of testing for sensory and GC-MS volatile analysis. NRF and RF samples were stored in sealed bags after milling under refrigeration at 2°C to reduce volatile loss (Akkad et al., 2022). Figure 4.2: Flowchart of preparation methods for five types of samples. Electronic nose (e-nose) and gas chromatography-mass spectrometry (GC-MS) analyses were conducted on NRF and RF samples; GC-MS and descriptive analysis (DA) were performed on NRP, RP, and BP samples from each of the eight pulse types of Navy, Otebo, Great Northern, White Kidney, Mayacoba, Manteca, Cranberry and Chickpea. 99 Descriptive analysis The NRP, RP, and BP samples were characterized for their sensory profile using quantitative descriptive analysis (DA, ISO 11035:1994). Since flour cannot be directly consumed by human panelists, to understand the characteristics of pulse flour in its simplest form, pulse porridges from the pulse flour and boiled pulses from raw seeds were prepared as described above for sensory assessment. Sensory panelists were recruited and screened for taste acuity and verbal ability to participate in a six-week descriptive analysis (DA) panel. The panel consisted of 9 panelists (2 males, 7 females) aged 18-41. The panelists underwent a training program consisting of 27 one-hour sessions. During the initial 15 training sessions of the study, panelists received instruction on DA methodology, engaged in term generation and refinement, and reference selection. The subsequent 3 sessions included reference scaling, followed by 9 sessions dedicated to group sample evaluation practice and panel calibration exercises. Each day, a rotating, balanced subset of pulse samples was provided for panel training and practice. The attributes generated and evaluated by the panel, along with references, definitions, and sample evaluation instructions, are listed in Table 4.1. Between tasting samples, the panelists used a rinse procedure that included the following steps: expectorating the sample, rinsing with room temperature water and expectorating the water, biting into a cracker to cleanse the palate and expectorating, and finally, rinsing with room temperature water and expectorating the water again. After their training, the panelists evaluated samples in individual sensory booths in duplicate using the RedJade sensory software (RedJade Sensory Solutions LLC, Pleasant Hill, CA, USA). Pulse samples were presented following a randomized complete block design, blinded with random 3- digit codes, across four evaluation sessions on four consecutive days. Before each evaluation session, panelists were instructed to recalibrate themselves using freshly prepared reference samples. These references were labeled with their identity and served in plastic cups with lids. The panelists rated attribute intensities of the samples on a questionnaire using a continuous, visual analog scale from 0-15 anchored at the ends by none and strong for most attributes, except for saturation, which was anchored by dull and bright. 100 Table 4.1: Lexicon used to characterize pulse samples, including sensory attributes used in the descriptive analysis, corresponding codes, definitions, references, evaluation procedures for pulse samples, and reference ratings on a 0 to 15 scale. Attributes are organized by sensory modality. Attribute Abbreviation Definition Reference Reference Rating Appearance Protocol for sample: Lid off and evaluate each sample cup over white paper and use the respective color swatches as references for assessment color_value Value 1-3-4-7-9-12-14 Saturation color_saturation The value of the sample from light to dark The saturation of the sample from dull to bright Greyscale (Munsell Color Company) 10YR hue page (Munsell Color Company) Aroma Protocol for sample: Shake samples and crack the corner of the lid to sniff Kidney bean Chickpeas Aroma of canned kidney beans Aroma of canned chickpeas aroma_kidney.bean aroma_chickpeas Canned kidney beans Canned chickpeas Pinto beans aroma_pinto.bean Great northern beans Mushroom Boiled Potato Boiled rice Toasted bread Tofu aroma_great. northern.bean aroma_mushroom aroma_boiled.potato aroma_boiled.rice aroma_ toasted.bread aroma_tofu Grainy aroma_grainy Sweet aroma of canned pinto beans Sour aroma of canned great northern beans Musty aroma of fresh mushroom Aroma of peeled, boiled and mashed potato Aroma of boiled rice Aroma of fresh white Toasted bread Fermented aroma of uncooked tofu Aroma of cooked grains Canned pinto beans Canned great northern beans Uncooked sliced mushroom Boiled potato Boiled rice White Toasted bread Firm tofu Cream of wheat 1-3-5-7-9-11-13-14 13 14 11 11 14 11.5 12 12 10 10 Aroma-by-mouth/Flavor Protocol for sample: Chew one piece of the whole beans thoroughly with back teeth for 3 sec. Move a spoonful of porridge thoroughly around the mouth for 3 sec. Kidney bean Chickpeas flavor_kidney.bean flavor_chickpeas Pinto beans flavor_pinto.bean Great northern beans Mushroom Boiled Potato Tofu flavor_great. northern.bean flavor_mushroom flavor_boiled.potato flavor_tofu Vegetable flavor_vegetable Flavor of canned kidney beans Flavor of canned chickpeas Sweet flavor of canned pinto beans Sour flavor of canned great northern beans Musty flavor of fresh mushroom Aroma of peeled, boiled and mashed potato Fermented flavor of uncooked tofu Flavor of cooked green vegetables Canned kidney beans Canned chickpeas Canned pinto beans Canned great northern beans 13 13 11 12 Uncooked sliced mushroom 14.5 Boiled potato Raw tofu Canned green bean 8.8 7 13 Taste Protocol for sample: Chew one piece of the whole beans thoroughly with back teeth for 3 sec. Move a spoonful of porridge thoroughly around the mouth for 3 sec. Sour taste_sour Sour taste of Citric acid solution 0.05% Citric acid Solution 11 101 Table 4.1 (cont’d) Umami taste_umami Bitter taste_bitter Umami taste of MSG solution Bitter taste of caffeine solution 0.05% MSG solution 0.05% Caffeine Solution 11.2 9 Aftertaste Protocol for sample: Chew one piece of the whole beans thoroughly with back teeth for 5 sec and expectorate. Move a spoonful of porridge thoroughly around the mouth for 5 sec and expectorate. Astringent 0.05% Alum solution aftertaste_astringent 13.5 Bitter aftertaste_bitter 0.05% Caffeine Solution 9 Lingering dryness after swallowing Bitter aftertaste of caffeine solution Headspace Solid-Phase Microextraction coupled with Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) analysis NRF, NRP, RF, RP, and BP samples were analyzed for HS-SPME-GC-MS. Volatile profiles of all five samples were obtained using the same equipment, procedure, and conditions. The following quantities of each sample were placed in 20 mL headspace vials: 2 g each of NRF and RF, 5 g of mashed BP, and 5 g of NRP and RP (each porridge mixed with 1 g NaCl). NaCl addition enhanced volatile extraction by lowering the partitioning coefficient (K) for some analytes and increasing their concentration in the headspace (Westland, 2021). Samples were then analyzed by the HS-SPME-GC-MS method described previously (Chapter 3). Briefly, samples were first equilibrated at 50 °C for 30 minutes followed by exposing a carboxen/polydimethylsiloxane/divinylbenzene (CAR/PDMS/DVB) 2 cm, 30/50 µm, (Supelco, Sigma–Aldrich) SPME fiber to the headspace for an additional 30 minutes at 50 °C. Volatile compounds were desorbed for 20 sec in a split/splitless injector port (200 °C) of a gas chromatograph (Agilent 6890 Gas Chromatograph, Hewlett-Packard Co., Wilmington, DE) and separated on a 30 m × 0.25 mm i.d. HP-5 (Hewlett-Packard) capillary column (0.25 µm) with helium carrier gas at a ramped flow rate initially at 1.2 mL/min and then increased at a rate of 1 ml/min to a final flow rate of 1.8 mL/min. The initial GC oven temperature was set at 32 °C and increased to 60 °C at a rate of 20 °C/min. It was then ramped to 150 °C at a rate of 50 °C/min, followed by a final increase to 280 °C at a rate of 70 °C/min, where it was held for 2 minutes. The total run time for the analysis was 7.4 minutes. Detection was carried out using TOF-MS (LECO Pegasus III) with electron ionization at 70 eV and a mass range of 29–400 m/z. Volatile compounds were identified through comparisons with the National Institute of Standards and Technology (NIST) mass spectra library database (V.05) and/or by matching retention times of authenticated standards. The following volatiles were identified using authenticated pure commercial standards: 102 2-butanone, 2-methyl butanal, butanol, 2-ethylfuran, 3-methylbutanol, dimethyl disulfide, 1- pentanol, hexanal, (E)-2-hexenal, 1-hexanol, o-xylene, 2-heptanone, styrene, heptanal, methional, 2,5-dimethyl pyrazine, benzaldehyde, 1-octen-3-ol, 6-methyl-5-hepten-2-one, octanal, decane, L- limonene, nonanal, decanal, and geosmin, all obtained from Sigma-Aldrich (St. Louis, MO, United States). The peak areas of volatiles collected from the HS-SPME GC-MS analysis were obtained from the average triplicates of the area under the curve (AUC) and reported for a single m/z (mass-to-charge ratio) corresponding to the unique mass (Chapter 3, Table S2). E-nose analysis The volatile profile analysis of pulse flours was also conducted using an ultra-fast chromatographic system Heracles Neo (Alpha MOS, Toulouse, France). The instrument was equipped with two metal capillary columns working in parallel mode and characterized by different polarity and stationary phase: a non-polar column (MXT5: 5% diphenyl, 95% methylpolysiloxane, 10 m length and 180 μm diameter) and a polar column (MXT-1701: 14% cyano-propyl phenyl, 86% dimethyl polysiloxane, 10 m length, 180 μm diameter). An FID detector was connected at the end of each column and the acquired signal was digitized every 0.01 s. NRF & RF samples of all eight cultivars were subjected to e-nose analysis. For each sample, 1 g of flour was placed in a 10 mL glass vial. The headspace extraction was conducted in a septa- sealed screw cap vial that was equilibrated for 20 min at 60°C. Afterward, the headspace above the sample was injected into the electronic nose at the speed of 500 μL /s with a pressure of 10 kPa, a flow rate of 60 mL/min, and an injection time of 60 sec using an automatic headspace sampler (CTC Analytics company, Zürich, Switzerland). The column oven temperature program used for the experiment started at 50°C, held for 2 s, and then ramped at a rate of 3°C/s until it reached 250°C and then held for 5s. The injection temperature of the injector and detector were set at 240°C and 270°C, respectively. For calibration of the method, an alkane solution (from n-hexane to n-hexadecane) was used to convert retention time in Kovats indices to identify possible compound matches using the AroChemBase database (Version 4.6, Alpha MOS Corporation, Toulouse, France). The peak areas indicate the relative concentration of the odor components. 103 Statistical Analysis Sensory and instrumental volatile data were analyzed for sample differences using the R statistical computing software (version 4.2.2; R Core Team, 2022) to conduct Analysis of Variance (ANOVA) and Least Significant Difference (LSD) post hoc multiple comparisons tests using the following packages: tidyverse v. 2.0.0 (Wickham et al., 2019), and agricolae v. 1.3.5 (de Mendiburu, 2021). Principal component analysis (PCA), hierarchical cluster analysis (HCA), and Pearson’s correlation were also conducted and visualized using R statistical computing software (version 4.2.2; R Core Team, 2022) using the following packages: FactoMineR v. 2.8 (Lê et al., 2008), ggplot2 v. 3.5.1 (Wickham, 2016) and Hmisc v. 5.1.2 (Harrell Jr, 2024). The data from the descriptive sensory analysis were analyzed using ANOVA. The multifactorial ANOVA model included interactions (panelist:sample, panelist:day, and sample:day), with panelists treated as a random effect and sample and day as fixed effects. A pseudo-mixed model was applied to verify whether sample effects were significant independently of interactions with panelist and day. Sensory attributes with significant panelist or day interaction effects were excluded, and LSD post hoc analysis was performed on the remaining significant sensory attributes to identify differences in attribute ratings between samples. For all statistical tests, an α of 0.05 was used to determine statistical significance. Mean intensity ratings from duplicate reps for significantly different sensory attributes were used for PCA analysis to identify relationships among pulse samples based on their sensory attributes, and HCA analysis was conducted to segment samples into subgroups sharing common sensory patterns. Radar plots were generated using Microsoft Excel (Microsoft Corporation, Seattle, WA, U.S.A.) The volatile peak areas from the HS-SPME GC-MS analysis represent the average of three replicates (Chapter 3, Table S2). The identified volatile compounds using HS-SPME GC-MS were categorized according to their chemical class as follows- aldehydes, alkanes, alcohols, ketones, terpenoids, sulfurous, nitrogenous, and aromatic compounds and analyzed using ANOVA followed by LSD post hoc multiple comparisons tests. The mean-centered AUC values were analyzed using PCA to examine relationships between volatile profiles and processed pulse samples, as well as HCA to group samples with similar volatile patterns grouped by chemical class. The peak areas for each discriminant ion in a sample from e-nose analysis were obtained from the average of triplicates. Partial least squares regression (PLS) was used to identify discriminant ions from e-nose volatile profiles to correlate chromatograms with mean sensory intensity scores using 104 the Alpha MOS software (Version 2023, Toulouse, France) (Cevoli et al., 2022; Lozano et al., 2007; Ravi et al., 2019). Mean-centered peak areas of discriminant ions were used for PCA to visualize the relationship between pulse flour and discriminant ions, as well as HCA to segment samples into subgroups sharing common discriminant ion markers. PCA coordinate distance matrices from the first three dimensions of the following- descriptive sensory analysis mean ratings (DA), mean peak areas of discriminant ions from e-nose analysis, and means of AUC of volatiles analyzed by HS-SPME-GC-MS were used to conduct Pearson’s correlation test and depicted in a scatter plot. Results and Discussion Descriptive analysis Panelists consistently and significantly (ANOVA, p < 0.05) differentiated pulse varieties based on appearance, aroma, aroma-by-mouth, taste, and aftertaste. The ANOVA results showed that out of twenty-five sensory descriptors, twenty descriptors were significantly discriminating (p < 0.05). The following attributes did not show significance: mushroom odor, boiled potato odor, boiled potato flavor, bitter taste, and bitter aftertaste. Mean panel attribute ratings and least significant difference (LSD) values for the significantly discriminating (p < 0.05) descriptive sensory attributes grouped by modality are reported in Table 4.2. 105 Table 4.2: Mean ratings on a 0 to 15 intensity scale for attributes that showed significant differences between samples (ANOVA, p < 0.05) from descriptive analysis grouped by modality for non-roasted porridge (NRP), roasted porridge (RP) and boiled pulse (BP) of eight pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN). Means are the average ratings for attributes from nine panelists over two replications. Least Significant Difference (LSD) and sample effect p values for each sensory attribute in a column are also reported. For each attribute column, mean values that do not share a letter are significantly different (p < 0.05). Modality: Appearance, Taste and Aftertaste Sample Color Value Color Saturation p-value LSD value 6.8E-81 0.55 4.6E-67 0.49 Taste Sour 8.3E-05 0.99 Taste Umami 5.0E-14 1.04 Aftertaste Astringent 2.0E-07 1.17 N_NRP N_RP N_BP CHKP_NRP CHKP_RP CHKP_BP CR_NRP CR_RP CR_BP GN_NRP GN_RP GN_BP O_NRP O_RP O_BP WK_NRP WK_RP WK_BP MN_NRP MN_RP MN_BP MY_NRP MY_RP MY_BP 2.25l 3.01j 2.36kl 4.72fg 5.32de 6.19c 7.19b 7.51b 8.92a 2.57jkl 4.08hi 3.61i 1.64m 2.79jkl 3.59i 2.9jk 4.74fg 4.82efg 3.74i 5.46d 5.07def 3.02j 4.3gh 3.81hi 2.51jklm 3.36fg 2.78hijkl 5.07b 5.25b 7.69a 2.33lm 2.56ijklm 3.15fgh 2.48klm 4.03cde 4.28cd 2.14m 2.99ghij 3.91de 3.01ghi 4.25cd 5.37b 3.56ef 4.52c 5.16b 2.86hijk 3.39fg 4.06cd 4.61a 4.69a 1.71k 3.94abcde 4.83a 3.1defghij 3.53bcdef 3.52bcdefg 2.91efghij 4.01abcd 4.41ab 2.41hijk 4.34abc 3.97abcd 2.27jk 3.34cdefghi 4.58a 2.31ijk 4.35abc 4.91a 2.48ghijk 2.87fghij 3.43bcdefgh 2.51fghijk 2.82bcd 3.36abc 1.41ef 3.39abc 3.97ab 2.53cde 4.04a 4.15a 2.33cde 2.56cde 2.52cde 2.29cdef 2.07def 3.44abc 1.79def 2.02def 2.66cd 2.01def 1.85def 2.37cde 1.87def 1.76def 2.31cdef 1.16f 1.46bcdefg 1.97abcd 0.88efgh 1.57bcdef 1.44bcdefg 1.47bcdefg 1.49bcdefg 1.39bcdefgh 0.58fgh 1.08defgh 1.55bcdef 0.52gh 1.16cdefgh 1.24cdefgh 0.44h 1.86abcde 2.09abc 0.71fgh 1.4bcdefgh 2.32ab 0.72fgh 1.17cdefgh 2.73a 0.52gh 106 Modality: Aroma Aroma Boiled Rice 1.3E-09 1.13 Aroma Cream Of Wheat 4.2E-03 1.26 Aroma Toasted Bread 3.3E-06 1.02 2.94efghij 3.1cdefghij 3.19cdefghi 3.53cd 3.46cde 2.37efg Table 4.2 (cont’d) Sample Aroma Tofu p-value 9.3E-59 LSD value 0.95 4.59c 2.75fghij 1.68kl N_NRP N_RP N_BP CHKP_ 9.63a NRP CHKP_RP 10.24a CHKP_BP 3.24defgh 2.81fghij CR_NRP 2.46ghijk CR_RP 2.12ijkl CR_BP GN_NRP 3.81cde 1.95jkl GN_RP 2.31hijkl GN_BP 5.63b O_NRP 2.13ijkl O_RP 2.16ijkl O_BP WK_NRP 2.76fghij 2.99efghi WK_RP 2.24ijkl WK_BP MN_NRP 3.28defg 3.64cdef MN_RP 2.01jkl MN_BP MY_NRP 3.97cd MY_RP MY_BP 2.43ghijk 1.47l 4.94ab 2.6ghij 5.43a 2.56hij 3.34cdef 4.44a 2.37efg 3.13cdefghij 2.74defg 4.06abcd 2.26fg 3.09defghij 4.11bc 2.17j 3.08cdef 3.06defghij 3.04cdef 3.92abcde 3.38cdef 2.13j 2.35efg 4.12abc 1.85g 2.93efghij 2.49defg 2.79fghij 2.47defg 3.59abcdefg 2.38efg 3.39bcdefgh 2.91defg 3.76abcdef 3.1cdef 4.29ab 1.83g 3.7abcdef 2.36ij 2.76defg 3.44abcdefgh 3.13cdef 4.07abcd 2.28fg Aroma Kidney Bean 7.4E-33 0.92 2.43hijkl 2.63hijkl 2.63ghijkl Aroma Chickpea 2.9E-38 1.20 2.85efgh 2.71efgh 3.73cde 1.79l 2.02jkl 2.47hijkl 3.71cd 4.68b 8.25a 2.18ijkl 3.31cdefgh 3.04defghi 2.27ijkl 3.64cde 2.78efghijk 2.36ijkl 2.93defghij 4.74b 2.72fghijk 3.55cdefg 3.61cdef 2kl 2.27ijkl 3.97bc 4.41bc 5.61b 11.84a 2.85efgh 1.95gh 1.83h 3.02defgh 2.85efgh 3.48cdef 2.51fgh 2.84efgh 2.39fgh 3.02defgh 2.28fgh 2.89efgh 3.03defgh 3.44cdef 3.71cde 3.1defg 2.36fgh 4.17cd Aroma Great Northern Bean 1.8E-09 1.38 4.14bcdef 2.47hij 3.69efghi 2.16j 2.57ghij 2.38ij 3.68efghi 3.17fghij 2.75ghij 3.93cdefg 3.77defgh 6.21a 4.17bcdef 5.29abc 4.58bcde 4.53bcdef 3.58efghi 5.28abc 4.73bcde 4.17bcdef 5.13abcd 5.37ab 3.62efghi 3.43efghij Aroma Pinto Bean 3.2E-13 1.12 2.3ghi 2.98defghi 3.29cdefgh 2.11i 2.69efghi 1.96i 3.02defghi 4.49b 6.69a 2.14i 3.41bcdefg 3.46bcdef 2.23hi 4.28bc 3.05defghi 2.82defghi 3.91bcd 3.49bcdef 2.76efghi 3.66bcde 3.06defghi 2.43fghi 2.96defghi 3.03defghi 3.84abc 3.07cdef 2.01f 4.41ab 4.44a 2.66cdef 3.54abcd 2.83cdef 2.14ef 2.78cdef 2.95cdef 2.13ef 3.31abcde 3.64abc 2.31def 3.16bcdef 3.29abcde 3.21abcdef 3.26abcdef 3.34abcde 2.64cdef 3.51abcd 3.14cdef 2.33def Modality: Aroma-by-mouth Sample p-value LSD value Flavor Mushroom 7.2E-10 1.27 Flavor Tofu 2.9E-49 0.95 Flavor Vegetable 5.7E-11 1.10 Flavor Kidney Bean 5.5E-26 0.82 Flavor Chickpea 9.6E-56 0.91 Flavor Pinto Bean 2.6E-10 1.02 Flavor Great Northern Bean 8.0E-09 1.20 N_NRP N_RP N_BP CHKP_NRP 3.49defghi 4.96abc 3.56defghi 2.94ghi 3.67bc 2.57defghi 1.65ij 8.58a 4.16ab 2.43efgh 1.67hi 1.8ghi 2.14efgh 1.67gh 2.13efgh 1.54h 3.01defg 2.66efghi 1.96ij 4.66c 1.43gh 2.47cdef 1.65fgh 1.23h 4.3abc 3.22cdefg 3.41bcdef 2.07g 107 Table 4.2 (cont’d) CHKP_RP CHKP_BP CR_NRP CR_RP CR_BP GN_NRP GN_RP GN_BP O_NRP O_RP O_BP WK_NRP WK_RP WK_BP MN_NRP MN_RP MN_BP MY_NRP MY_RP MY_BP 2.99ghi 3.46efghi 5.43a 5.08ab 3.31fghi 4.13bcdefg 3.79cdefgh 2.36i 4.38abcdef 3.39fghi 2.37i 5.11ab 4.37abcdef 2.62hi 4.76abcd 5.21ab 2.84hi 4.7abcde 5.38ab 3.25fghi 9.12a 3.65bc 2.75cdefgh 2.29efghi 1.99ghij 3.49cd 2.32efghi 1.86hij 4.45b 2.94cdefg 1.87hij 3.08cdef 2.76cdefgh 2.54defghi 3.49cd 3.37cd 2.16fghij 3.15cde 2.06ghij 1.24j 1.31i 1.83ghi 4.14ab 2.48efgh 2.16fghi 3.27bcde 2.58defgh 2.02ghi 3.67abcd 2.43efgh 1.92ghi 4.23ab 2.79defg 2.5efgh 3.95abc 3.24bcdef 2.32efghi 4.37a 3.21bcdef 2.89cdefg 2.09efgh 2.2efgh 3.64bc 4.1b 6.28a 2.06efgh 2.49defg 2.48defg 1.97efgh 2.64def 2.46defg 2.47defg 2.77de 4.11b 1.89fgh 3.84bc 3.23cd 2.17efgh 2.73de 3.03cd 6.32b 11.45a 2.58efghi 1.44j 2.12ghij 2.79efghi 2.19ghij 2.9defgh 2.11ghij 2.26ghij 2.27ghij 2.57efghi 2.43fghi 2.85defghi 2.76efghi 3.21def 3.76cd 2.86defghi 2.06hij 3.35de 2.34cdefg 1.7fgh 2.24defgh 3.28bc 4.82a 1.55fgh 2.56bcdef 2.34cdefg 1.68fgh 2.73bcde 2.44cdefg 2.24defg 2.84bcd 3.31bc 1.77efgh 3.51b 1.81efgh 1.82efgh 2.39cdefg 1.67fgh 2.27fg 2.06g 4.72a 2.99defg 2.69efg 3.92abcd 4.11abcd 4.41abc 3.84abcde 4.69a 4.36abc 4.74a 4.38abc 3.99abcd 4.86a 4.16abcd 4.63a 4.69a 4.61ab 4.62a 108 Figure 4.3: Principal component analysis biplot to visualize the effect of cultivar and processing treatments on significant sensory attributes (p < 0.05) of pulse samples in boiled pulse (BP), non- roasted porridge (NRP) and roasted porridge (RP) represented by squares (■), circles (●), and triangles (▲) respectively, across eight pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN). Hierarchical cluster analysis assigned colors and grouped samples into clusters with shared sensory profiles. Effect of processing Hierarchical cluster analysis (HCA) highlighted the distinct clustering of pulse samples based on processing treatments. PC1, PC2, and PC3 accounted for 31.4%, 21.3%, and 17% of the variance respectively in principal component analysis (PCA) (Figure 4.3). Boiled pulses (BP) were characterized by kidney and pinto bean-like odors and flavors, roasted porridge (RP) by great northern bean-like odors and flavors, and non-roasted porridge (NRP) by vegetative/green and mushroom/earthy/musty flavors. BP samples from white-colored beans (e.g., Navy, Great Northern, Otebo, White Kidney) and yellow-colored beans (e.g., Manteca, Mayacoba) (Figure 4.1) 109 formed cluster 4 in quadrant 3 associated with kidney- and pinto-bean-like odors and flavors. The panelists characterized the beany notes of great northern bean-like odor and flavor as sour beany while the pinto bean-like odor and flavor were described as sweet and beany. The "beany" odor and flavor ratings for these samples could have stemmed from their closer resemblance to canned bean references provided during the sensory evaluation. These references could have cued visual differences in panelists’ perception, although, it also could be that boiling resulted in aroma profiles more similar to canned bean references. Previous literature has also characterized boiled beans with beany odor and flavor along with earthy, vegetative notes (Bassett et al., 2021; Koehler et al., 1987; Mkanda et al., 2007). Interestingly, despite NRP and RP samples being visually indistinguishable, PCA revealed a distinct separation between them, confirming that roasting significantly altered the sensory profile of both white and yellow beans. In this study, PCA results revealed that NRP samples of all pulses except Chickpea were strongly associated with “known off-flavors” including vegetative/green (Troszyńska et al., 2011) and mushroom/earthy/musty flavors (Vara-Ubol et al., 2004). In contrast, RP samples of white-colored and Manteca beans were rated higher for "beany odor and flavor" attributes, such as canned great northern bean-like characteristics (Figure 4.3, Table 4.1). This suggests that roasting effectively reduces “known off-flavors” such as vegetative/green and mushroom/earthy/musty flavors but simultaneously increases some beany attributes. Previous research supports the potential of pre-treatment methods to mitigate off-flavors in pulses before their transformation into food ingredients. For example, Young et al., (2020) demonstrated that roasting peas prior to milling and incorporating the flour into bread reduced beany flavors. Similarly, Frohlich et al. (2019) showed that micronizing peas before milling improved bread formulations, while Der (2010) reported similar benefits when micronizing lentil seeds for low-fat beef burgers. These findings highlight the importance of refining pre-treatment strategies such as roasting conditions, including time and temperature, tailored to the specific size and type of pulses, to enhance the flavor profiles of pulse-based products, making them more appealing for diverse food applications. 110 Figure 4.4: Radar plot displaying means of descriptive sensory analysis ratings of Navy non- roasted porridge (black) compared to Navy roasted porridge (dotted black). Asterisks refer to statistically significant change. Effect of cultivar selection Among the eight cultivars studied, Chickpea and Cranberry samples exhibited the most distinct sensory profiles, compared to the white and yellow-colored beans (Figure 4.1) (Figure 4.3). For chickpea, the differences could arise from its classification into a different genera from the rest of the samples—Chickpea (Cicer arietinum) and Common bean (Phaseolus vulgaris), respectively, which could explain their unique sensory characteristics. Panelists rated boiled Cranberry bean samples (cluster 1, quadrant 3) the highest for attributes like dark color (value), dull appearance (saturation), astringent aftertaste, and strong beany odors and flavors, including kidney- and pinto- bean-like notes. Chickpeas demonstrated distinct sensory characteristics across processing treatments (boiled, roasted, and non-roasted), consistently clustering separately from other pulses (Figure 4.4) and stood out for its tofu-like and canned chickpea-like odors and flavors, forming a cluster 5 in quadrant 4 of the PCA (Figure 4.4). 111 Both Chickpea and Cranberry NRP samples received the highest ratings for darkness of appearance and astringent aftertaste among all cultivars during sensory analysis. This astringency may be attributed to their biochemical composition. Non-volatile compounds such as isoflavones, saponins, and phenolics have been associated with bitterness and astringency in soybeans and peas, respectively (Roland et al., 2017). Chickpeas contain phenolic isoflavones, including formononetin and biochanin A, which activate the same bitter receptors as other isoflavones like daidzein and genistein, suggesting they may also impart bitterness (Roland et al., 2011). Additionally, phosphatidylcholine, identified in defatted chickpea flour (Sánchez-Vioque et al., 1998), has been linked to bitterness in soybeans when oxidized (Sessa et al., 1974). This suggests that phosphatidylcholine oxidation in chickpeas may similarly contribute to bitterness. Additionally, dark-colored pigmented pulses, such as Cranberry beans, exhibited the highest total phenolic levels (19.12 mg/g DW) compared to non-pigmented, lighter-colored beans like Navy, Great Northern, Otebo, and White Kidney (Olumide O. Fashakin et al., n.d.) (Figure 4.1). This highlights the distinct flavor and odor profiles of Cranberry bean and Chickpea compared to white- colored beans as in the first row of Figure 4.1, particularly Navy and Great Northern beans, which exhibited milder sensory attributes (Table 4.2). This observation aligns with existing literature, which indicates that lighter-colored beans tend to have milder flavors, making them more versatile for use in food manufacturing. For instance, boiled white-colored beans were characterized as starchy and sweet with shorter cooking times, whereas dark-colored beans exhibited stronger vegetative and earthy intensities (Bassett et al., 2021). Studies further support the acceptability of light-colored beans for use in flour products; for example, a study conducted by Hooper et al. (2023) showed white kidney bean pasta received higher acceptability scores for overall liking and appearance on a 9-point hedonic scale than darker-colored Mayacoba and Black bean pasta prototypes. Winged bean seeds with lighter colors were also noted for their mild, nutty flavor, making them generally more acceptable compared to darker, bitter varieties (Ruberte & Martin, 1979). An interesting finding in our study was observed in the Navy bean. Navy RP exhibited significantly reduced “known off-flavors”, such as mushroom/earthy/musty and vegetative/green/grassy flavors compared to Navy NRP (Chigwedere et al., 2022). Although the great northern bean-like odor was significantly reduced, a small but statistically significant increase in pinto bean-like flavor was also observed in Navy RP compared to Navy NRP (Figure 4.4). These findings highlight the 112 significant impact of cultivar selection on the sensory characteristics of pulse flour. Additionally, promoting the use of light-colored bean flours, such as Navy and Great Northern, due to their closer resemblance to the color of wheat flour and milder flavor intensity could increase their adoption in gluten-free pulse-based products as alternatives to the commonly used Chickpea flour (Sadohara et al., 2022). Figure 4.5: Characterization of pulse flavor via GC-MS and e-nose. A) Principal component analysis (PCA) biplot to visualize the relationship between area under the curve of volatiles 113 Figure 4.5 (cont’d) analyzed by HS-SPME-GC-MS, grouped by chemical class, and pulse samples in boiled pulse (BP), non-roasted porridge (NRP) and roasted porridge (RP) represented by squares (■), circles (●), and triangles (▲) respectively, across eight pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN). Samples sharing similar volatile profiles are clustered together using hierarchical cluster analysis (HCA) and represented by distinct colored clusters. B) PCA biplot to visualize the relationship between peak areas of discriminant ions (DI>0.97), identified through PLS analysis of peak areas from e-nose analysis and mean panel attribute ratings from descriptive sensory analysis, for non-roasted flour (NRF) and roasted flour (RF) samples represented by circles (●) and triangles (▲) respectively, for eight pulse cultivars: N, O, CR, CHKP, MN, MY, WK, GN. HCA grouped samples with similar discriminant ion profiles into distinct colored clusters. Predictive compound identities associated with the discriminant ions are listed in Table S3. Instrumental techniques applied to the study of off-flavors in pulses Volatile compound analysis by HS-SPME-GC-MS Targeted GC-MS analysis identified 32 volatile compounds, including aldehydes (8), alcohols (6), ketones (4), aromatics (6), terpenoids (1), alkanes (1), nitrogen-containing compounds (2), and sulfur-containing compounds (4). In total, 12 key volatile compounds were significantly correlated (p < 0.05) with odor and flavor intensities assessed by DA sensory analysis, were identified, highlighting their critical roles in shaping the sensory profiles of pulses through their associations with “known off-flavors” like vegetative/green, mushroom/ earthy and beany attributes. The identified compounds included (E)-2-hexenal, decanal, benzaldehyde, 1-hexanol, 1-octen-3-ol, 3- methyl butanol, styrene, L-limonene, 2-pentyl furan, naphthalene, 3,5-octadien-2-one, and 6- methyl-5-hepten-2-one. To explore the relationships between volatile profiles of cooked pulse samples (NRP, RP, and BP) across eight cultivars, Principal Component Analysis (PCA) was conducted. Together, PC1 (30.2%), PC2 (22.5%), and PC3 (15%) explained 67.7% of the variance in the volatile peak areas. Processing treatment drove differences in volatile profiles of the cooked samples (NRP, RP, and BP) such that NRP samples clustered predominantly in quadrants I and IV. In contrast, the thermally processed RP and BP samples are clustered in quadrants II and III respectively (Figure 4.5A). NRP samples from Mayacoba and Cranberry cultivars clustered in quadrant I, forming clusters 1 and 5 respectively exhibited higher concentrations of alkanes and ketones, while Chickpea, White Kidney, and Great Northern beans in quadrant IV represented by cluster 4 were associated with aldehydes, alcohols, and aromatics. The NRP samples were mainly characterized by higher 114 concentrations of aldehydes and alcohols. Alcohols such as 1-octen-3-ol (R=0.67), and 3-methyl butanol (R=0.41) were significantly correlated with mushroom/earthy/ musty flavors while 1- hexanol (R=0.61) was significantly correlated with vegetative/green flavor (p<0.05). Similarly, aldehydes including (E)-2-hexenal (R=0.67) and benzaldehyde (R=0.61) were significantly correlated to vegetative/green flavors while decanal (R=0.65), benzaldehyde (R=0.64) and (E)-2- hexenal (R=0.51) were significantly correlated with mushroom/earthy/ musty flavors as observed in descriptive sensory analysis (DA) data (p < 0.05). These findings align with previous studies. For instance, (Vara-Ubol et al., 2004) used descriptive sensory analysis and HS-SPME-GC-MS to demonstrate that low concentrations (1–10 ppm) of hexanol and 2-pentyl furan contributed to musty and earthy notes, while hexanal was strongly associated with green/pea pod aromas. Similarly, Xu et al. (2019) identified hexanal (grassy), (E, E)-2,4-nonadienal (rancid), 1-hexanol (green), 1-octen-3-ol (mushroom), and 2-pentyl furan (green bean) as key markers of beany flavors in germinated lentil flour using HS-SPME-GC-MS/olfactometry. In our study, although significant correlations (p < 0.05) were observed between individual volatiles and sensory attributes among chemical classes, alcohols uniquely exhibited significant correlations (p < 0.05) with both vegetative (R = 0.62) and mushroom/musty (R = 0.50) flavors, as determined by DA sensory analysis. Thus, in less thermally processed NRP samples, alcohol concentration could be the predictive indicator of known off-flavors in pulses. NRP samples across all pulses except Chickpea demonstrated elevated levels of hexanal, hexanol, 1-octen-3-ol, and 2-pentyl furan, which corresponded to stronger intensities of vegetative/green and mushroom/earthy off-flavors in descriptive sensory analysis (Figure 4.3). Roasting significantly (p < 0.05) reduced these volatiles in RP samples, particularly in the Navy cultivar, explaining the lower sensory intensities of vegetative/green and earthy/musty flavors in RP samples compared to NRP samples in Navy (Figure 4.4). Since the NRP samples were characterized by higher concentrations of aldehydes and alcohols, these results highlight the importance of roasting in mitigating known off-flavors by decreasing the concentrations of key volatiles responsible for vegetative/green and mushroom/earthy flavors. Additionally, roasting offers a more scalable, energy-efficient, and nutrient-preserving solution for pulse flour processing compared to boiling. In contrast to NRP samples, the thermally treated RP and BP showed higher levels of terpenoids, sulfurous, and nitrogenous compounds. Specifically, quadrant II predominantly included RP samples from Manteca, Mayacoba, Cranberry, White Kidney, and Great Northern cultivars. These 115 samples exhibited elevated levels of sulfurous compounds such as dimethyl disulfide and methional as well as nitrogenous compounds, which were not detected in NRP samples. Following heat treatment, nitrogenous compounds like 3-butyl-2,5-dimethyl-pyrazine and 2,5-dimethyl- pyrazine increased significantly (p<0.05) in RF and RP samples of Cranberry, White Kidney, Manteca, and Mayacoba cultivars. Alkylpyrazines, which contribute a nutty flavor, are primarily formed through Maillard reactions between amino acids and carbohydrates Shibamoto & Bernhard, (1977) or by the pyrolysis of serine and threonine Baltes & Bochmann, (1987) during thermal treatments. Sulfur compounds and pyrazines, despite their low odor thresholds Landaud et al., (2008); Müller & Rappert, (2010), did not show significant correlations with sensory attributes from DA analysis in this study. This limitation may stem from the targeted approach for GC-MS analysis chosen in this study, which may not have encompassed a broader range of sulfurous and nitrogenous compounds that could potentially contribute to the beany odors observed in sensory evaluations. Expanding the scope of targeted compounds in future analyses or leveraging the untargeted profiling could provide a more comprehensive understanding of the volatile markers contributing to beany odors. Quadrant III, on the other hand, consisted mostly of BP samples from Otebo, Navy, Manteca, Chickpea, Cranberry, and Great Northern cultivars. These samples were primarily associated with terpenoid compounds. The presence of monoterpenes such as α-pinene, β-pinene, sabinene, 3-carene, myrcene, limonene, (Z)-β-ocimene, and (E)-β-ocimene may originate from endogenous isoprenoid biosynthesis or carotenoid degradation, potentially catalyzed by lipoxygenase (LOX) or hydroperoxides. Terpenoids showed significant positive correlations (p<0.05) with chickpea-like, kidney bean-like, and pinto bean-like beany odors and flavors from DA sensory analysis. Terpenoids have been reported to increase after roasting in flours of navy, red kidney bean, and yellow pea (Ma Zhen et al., 2016) and blanching in green peas (Barra et al., 2007; Jakobsen et al., 1998; Oomah et al., 2007). However, these compounds have not been directly linked to producing beany odors and flavors in prior research. In fact, (Y. Liu et al., 2023) reported "fragrant" sensory properties of egg white powder linked to high terpene content, and other studies have suggested that compounds like limonene and linalool could mask unpleasant odors (Ben Salha et al., 2021). This limitation highlights that targeted GC-MS was unable to identify volatiles or chemical classes responsible for beany odors and flavors. In summary, targeted GC-MS analysis identified 12 key flavor compounds that were significantly correlated (p<0.05) with odor and flavor intensities based on DA sensory analysis. The analysis 116 also provided deeper insights into the role of roasting in mitigating aldehydes and alcohols associated with vegetative/green and mushroom/earthy/musty off-flavors, showcasing the method's capability for precise qualitative and quantitative profiling of volatile compounds. Volatile compound analysis by e-nose Flour samples were analyzed using an e-nose to determine whether volatile profiles from raw flour could predict the flavor attributes of cooked pulse products, aimed to streamline product development by identifying key markers directly from raw materials. The e-nose detected 64 major peaks, with 12 peaks identified as discriminant ions through partial least squares regression (PLS) analysis of e-nose data and DA sensory assessments. Retention times of these discriminant ions were converted to Kovats indices (KI), and potential compound profiles were suggested using the (AroChemBase) database. E-nose distinguished the volatile profiles of NRF and RF samples from eight pulse cultivars, explaining 81% of the variance in discriminant ions across the first three principal components (Figure 4.5B). NRF samples clustered in quadrants III and IV (Cluster 4), predominantly representing White Kidney, Mayacoba, Manteca, and Cranberry cultivars. These samples were associated with discriminant ions KI-801, KI-1102, and KI-416, potentially linked to aldehydes and alcohols such as hexanal (leafy), nonanal (sweet), methanol (pungent), and butanol (cheese, sweet, oily, medicinal) odors (AroChemBase) (Table S3). KI-801 and KI-1102 were significantly positively correlated (R = 0.5, p < 0.05) with mushroom/musty flavors identified in DA sensory analysis. However, e-nose did not identify discriminant ions directly correlated with vegetative/green flavors observed in DA results. Conversely, e-nose data aligned well with DA results in identifying markers associated with increased beany odors and flavors after roasting (Figure 4.2, Figure 4.3). RF samples clustered in quadrants I and IV (Cluster 1), primarily including White Kidney and Otebo cultivars. These samples were associated with discriminant ions KI-622, KI-650, and KI-453, tentatively identified as butanals (almond, toasted, malty), furans (beany, sweet, metallic, vegetable), and sulfurous compounds (rotten cabbage, onion) odors (AroChemBase) (Table S3). Similarly, RF samples from Mayacoba, Manteca, and Cranberry cultivars were linked to ions KI-479, KI-597, and KI-699, potentially linked to butanal (almond, toasted, malty), pentanal (nutty, almond), propanal (nutty, earthy), and sulfurous compounds such as propanethiol (rotten cabbage, onion) and dimethyl sulfide (rotten, sulfurous) (AroChemBase) (Table S3). Among these discriminant ions, KI-622, 117 KI-650, KI-479, KI-597, and KI-699 were significantly correlated with toasted odors (R = 0.7) and canned kidney bean- (R = 0.6) and pinto bean-like odors and flavors (R = 0.7) from DA sensory assessments (p < 0.05). These markers, likely linked to butanals (toasted, malty), furans (burnt, sweet), and sulfurous compounds (rotten cabbage, onion), provide insights into volatile compounds contributing to toasted and beany odors and flavors in pulses after roasting. Previous studies have highlighted the role of sulfurous and furan compounds in off-flavors. (Mishra et al., 2019) demonstrated correlation of volatile profile data with descriptive sensory analysis and odor activity values to establish the role of sulfurous compounds, such as methanethiol, diethyl sulfide, dimethyl disulfide, methional, and dimethyl trisulfide, contributing to "cooked kidney beany" aroma, while dimethyl sulfoxide and dimethyl sulfone were associated with sulfurous odors. Furans, commonly formed through Maillard reactions or the thermal degradation of sugars, amino acids, carotenoids, and polyunsaturated fatty acids (PUFAs) like linoleic acid ( Izzotti & Pulliero, 2014; Min et al., 2003). (Sharan et al., 2022; Trindler, Annika Kopf-Bolanz, et al., 2022; C. Wang et al., 2021) identified 2-ethyl furan and 2-pentyl furan as key contributors to earthy, green, and beany notes in peas, faba beans, and soybeans. 118 Figure 4.6: Scatter plot with linear trendline demonstrating the relationship between PCA coordinate distance matrices of descriptive sensory analysis mean ratings (DA) for non-roasted porridge (NRP) and roasted porridge (RP) porridges with PCA coordinate distance matrices of A) discriminant ions from e-nose analysis in non-roasted flour (NRF), roasted flour (RF); B) PCA coordinate distance matrices of volatiles analyzed by HS-SPME-GC-MS in NRF and RF; C) volatiles analyzed by HS-SPME-GC-MS in NRP and, RP. R-values indicate the Pearson’s correlation coefficient between the two variables depicted in the scatter plot. P-value < 0.05 indicates statistical significance in predicting sensory attributes. Comparing GC-MS and e-Nose for rapid off-flavor profiling in pulses An objective of this study was to evaluate the effectiveness of GC-MS and E-nose in predicting off-flavors in pulses, determining which technique offers better potential for rapid profiling. We evaluated how well profile distances for e-nose or GC-MS predicted the degree of sensory difference (Figure 4.6). While descriptive analysis (DA) remains the benchmark for assessing sensory profiles, its reliance on trained panels, extensive sample preparation, and high costs makes it impractical for large-scale or high-throughput evaluations (Shurmer & Gardner, 1992). Instrumental techniques such as GC-MS and e-nose address these limitations by offering efficient, reproducible, and time-saving alternatives for off-flavor profiling. These methods can identify chemical compounds associated with sensory perception, complementing traditional DA approaches. A significant correlation (p= 4.6e-22, R = 0.55) was observed between GC-MS volatile profiles and sensory attributes in cooked pulse products (NRP, RP) (Figure 4.6C). However, its ability to predict sensory characteristics from uncooked pulse flours (NRF, RF) was limited, as evidenced 119 by weaker correlations (p=5.1e-08, R = 0.33, Figure 4.6B). Since GC-MS relies on analyzing individual volatile compounds, it may overlook the complex interactions that contribute to sensory perception particularly, in diverse physical matrices and chemical constituents, further complicated by individual differences in human odor perception. Unlike this approach which relied on targeted GC-MS analysis, the e-nose uses an untargeted methodology, enabling the identification of a broader range of volatile compounds. As shown in Figure 4.6A, the e-nose demonstrated a significant correlation (p= 2.9e-19, R = 0.52) between discriminant ions in uncooked pulse flours (NRF, RF) and sensory data for cooked products (NRP, RP) (Figure 4.6A). The discriminatory ions KI-622, KI-650, KI-453, KI-479, KI-597, and KI-699 identified by e-nose, could serve as a digital fingerprint for beany odors and flavors. This ability to predict beany notes directly from raw pulse flour without cooking makes the e-nose a valuable tool for rapid screening. This can allow breeders and product developers to rapidly identify cultivars or formulations with reduced off-flavors, eliminating the need for extensive sample preparation and cooking. Additionally, e- nose has been reported to facilitate the optimization of processing parameters, such as roasting time and temperature, to minimize the formation of undesirable volatile compounds. Previously Cai et al., (2021) investigated the effects of various roasting time and temperature levels on the physicochemical, sensory, and volatile profiles of soybeans using both e-nose and HS-SPME-GC- MS techniques. Similarly, Asikin et al. (2018) compared the ripening stages of dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa) using HS-SPME-GC-MS and an MS- based E-nose. The results from these studies concluded that HS-SPME-GC-MS identified specific marker compounds providing detailed chemical profiles and insights into the aroma changes. In contrast, the e-nose used discriminant ion masses to generate overall aroma profiles, enabling rapid differentiation between pre-treatment conditions or ripening stages through multivariate analysis. E-nose’s ability to analyze overall aroma profiles highlights its strength in rapid screening and quality control, particularly for industrial applications. Key advantages of e-nose include high sensitivity, rapid analysis times, and ease of use, making it a practical tool for settings far removed from specialized chemical laboratories (Dymerski et al., 2011; Otles, 2016; Van Ruth, 2001). Despite these advantages, GC-MS remains an essential tool for quantifying specific volatile changes and understanding the effects of processing on pulse volatiles, such as those induced by roasting. These approaches offer a robust strategy for optimizing product development and quality 120 control in pulse-based foods, enabling both rapid screening and detailed characterization of volatile profiles. Conclusion This study investigated the sensory characteristics and volatile profiles of eight pulse cultivars to address challenges associated with off-flavors and processing in pulse-based products, while also evaluating the potential of instrumental approaches to predict flavor development. Sensory and volatile differences across cultivars and processing methods were observed using DA, GC-MS, and e-nose. Sensory analysis revealed that cultivars were differentiated primarily based on appearance and seed coat characteristics. The sensory and volatile profiles following processing pre-treatments, such as roasting and boiling, demonstrated shifts in the flavor profiles of treated pulse samples, with some flavors reducing and others intensifying as a result of heat treatment. E- nose successfully captured dynamic changes in key beany flavor markers, aligning with DA findings better than targeted GC-MS, demonstrating its potential as a predictive tool for flavor profiling in pulses. However, the untargeted approach of e-nose may have cast a wider net, detecting broader classes of discriminant ions potentially arising from furans or sulfurs that were underrepresented during targeted GC-MS analysis. Additionally, differences in column polarity between the two instruments could have influenced volatile separation and detection. Finally, since model products (roasted and non-roasted porridges) were not analyzed using e-nose, it is difficult to conclusively determine its superiority over GC-MS in predicting sensory characteristics of finished products. The findings provide a foundational understanding of how cultivar selection, heat processing, and volatile composition influence the sensory quality of pulses. Future research should explore the impact of different milling techniques on flavor profiles. Investigating the effects of alternative pre-treatment methods, such as infrared radiation or optimized roasting, and leveraging e-nose as a rapid screening tool, can help identify processing conditions that enhance sensory quality. Identifying cultivars tailored for specific product applications could significantly improve consumer acceptance. Furthermore, consumer testing is needed to evaluate whether the sensory profile changes resulting from processing are perceived positively or negatively, particularly in the context of targeted food applications like snacks, pastas, or baked goods. This study highlights the complementary roles of GC-MS and e-nose techniques in refining pulse flour flavor profiles. By providing actionable insights into optimizing processing parameters and 121 cultivar selection, these findings contribute to the integration of pulses into diverse food products, promoting their utilization in sustainable food systems and addressing global food security challenges. 122 REFERENCES Akkad, R., Kharraz, E., Han, J., House, J. D., & Curtis, J. M. (2019). Characterisation of the volatile flavour compounds in low and high tannin faba beans (Vicia faba var. minor) grown 285–294. 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Effect of roasting as a premilling treatment on the functional and bread baking properties of whole yellow pea flour. Cereal Chemistry, 97(2), 183–195. 128 Research Summary Chapter 5: Conclusion This research examined how cultivar, processing, and harvest year influence the volatile composition of pulses and how cultivar and processing affect their sensory profiles. The findings provide critical insights into factors affecting off-flavor formation in pulses and offer strategies to improve sensory quality. Using sensory descriptive analysis (DA), headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), and electronic nose (e-nose), this study demonstrated that cultivar and processing treatments significantly shape the composition of volatile organic compounds (VOCs), which directly impact sensory perception. Additionally, this research evaluated the potential of e-nose as a rapid screening tool for detecting off-flavors in pulses and compared its effectiveness to GC-MS in predicting sensory attributes. Hierarchical clustering and principal component analysis revealed that seed coat color and harvest year drove differences in total volatile concentration and volatile composition. These findings highlight the role of cultivar and environmental conditions in shaping pulse flavor. Processing methods such as roasting and boiling altered VOC profiles, with boiling causing the greatest reduction in volatiles. However, roasting is a more practical pre-treatment strategy for pulse flour production due to its energy efficiency, ease in industrial adoption, and nutrient retention compared to boiling. Roasting significantly reduced the concentration of alcohols and aldehydes, but it also increased sulfurous and nitrogenous compounds in the roasted model product compared to non- roasted product (Chapter 3). Studying the sensory profile of pulse flour in a simple matrix like porridge provided valuable insights for incorporating them into various food formulations. DA showed that roasting decreased vegetative/green and earthy/musty/mushroom flavors, which significantly correlated with alcohol and aldehyde volatile markers identified from GC-MS. Roasting increased beany odors and flavors, but GC-MS did not identify specific volatile compounds directly correlated to these sensory attributes. This suggests that either the volatiles responsible for beany notes were not included in the analytical targets, or that complex interactions between compounds contribute to the perceived beany flavor. Among cultivars, dark-colored pulses, such as cranberry beans, had stronger beany odor characteristics, whereas lighter-colored pulses, including navy and great northern beans, exhibited milder sensory profiles especially after roasting (Chapter 4). 129 Instrumental analysis revealed key differences between GC-MS and e-nose in detecting volatiles and predicting flavor. While GC-MS identified key VOCs associated with several key off-flavors, its capacity to predict the intensity of beany notes in samples based on flour or model products VOCs was limited. In contrast, e-nose analysis of flours significantly correlated discriminant ions (potentially associated with furans and sulfurous compounds) with sensory ratings of beany flavors in cooked product, suggesting it can serve as a high-throughput tool for rapid flavor screening without requiring the cooking of large sample sets (Chapter 4). These findings support the integration of multiple analytical techniques to improve the sensory quality of pulse-based products. Future Directions Future research should focus on optimizing roasting and exploring novel pre-treatment methods such as infrared radiation to mitigate the formation of undesirable aroma compounds while preserving the nutritional integrity of pulses. Additionally, developing calibrated models using e- nose to optimize processing conditions based on physical characteristics of pulses including seed coat color and size will help streamline process development. Further studies should also assess how different milling techniques impact the sensory and functional properties of pulses. Breeding milder-flavored pulse cultivars presents an opportunity to enhance sensory quality, making them easier to formulate into various food products. Consumer acceptance studies can identify cultivars for targeted food applications based on their sensory profiles and determine whether the shift in aroma profile from vegetative/green and earthy/musty/mushroom to beany notes due to roasting is perceived positively or negatively. By addressing both genetic factors and processing-related issues, this research can help increase pulse consumption and promote sustainable, plant-based food systems. 130 APPENDIX Figure S1: Effect of thermal processing (roasting; boiling) on the estimated volatile concentration of (A) alcohols; (B): aldehyde; (C): ketone; (D): nitrogenous compound; (E): sulfurous compound 131 Figure S1 (cont’d) of eight cultivars grown in 2022: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), and Great Northern (GN) in non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP) and boiled pulses (BP). Results are the average value from triplicates. For each type of pulse, mean values that do not share a letter are significantly different (p < 0.05) as per the LSD post hoc comparison test. (Chapter 3). 132 Table S1: Estimated concentration in mol/L of volatiles quantified using authentic chemical standards across non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP), and boiled pulses (BP) from the pulse cultivars (Cranberry, Great Northern, Navy, Otebo, White Kidney, Manteca and Mayacoba) grown in harvest year 2023 from Michigan and a market sample of Chickpea obtained commercially (harvested in 2022). These samples were analyzed August through September 2024. Values represent the average of triplicate measurements grouped by chemical class. nd: not detected. Odor descriptions reflect the top three odor notes as reported by The Good Scents Company (2009) (Chapter 3). White colored beans (estimated volatile concentration in mol/L) Great northern beans White Kidney beans NRF RF NRP RP BP NRF RF NRP RP BP Odor Description Compound Name ALDEHYDE 2-Methyl butanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 1.6E-09 2.8E-09 8.9E-09 1.1E-09 nd 3.2E-09 9.3E-09 9.8E-08 2.5E-09 2.4E-10 Hexanal 8.6E-09 7.6E-09 1.7E-08 1.3E-09 4.6E-10 8.0E-09 9.9E-09 1.1E-08 1.4E-09 2.7E-10 2.1E-10 2.4E-10 2.0E-10 1.1E-10 6.7E-10 2.0E-10 3.2E-10 3.0E-10 5.7E-11 4.4E-10 5.5E-09 4.1E-10 1.2E-09 9.8E-11 4.7E-10 3.2E-10 4.8E-11 8.1E-10 4.6E-11 2.3E-10 nd 4.5E-11 6.0E-11 2.8E-11 1.1E-10 1.9E-10 3.7E-10 1.6E-10 1.5E-10 6.7E-10 2.8E-10 5.5E-10 4.5E-10 2.4E-10 1.1E-09 1.7E-08 3.2E-10 1.7E-09 1.9E-10 1.1E-09 1.3E-09 1.2E-10 1.4E-09 9.6E-11 6.2E-10 nd 4.6E-11 1.5E-10 4.9E-11 2.7E-10 7.5E-11 4.9E-11 6.9E-11 5.7E-11 2.5E-11 9.9E-11 1.3E-10 1.5E-10 1.3E-10 2.7E-11 4.3E-10 5.7E-10 1.6E-10 1.0E-10 nd 4.6E-10 1.5E-09 1.1E-10 9.3E-11 3-Methylbutanol 1.1E-09 2.8E-09 6.8E-09 1.6E-08 4.0E-11 7.5E-11 4.1E-09 1.5E-09 6.7E-09 nd nd 1-Pentanol 1-Hexanol 3.3E-10 4.7E-10 4.2E-10 1.8E-10 2.0E-09 6.2E-09 8.1E-10 2.7E-09 1.1E-11 1.1E-11 1.4E-10 1.3E-10 2.8E-10 1.5E-10 1.1E-09 4.0E-09 3.4E-10 1.1E-09 2.2E-11 1.2E-11 1-Octen-3-ol KETONE 1.1E-10 1.4E-10 1.6E-10 9.5E-11 6.6E-12 1.1E-10 1.8E-10 5.4E-10 5.5E-10 1.8E-11 2-Butanone 2-Heptanone 1.1E-08 4.0E-11 1.7E-08 8.1E-11 1.1E-09 9.9E-11 2.7E-10 5.7E-11 nd 3.4E-12 1.5E-08 3.2E-11 3.5E-08 1.1E-10 1.0E-09 7.2E-11 4.9E-10 2.5E-11 nd 5.4E-12 133 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken camphoreous, acetone, fruity sweet, spicy, banana Table S1 (cont’d) 7.0E-12 6-Methyl-5- hepten-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin TERPENOIDS 4.9E-10 6.4E-11 2.4E-11 nd 1.6E-11 2.2E-11 L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine 6.7E-11 nd 1.3E-11 5.9E-11 1.7E-11 1.9E-13 2.2E-12 2.3E-11 1.3E-10 1.8E-15 4.6E-11 5.7E-12 musty, banana, fruity 4.1E-10 8.9E-11 4.4E-11 nd 4.9E-10 1.5E-11 7.1E-12 nd 5.2E-11 nd 8.1E-12 nd 1.2E-10 5.7E-12 nd nd 4.5E-10 3.5E-11 2.6E-11 nd 1.4E-09 5.1E-11 4.0E-11 nd 3.5E-10 nd nd nd 9.8E-11 nd nd nd 1.5E-10 malty, cocoa, nutty geranium 1.6E-11 sweet, plastic, floral nd musty, earthy, fresh nd 9.7E-12 3.0E-12 1.7E-12 nd 1.2E-11 1.8E-10 2.4E-12 3.9E-12 nd camphoreous, herbal, terpenic 2.9E-11 8.1E-12 7.1E-12 5.5E-12 2.0E-11 2.6E-11 1.8E-11 5.7E-12 2.5E-12 unknown 3.9E-10 1.8E-12 6.5E-11 9.2E-13 2.7E-10 2.2E-11 nd nd nd nd 3.7E-10 5.9E-12 4.0E-11 nd 1.1E-09 1.1E-11 3.2E-11 1.6E-12 vegetable, onion, cabbage cabbage, pungent 1.9E-11 1.4E-13 3.6E-11 nd nd 6.1E-11 nd 2.6E-11 nd nutty, peanut, musty White colored beans (estimated volatile concentration in mol/L) Navy bean Otebo NRF RF NRP RP BP NRF RF NRP RP BP Odor Description 1.3E-09 3.8E-09 2.3E-10 2.6E-10 6.7E-10 8.5E-10 1.8E-09 7.3E-08 2.0E-09 4.2E-10 Hexanal 4.1E-09 3.7E-09 3.6E-09 1.3E-09 9.2E-10 2.5E-09 5.7E-09 1.1E-08 2.9E-09 7.2E-10 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL 1.3E-10 2.2E-10 1.1E-10 8.0E-11 7.6E-10 1.8E-10 4.6E-10 3.1E-10 1.2E-10 7.8E-10 4.5E-09 5.4E-11 2.5E-10 3.6E-11 2.1E-10 1.4E-09 4.4E-11 3.7E-10 3.9E-11 1.2E-10 nd 2.8E-11 1.0E-10 1.9E-11 1.2E-10 1.9E-10 1.7E-10 1.2E-10 2.4E-10 2.0E-09 1.5E-10 4.6E-10 2.1E-10 1.5E-10 1.0E-09 2.6E-09 3.0E-10 7.3E-10 1.0E-10 4.4E-10 9.8E-10 1.0E-10 8.1E-10 6.6E-11 4.3E-10 nd 3.7E-11 5.2E-11 1.3E-11 6.2E-11 1.3E-10 3.9E-11 2.0E-11 1.9E-11 2.8E-11 1.0E-10 1.3E-10 4.4E-11 4.4E-11 1.7E-11 134 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral Compound Name ALDEHYDE 2-Methyl butanal sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken camphoreous, acetone, fruity sweet, spicy, banana Table S1 (cont’d) Butanol 3.9E-10 3.3E-10 nd 2.0E-10 nd 3.8E-10 3.1E-10 2.2E-09 3.6E-10 4.1E-11 3-Methylbutanol 9.5E-11 1.5E-09 1.5E-09 3.4E-09 1.9E-11 3.2E-10 1.6E-09 5.9E-10 6.3E-10 nd 1-Pentanol 1-Hexanol 8.3E-11 7.3E-11 2.4E-10 6.6E-11 4.8E-10 1.1E-09 3.2E-10 5.8E-10 4.3E-11 2.3E-11 4.6E-10 8.5E-10 2.6E-10 1.6E-10 7.6E-10 2.9E-09 3.6E-10 1.1E-09 nd 2.5E-11 1-Octen-3-ol KETONE 7.6E-11 1.7E-10 2.0E-10 2.2E-10 1.1E-11 6.9E-11 1.2E-10 2.7E-10 1.3E-10 6.6E-12 2.1E-11 8.5E-09 1.5E-11 2-Butanone 2-Heptanone 6-Methyl-5- hepten-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin TERPENOIDS 1.2E-10 nd nd nd 1.3E-11 6.2E-12 L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd nd 3.7E-08 4.1E-11 9.5E-09 1.0E-11 2.3E-09 2.9E-11 1.9E-10 5.4E-12 4.3E-09 1.9E-11 1.2E-08 6.2E-11 4.5E-10 4.1E-11 1.0E-09 3.3E-11 nd 4.8E-12 9.3E-11 1.3E-11 1.7E-11 1.0E-12 2.2E-11 1.2E-11 3.8E-11 2.1E-11 2.7E-12 musty, banana, fruity 6.4E-10 2.3E-11 nd nd 3.6E-10 nd nd nd 4.2E-10 nd nd nd 1.6E-11 nd nd nd 2.1E-10 5.5E-11 nd nd 4.5E-10 5.0E-11 2.4E-11 nd 2.3E-10 1.3E-11 nd nd 1.5E-10 nd nd nd 1.2E-11 malty, cocoa, nutty geranium 3.4E-11 sweet, plastic, floral 3.6E-12 musty, earthy, fresh nd 2.0E-11 1.6E-11 nd nd nd nd nd 1.7E-11 4.0E-10 2.2E-12 2.8E-12 nd camphoreous, herbal, terpenic 3.5E-12 8.0E-12 2.5E-11 3.8E-12 6.4E-12 6.0E-12 unknown 9.8E-11 1.2E-12 2.0E-11 nd 3.9E-09 nd 2.4E-11 1.1E-12 nd nd 2.8E-10 2.9E-12 5.1E-11 8.0E-13 3.5E-09 8.6E-12 4.0E-11 6.5E-13 vegetable, onion, cabbage cabbage, pungent 2.6E-11 nd nd nd nd 1.7E-11 1.5E-11 1.4E-11 nd nutty, peanut, musty Yellow colored beans (estimated volatile concentration in mol/L) Manteca beans Mayacoba beans Compound Name ALDEHYDE NRF RF NRP RP BP NRF RF NRP RP BP Odor Description 135 Table S1 (cont’d) 2-Methyl butanal 1.8E-09 5.7E-09 1.6E-08 6.2E-09 4.0E-10 1.0E-09 2.7E-09 4.5E-08 2.7E-09 1.3E-10 Hexanal 4.5E-09 1.1E-08 1.7E-08 2.6E-09 6.7E-10 4.0E-09 5.8E-09 5.3E-09 5.9E-10 2.9E-10 2.6E-10 2.2E-10 1.3E-10 8.5E-11 9.5E-10 4.0E-10 6.0E-10 3.6E-10 6.5E-11 6.6E-10 1.8E-08 7.1E-10 1.0E-09 1.6E-10 7.0E-10 4.1E-09 2.3E-10 1.2E-09 7.6E-11 4.9E-10 1.7E-10 5.0E-11 4.2E-10 3.5E-11 2.7E-10 1.8E-10 1.3E-10 2.3E-10 5.7E-11 3.7E-10 2.0E-10 3.3E-10 4.1E-10 1.7E-10 1.0E-09 7.4E-09 1.3E-10 5.8E-10 6.5E-11 3.5E-10 1.2E-09 7.6E-11 7.4E-10 7.5E-11 5.4E-10 8.2E-11 2.7E-11 2.4E-10 1.6E-11 1.0E-10 1.2E-10 4.3E-11 1.1E-10 5.1E-11 3.4E-11 2.1E-11 5.2E-11 5.8E-11 1.0E-10 2.8E-11 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 1.2E-10 8.1E-10 7.2E-11 6.2E-11 3-Methylbutanol 1.3E-10 2.9E-09 3.0E-09 4.8E-09 nd nd 2.7E-10 5.8E-10 2.7E-12 1.8E-10 3.9E-10 1.6E-09 4.2E-10 2.2E-09 nd nd 1-Pentanol 1-Hexanol 1.1E-10 2.6E-10 5.2E-10 1.7E-10 1.1E-09 3.6E-09 4.2E-10 1.9E-09 nd 4.1E-11 8.4E-11 1.0E-10 1.9E-10 6.5E-11 2.0E-10 1.2E-09 1.9E-10 7.1E-10 nd 1.6E-11 1-Octen-3-ol KETONE 6.3E-11 1.9E-10 3.5E-10 2.9E-10 2.9E-11 7.9E-11 1.4E-10 3.7E-10 3.0E-10 2.4E-11 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken camphoreous, acetone, fruity sweet, spicy, banana 3.1E-11 8.6E-09 2.3E-11 2-Butanone 2-Heptanone 6-Methyl-5- hepten-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin TERPENOIDS 2.9E-10 4.2E-11 3.3E-11 nd 2.5E-08 7.6E-11 8.0E-10 4.5E-11 5.0E-09 2.8E-11 2.4E-10 7.4E-12 1.4E-08 1.6E-11 4.1E-08 4.2E-11 4.2E-10 2.4E-11 7.9E-10 2.1E-11 nd 5.6E-12 3.9E-11 3.1E-11 2.7E-11 2.0E-11 1.3E-11 4.0E-10 1.0E-10 3.3E-11 6.9E-12 musty, banana, fruity 1.4E-09 4.5E-11 2.8E-11 nd 1.0E-09 nd nd nd 2.7E-10 nd nd nd 7.1E-11 9.8E-12 nd nd 2.7E-10 nd 1.5E-11 nd 3.2E-10 2.2E-11 1.9E-11 nd 2.7E-10 1.8E-11 6.0E-12 nd 5.7E-11 nd 6.1E-12 nd 4.0E-11 malty, cocoa, nutty geranium 9.5E-12 sweet, plastic, floral nd musty, earthy, fresh nd L-limonene ALKANES Decane 2.1E-11 1.2E-11 1.5E-12 1.2E-12 2.2E-12 1.0E-11 1.1E-10 6.8E-12 2.4E-12 nd camphoreous, herbal, terpenic 2.6E-11 2.0E-11 7.2E-12 3.6E-12 3.5E-12 2.0E-11 2.6E-11 7.2E-12 5.7E-12 6.6E-12 unknown 136 Compound Name ALDEHYDE 2-Methyl butanal (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol Table S1 (cont’d) SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine nd nd nd 4.3E-10 3.6E-12 1.5E-10 1.3E-12 1.8E-09 7.5E-12 3.7E-10 5.4E-12 nd nd 1.1E-10 nd nd nd 4.2E-09 7.2E-12 1.5E-10 2.0E-12 vegetable, onion, cabbage cabbage, pungent 1.4E-11 nd 1.4E-11 nd Other pulses (estimated volatile concentration in mol/L) 4.1E-12 5.1E-11 nd 3.4E-11 nd nutty, peanut, musty Chickpea 2022b Cranberry beans NRF RF NRP RP BP NRF RF NRP RP BP Odor Description 8.5E-10 1.3E-09 1.9E-11 1.1E-08 7.0E-10 2.1E-09 2.2E-09 3.0E-10 1.3E-09 1.9E-10 Hexanal 2.5E-10 5.3E-10 5.5E-08 6.4E-08 1.7E-09 7.9E-09 5.2E-09 6.3E-09 2.3E-09 4.9E-10 6.5E-11 4.7E-10 1.3E-10 1.3E-11 1.1E-10 nd 4.8E-11 1.7E-10 3.7E-11 2.1E-10 6.7E-10 7.6E-10 4.3E-10 1.5E-10 1.2E-09 7.3E-10 1.4E-09 5.9E-10 4.0E-10 2.6E-09 nd 1.1E-10 2.2E-10 8.8E-11 1.0E-09 1.3E-10 1.6E-10 1.7E-10 3.3E-11 2.5E-10 1.7E-10 3.1E-10 2.4E-10 6.2E-11 5.1E-10 7.8E-09 1.8E-10 8.2E-10 1.0E-10 4.9E-10 8.7E-10 1.5E-10 8.1E-10 8.7E-11 6.2E-10 nd 4.9E-11 1.2E-10 3.7E-11 1.9E-10 2.5E-11 7.0E-11 9.2E-11 1.5E-10 3.4E-11 nd 5.7E-11 1.3E-10 8.4E-11 2.6E-11 5.1E-10 1.4E-09 4.5E-11 2.4E-10 nd 5.2E-10 4.4E-10 7.5E-11 1.2E-10 3-Methylbutanol 3.8E-10 9.8E-10 1.1E-08 1.3E-08 2.3E-10 5.6E-10 8.0E-10 5.2E-09 7.8E-09 nd nd 1-Pentanol 1-Hexanol 1.6E-09 4.2E-09 2.9E-09 7.8E-09 5.8E-09 3.8E-09 4.6E-09 4.7E-09 3.7E-11 4.8E-11 2.0E-10 5.0E-11 2.3E-10 1.2E-10 4.6E-10 1.8E-09 7.2E-10 1.1E-09 nd 1.2E-11 1-Octen-3-ol KETONE 1.3E-10 2.4E-11 6.3E-10 1.0E-09 2.2E-11 7.5E-11 9.9E-11 4.2E-10 3.6E-10 1.4E-11 2-Butanone 2-Heptanone 8.6E-09 nd 8.5E-09 5.8E-11 1.7E-10 2.6E-10 5.5E-10 3.8E-10 nd 1.3E-11 2.4E-08 1.4E-11 1.4E-08 4.2E-11 9.0E-10 3.1E-11 8.0E-10 2.1E-11 2.7E-10 6.1E-12 137 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken camphoreous, acetone, fruity sweet, spicy, banana Table S1 (cont’d) 1.0E-11 6-Methyl-5- hepten-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin TERPENOIDS 1.4E-10 nd nd nd 1.1E-11 1.1E-11 L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional NITROGEN COMPOUNDS 2,5-Dimethyl pyrazine 2.9E-11 nd nd 3.1E-11 2.2E-11 2.3E-11 3.3E-12 1.1E-11 nd 4.1E-11 3.6E-11 7.6E-12 musty, banana, fruity 2.5E-10 4.7E-11 2.0E-11 nd 1.5E-10 1.9E-11 2.4E-11 nd 6.9E-10 2.1E-11 1.7E-11 nd 7.3E-11 nd nd nd 7.0E-10 nd nd nd 3.1E-10 4.2E-11 1.4E-11 nd 3.5E-10 nd nd nd 1.7E-10 nd nd nd 1.1E-10 malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh nd nd nd 3.1E-11 4.9E-12 6.8E-12 nd 5.7E-12 1.3E-11 2.9E-12 3.1E-12 nd camphoreous, herbal, terpenic 2.6E-11 1.7E-11 3.8E-11 3.6E-12 4.0E-11 2.6E-11 4.0E-12 5.0E-12 3.8E-12 unknown 1.9E-11 nd nd nd 3.1E-11 nd 1.5E-11 2.9E-13 1.2E-10 nd 5.0E-11 2.2E-12 1.7E-11 5.3E-12 3.5E-09 1.3E-11 3.7E-11 6.5E-13 vegetable, onion, cabbage cabbage, pungent nd 7.1E-12 8.4E-12 nd nd 8.1E-12 nd 6.2E-12 nd nutty, peanut, musty 138 Table S2: Average peak areas of volatiles quantified using means of triplicate measurements from area under the curve and reported for a single m/z (mass-to-charge ratio) using the respective unique mass of volatiles grouped by chemical class across non-roasted flour (NRF), non-roasted porridge (NRP), roasted flour (RF), roasted porridge (RP), and boiled pulses (BP) from the pulse cultivars- Cranberry, Great Northern, Navy, Otebo, White Kidney, Manteca and Mayacoba grown in harvest years 2022 and 2023 from Michigan and a market sample of Chickpea obtained commercially (harvested in 2022). *2022a: analyzed in April 2024; *2022b: analyzed in September 2024. Volatiles annotated as MS, NIST: compared mass spectrum with National Institute of Standards and Technology (NIST) mass spectra library database (V.05); RT, STD: compared retention time and spectrum of identified compound with those of an authentic compound. by comparisons with the National Institute of Standards and Technology (NIST) mass spectra library database (V.05) and/or by matching retention times of authenticated standards, nd: not detected. Odor descriptions reflect the top three odor notes as reported by (The Good Scents Company 2009) (Chapter 3). Chickpea cv. ‘Sierra’ (2022a ) Average Area Counts Compound Name ALDEHYDE NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 57980 414889 546041 nd 34004 Hexanal 687411 1428640 17152259 16002248 891104 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol nd nd 365744 32511 140263 28312 163276 129553 nd 132085 1926040 256267 1143847 154494 358959 264241 88064 126462 396589 102541 485943 51606 nd nd 77540 132464 349750 102915 501537 36514 nd nd 1085834 11044734 2612451 18362955 1207651 408313 1110644 451674 nd 62000 852029 144297 405945 64847 nd 185233 119298 330037 1-Octen-3-ol 436572 763191 1265637 846175 273829 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken 57 57 55 70 77 44 57 41 31 42 31 56 57 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD 139 Table S2 (cont’d) Maltol KETONE 12077 319546 nd nd 40925 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Naphthalene 63489 60496 1431 47960 133628 76080 6527 nd 98919 510591 262756 123043 166099 832930 157882 27986 nd 155721 nd 119953 nd 24386 150063 nd nd nd 16911 nd 163355 nd 24711 133044 nd nd nd 21777 nd 71242 7971 nd 159925 nd nd nd nd 912195 1153821 1263940 1105033 883548 sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic unknown vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 MS,NIST 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE 8189 13167 4125 nd 35450 nd nd nd nd nd 13910 nd 79055 67203 nd 101979 nd nd nd nd 40563 nd nd nd nd nd 18890 nd 36078 nd nd nd nd nd 32782 nd 69970 nd nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Navy cv. ‘Alpena’ (2022) Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 140 Table S2 (cont’d) 2-Methyl butanal 113106 267493 11348 4395 nd Hexanal 1389407 3528105 2641011 355621 224302 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol nd 134124 318457 304444 603167 59057 117532 nd 41940 124256 nd 299721 765632 549158 1369214 132426 108082 155224 85353 166731 4453950 169401 1443147 107582 444076 85007 nd 144508 521750 6329960 587954 20192 842859 34686 126541 30448 123260 1163442 333857 2738191 nd 30116 285756 42280 181318 34987 36093 nd nd 81589 1-Octen-3-ol 144811 487518 978103 525501 33071 Maltol KETONE 30662 141453 nd 71275 25676 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 120068 37488 11116 74418 149402 59977 27007 nd 28567 276747 83037 38888 139122 357234 71268 25698 nd 59148 12191 85055 nd 268990 3966078 nd nd nd 40786 171437 64895 nd 219101 753359 615655 nd nd 38695 60887 nd 2688 20289 124337 nd nd nd 55765 2-Pentyl furan TERPENOIDS 194374 151859 933425 475306 121657 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 141 Table S2 (cont’d) L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE 12687 3137 nd nd nd nd nd nd nd nd 21833 nd 63703 194028 nd nd nd nd nd nd nd nd camphoreous, herbal, terpenic unknown 210415 nd 10994809 10449 nd nd 1040960 nd 15910 nd 243635 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd Cranberry cv. ‘CR1801-2-2’ (2022) nd 50999 nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 7366 605972 47990 85313 176145 Hexanal 3646 3889775 805634 394220 522771 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 6668 5034 4138 6173 5684 4686 4475 4455 4192 4869 69343 187041 596593 554017 1795543 235197 242529 392259 330206 365127 921232 25166 1138636 49373 234609 44687 nd 638756 639093 634028 72485 26136 670093 90007 712138 61279 nd 103455 nd 168257 197656 18803 1325034 46005 240270 66588 nd 631419 55356 467811 142 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal 57 57 55 70 77 44 57 41 31 42 31 56 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD Table S2 (cont’d) 1-Octen-3-ol 1993 246913 1510088 950002 304784 Maltol KETONE nd 257260 25273 78275 29985 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 63978 nd 6969 122924 3658 9248 2459 nd 48645 716197 144973 485307 100035 473033 181061 145276 nd 57671 8974 158612 21805 249504 918454 51782 nd nd 37872 6334 nd 6269 66106 225732 nd nd nd 34197 15579 55297 nd 27714 409060 93453 32264 nd 23163 233677 149113 614006 281804 556938 vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine nd nd nd nd nd nd nd 3048 84152 nd 21315 3122 nd nd 356675 nd 308985 617060 32931 57031 7277465 nd 2964252 nd nd nd nd nd 152323 123988 36962 47618 15951 camphoreous, herbal, terpenic 136 MS,NIST,RT,STD nd unknown 120469 nd 176395 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Compound Name NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation Great Northern cv. ‘Powderhorn’ (2022) Average Area Counts 143 Table S2 (cont’d) ALDEHYDE 2-Methyl butanal 107960 255189 152485 397480 nd Hexanal 286932 1545905 5619434 310420 114120 (E)-2-hexenals Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 27384 38297 168272 140995 535384 55724 226002 95225 161559 934682 40069 86449 475337 218361 549993 66589 187806 818890 136501 525992 1560337 102039 2596801 91133 295342 73738 3712206 1539601 742175 5251450 310623 20071 1020132 27252 108430 30725 nd 955558 100868 1790015 557763 nd 526200 nd 51410 27487 nd 58845 nd 272575 1-Octen-3-ol 302679 381264 488263 138242 109850 Maltol KETONE nd 190173 nd 34975 22650 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 15642 31521 11900 57375 70990 78154 56635 nd 25298 209978 98735 nd 50846 308425 84683 49280 nd 27943 6423 53243 nd 127339 395844 nd nd nd 14625 102178 nd nd 43395 61564 nd nd nd 15851 nd nd nd 30326 433483 7023 nd nd 164797 2-Pentyl furan TERPENOIDS 115889 163351 157412 97704 369884 144 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST Table S2 (cont’d) L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE 18159 1804 41332 nd nd nd nd nd nd nd nd nd nd nd nd nd 122274 nd 89141 139308 nd nd 21397 nd 7589305 nd 585719 nd nd nd nd nd 4722199 nd nd nd nd nd Manteca cv. ‘Y1608-07’ (2022) nd nd camphoreous, herbal, terpenic unknown vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 641577 995256 175534 236384 65887 Hexanal 8152275 5720436 4406276 1957748 628207 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 192588 458260 2017294 584212 1913750 125114 341673 72552 243126 714820 105363 324769 2326193 635472 2413253 314905 2314312 87698 1826027 140761 506170 99989 1118826 85716 2170174 162961 701670 145333 93873 42888 980601 87930 315002 53855 702868 2792408 1959916 488632 nd 757423 8630 nd 694325 1180427 317468 2959073 102069 1247426 nd 169031 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal 57 57 55 70 77 44 57 41 31 42 31 56 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD 145 Table S2 (cont’d) 1-Octen-3-ol 1797541 594486 1500354 1104172 215479 Maltol KETONE 262435 928096 34242 60912 79463 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 773333 315825 nd 480511 1268046 181182 155883 nd 73870 917975 405274 nd 183886 3322132 94736 29529 nd 82094 9777 66874 37342 184725 681984 80397 nd nd 49073 7672 70534 20012 109142 599096 65433 nd nd 57742 15273 nd nd nd 313949 nd nd nd nd 799041 547490 301726 236278 216342 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine nd nd 15875 nd nd nd 44591 7475 nd 13898 84440 nd 60758 nd nd nd 833995 8624 687294 222452 nd 34574 nd nd nd nd 9873385 nd 117765 nd 108515 nd 294419 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic unknown 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd Mayacoba cv. ‘Y 1802-9-1’ (2022) nd 21276 nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Compound Name NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation Average Area Counts 146 Table S2 (cont’d) ALDEHYDE 2-Methyl butanal 230630 1307858 36863 125937 26684 Hexanal 4529413 7708738 1848296 815824 255453 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 169767 318562 613117 446851 2086547 172584 231560 129003 321763 805099 338680 739587 3590426 1107052 3328626 352404 33770 53215 1717543 52429 239492 41280 561931 1729344 36021 970770 493390 929969 584087 3179344 179054 61231 1263318 87892 548963 123659 95326 220270 69477 749360 nd 27795 747102 55971 193681 23689 91846 111748 47191 728408 1-Octen-3-ol 482459 1149006 2021131 575402 280688 Maltol KETONE nd 766650 19543 72544 37394 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 257447 100681 16081 364292 605985 45247 4948 nd 97465 1609235 421925 nd 619023 2488932 161077 66396 nd 132576 19584 85759 nd 570967 1776885 nd nd nd 46288 10437 52193 nd 118622 137328 1252 nd nd 42867 92954 96645 nd nd 328096 nd nd nd nd 2-Pentyl furan TERPENOIDS 197354 760406 391568 134081 558060 147 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd 322389 24639 362696 70490 nd camphoreous, herbal, terpenic 136 MS,NIST,RT,STD 4604 unknown 3498334 nd 1729830 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST Table S2 (cont’d) L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE 65600 10667 nd 6607 nd 7363 28862 nd 27468 nd 10171 nd nd nd nd nd 439084 nd 353266 1147190 112765 221711 nd nd nd 39892 nd nd Otebo cv. ‘Samurai’ (2022) roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 136764 630763 552012 16013 nd Hexanal 574644 5615175 4418144 1402058 161843 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 56430 53323 142817 102533 286261 46754 97961 98269 102295 253106 229338 712577 2713296 690393 3935318 293342 795204 1131637 1989198 161733 1453781 153462 407815 53490 nd 36545 461710 794733 318322 2745521 314557 65019 1413045 118514 449648 65732 88868 49735 48799 821428 148 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral nd 6028 177428 nd 77595 22515 nd nd nd nd sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal 57 57 55 70 77 44 57 41 31 42 31 56 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD Table S2 (cont’d) 1-Octen-3-ol 135693 630554 326447 159584 nd Maltol KETONE 33921 904566 nd 47085 40810 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 23025 21462 nd 48019 104300 33986 5463 nd 27706 816949 412575 nd 501769 2183831 217909 122899 nd 105847 nd 38879 nd 160480 509727 nd nd nd 19158 4174 nd nd 62965 105915 nd nd nd 20159 3075 nd nd nd 53023 nd nd nd 70899 94119 509603 187385 88493 106333 vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic unknown 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine nd nd 48526 nd nd nd nd nd 9676 41278 442293 11695 426476 877153 nd 199851 nd nd nd nd 5704 nd nd nd 18316 nd 3899889 nd 86511 nd 11780 nd 222485 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic nd nd White kidney cv. ‘WK 1601-1’ (2022) nd nd nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Compound Name NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation Average Area Counts 149 Table S2 (cont’d) ALDEHYDE 2-Methyl butanal 77558 722699 9204506 263943 69772 Hexanal 1607810 2390494 3210856 456766 86759 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 59637 113431 496110 335186 1073351 165507 288984 54600 86307 427561 72253 265367 867235 666273 1587980 227961 268827 480609 106190 338580 3874239 116785 617729 44742 188414 43141 1337687 165298 141015 1213484 152127 26173 1524460 71408 281497 60268 235358 383615 34158 942608 nd nd 513599 28835 125657 nd 221446 116249 46531 1123889 1-Octen-3-ol 246232 193652 771812 1078474 297120 Maltol KETONE 34596 237879 nd 54831 38362 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 97795 64393 22560 139121 453941 382658 151256 nd 51315 298842 138797 25790 54261 1653719 128844 99248 nd 52696 6813 nd nd 89535 436526 nd nd nd nd 13440 67032 10165 62395 408338 52060 nd nd 38453 61729 99634 nd nd 270618 nd nd nd nd 2-Pentyl furan TERPENOIDS 189046 183585 105967 114361 360181 150 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST Table S2 (cont’d) L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE nd nd camphoreous, herbal, terpenic 136 MS,NIST,RT,STD 4540 8039 unknown 28750 nd 14386294 39898 nd nd 32161 nd 280892 nd 199754 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 2713 3216 4484 2055 nd nd nd nd nd nd nd nd 345558 nd 157162 268098 33624 37863 nd nd nd nd Chickpea cv. ‘Sierra’ (2023b) nd 32016 roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 59419 387145 5659 3419779 210922 Hexanal 119250 895377 91852318 108170543 2862079 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 14852 125978 171963 13490 86657 36547 207373 140286 nd 62116 884624 146186 645248 328642 753187 998023 2201817 608859 3656006 431750 814523 1853835 3000758 1572733 7880997 698617 2250763 1372602 71753 16123719 379667 17968514 1459304 10417620 7789526 58907473 15477842 29030667 12323314 35686466 nd 146996 1141425 347284 3013801 162417 nd 328326 98760 360808 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal 57 57 55 70 77 44 57 41 31 42 31 56 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD 151 Table S2 (cont’d) 1-Octen-3-ol 401120 378333 10077140 15981346 355819 Maltol KETONE nd nd 1413402 nd 57812 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Naphthalene 71591 nd 4820 76714 96714 nd nd nd 23092 452816 482128 65250 224076 754410 214320 162664 nd 105110 9244 2191455 46441 778643 441728 84182 192778 nd 62508 29137 3181706 47810 645502 2047561 94730 138832 nd 104647 nd 107716 6977 nd 217345 nd nd nd 22305 551366 1883562 3414641 15348415 913048 2182 28289 4530 6323 nd 5452 92780 59372 134588 12885 unknown vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 34845 1833 50009 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine 16977 nd 36153 nd nd nd 43645 nd 85171 nd nd nd nd nd nd nd 50956 nd 69357 nd 55394 nd 60826 nd 152 Table S2 (cont’d) Compound Name ALDEHYDE Navy cv. ‘Alpena’ (2023) Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 93920 268291 15849 18415 203053 Hexanal 1961877 1792488 1727300 647291 1555866 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 29476 59937 150669 83045 581674 184470 158613 35157 74345 182195 41500 122543 412850 119506 593941 58031 133903 561857 217622 163773 1028453 14383 324497 37135 161203 28693 nd 552651 430769 2823254 316016 11740 483277 40181 89963 28199 80017 1245662 287398 1440020 nd 36564 518842 76585 363440 132051 nd 26768 116362 171952 1-Octen-3-ol 237986 520133 623070 673866 180020 Maltol KETONE nd 94640 nd 28138 36052 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene 70716 39417 10122 112400 84569 nd nd 307757 104144 44365 218944 456487 40939 nd 79093 26180 5998 60748 256108 nd nd 10019 44724 2102 36336 48150 nd nd 19579 73666 8090 60168 298970 nd nd 153 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD Table S2 (cont’d) Geosmin Azulene/Naphthalene 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE nd 19696 nd 26053 nd nd nd 23733 nd nd 164968 144049 134969 66664 321794 musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany 112 128 81 MS,NIST,RT,STD MS,NIST MS,NIST 1201 3965 6171 7928 nd nd nd nd nd nd 57728 2158 161751 282490 42106 33783 nd nd 12053 nd nd nd nd nd 2291747 nd 438757 nd nd camphoreous, herbal, terpenic 136 MS,NIST,RT,STD 12347 unknown 54921 7006 45821 4815 vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd Cranberry cv. ‘CR 2111-1’ (2023) nd nd nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 146368 677176 90837 401006 56531 Hexanal 3815651 8743899 10630073 3868200 829517 28818 43625 226753 34528 188076 nd 187542 398168 1238214 242935 1545982 268395 8677973 236081 4209334 404913 1461520 597140 968374 196207 4154298 341814 1873293 394847 nd 63767 588656 145878 564788 122713 209800 701367 119688 187992 nd sweet, fermented, oily 154 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral 57 57 55 70 77 44 57 41 31 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol Table S2 (cont’d) 3-Methylbutanol 206235 1129021 7327834 10994293 nd 1-Pentanol 1-Hexanol 179607 123723 607290 913088 1244030 13367836 1928231 8160509 nd 93406 1-Octen-3-ol 233650 1583822 6682980 5797704 222684 Maltol KETONE nd 180655 nd 260243 nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 200524 36355 5139 183351 496530 nd nd nd nd 720971 350369 nd 1038233 927825 190419 113057 nd 25779 47567 258833 86197 513067 1037369 nd nd nd 48889 42443 174518 74472 578645 514427 nd nd nd 57263 14442 50638 15912 nd 316492 nd nd nd nd 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine 134637 920855 807391 607253 446810 1100 11680 2676 2869 nd 19309 91701 14176 17767 13639 unknown 84995 4058 202849 93883 vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 70483 nd nd 73902 nd nd 112999 13584 155654 380781 58387 nd 39092 33444 28967 141181 nd nd 7966765 80181 74849 1348363 44759 38798 155 musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic 42 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Table S2 (cont’d) Compound Name ALDEHYDE Great Northern cv. ‘Powderhorn’ (2023) Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 471245 856909 2698366 338557 nd Hexanal 14510778 12726072 28372235 2108937 773718 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 231958 313024 1042122 421006 2029467 355471 223509 414626 1521356 224899 1314958 230282 6106354 537339 5948839 385601 1423558 327035 360380 62300 4129106 180647 690084 270104 681161 1571530 911548 3933239 250785 9518067 164666 22441144 871435 3578848 1117227 1374352 5340355 47364251 2170323 20366093 nd 58219 304886 108720 332458 118547 nd 56043 29647 85631 1-Octen-3-ol 1761842 2265015 2644273 1530794 105891 Maltol KETONE nd 215407 292340 293137 nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin 559994 332439 14615 1217585 1452153 289964 192652 nd 903702 678120 123291 1268645 1216692 403838 358001 nd 58728 823503 36069 966217 1455114 68835 57732 nd nd 28378 4631 11440 355426 25890 nd nd 14548 473487 408 441728 155988 nd 66003 nd 156 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD Table S2 (cont’d) Azulene/Naphthalene 62076 29586 34015 51513 nd 1171249 1225280 747533 365494 306606 dry, resinous, pungent Fruity, green, earthy beany 128 81 MS,NIST MS,NIST 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 14974 8939 2791 1617 nd camphoreous, herbal, terpenic 136 MS,NIST,RT,STD 80044 104754 28907 25333 19625 unknown 152782 nd 55926 51908 91380 nd 896070 11326 448821 830358 134123 58342 147972 5736 26567 70967 1012 15987 621983 135513 365823 457229 257586 33775 nd nd 64225 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Manteca cv. ‘Y 1608-14’ (2023) Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 288323 288429 647470 334780 2845788 572010 446471 777076 1820631 258183 1999023 204935 19847479 921136 5319962 648039 2104420 541346 4583497 303494 6326361 297964 1485481 241218 191242 64634 2126840 138816 807697 162140 187196 179686 1295253 4092564 115071 4205484 98798 6752947 nd nd sweet, fermented, oily musty, vegetable, cocoa 157 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral 57 57 55 70 77 44 57 41 31 42 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD 2-Methyl butanal 553900 1737419 4819722 1876589 121495 Hexanal 7503150 18663458 28234895 4380401 1132061 Table S2 (cont’d) 1-Pentanol 1-Hexanol 298121 1967307 1390146 1287103 3017103 27001678 1118826 14117593 nd 310979 1-Octen-3-ol 1009830 3053472 5592654 4577089 471570 Maltol KETONE nd 43658 nd 57693 nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 452934 194910 64138 987203 877423 190875 271230 nd 86537 1343313 632730 81141 2405194 4092299 201995 226555 nd 48103 42377 374301 65766 2266421 3026965 nd nd nd 59690 265107 231815 56159 1237785 815607 nd nd nd 67008 12918 62034 42012 82686 212755 44336 nd nd 35443 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine nd nd 86743 nd nd nd 965076 22308 486943 718180 97217 64583 997336 1417659 1114061 589801 226918 19609 10792 1369 1104 2059 92106 69487 25654 12700 12577 unknown 337103 7924 75523 148799 4190278 46897 224705 435672 848147 33858 299336 196952 vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST nd nd Mayacoba cv. ‘Y 1802-11-2’ (2023) 102882 78010 29564 nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD 158 Table S2 (cont’d) Compound Name ALDEHYDE Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 71951 190985 13731725 812304 38214 Hexanal 1943143 2797521 8861167 986339 490394 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 40978 34057 297265 58854 280306 30215 107866 144299 75604 253589 46257 87322 533784 174986 796893 76159 234054 577059 172991 160248 8281540 163594 2940451 256636 1044066 272549 4245 589618 544845 8953282 1350182 99760 3788604 295461 1614121 484456 285744 3066170 506886 5345300 92038 34947 1222531 64573 312219 130924 nd nd nd 118152 1-Octen-3-ol 246890 433550 5972118 4860514 391498 Maltol KETONE nd 121347 nd 111626 nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 118486 40855 6283 224910 191283 nd 29902 nd 22208 339317 108415 192454 566611 229858 39758 39056 nd 35154 22383 204585 210247 900971 800368 82768 48946 nd 71850 nd 46937 14413 37268 119035 42979 nd nd 26197 42067 174899 69353 592496 169400 nd 50269 nd 70244 159 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST Table S2 (cont’d) 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine Compound Name ALDEHYDE (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 81 MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 161523 157842 346556 183045 122260 2031 22113 6339 2268 nd Fruity, green, earthy beany camphoreous, herbal, terpenic 9655 12408 25623 20262 23470 unknown nd nd nd nd nd nd 62766 nd 149348 279443 81361 52167 nd nd nd nd nd nd 9508711 45278 137521 434377 335844 12295 110307 nd vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic 247420 111878 nd nd Otebo cv. ‘Samurai’ (2023) roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral 57 57 55 70 77 44 57 41 31 42 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD 211011 225706 611498 926685 6086182 475002 172969 597428 1078370 579625 3003735 608148 2939824 391220 3723408 395199 1323151 205404 1098748 134514 4150822 261727 1297951 209592 nd 48633 263969 51321 185327 81785 606196 449193 491628 2198475 3474829 831763 575816 880104 65721 nd sweet, fermented, oily musty, vegetable, cocoa 160 2-Methyl butanal 258228 533725 22000208 605448 127305 Hexanal 4167707 9645358 17800849 4877927 1217061 Table S2 (cont’d) 1-Pentanol 1-Hexanol 1237593 6454520 694181 1204137 2046180 22236466 961508 8352552 nd 189985 1-Octen-3-ol 1111295 1847874 4268621 2095396 106381 Maltol KETONE nd 472015 nd nd nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 229230 159773 46409 936322 615429 247329 nd nd 126409 625323 517515 26210 868568 1358395 224926 200154 nd 37589 23871 340146 79610 923978 696950 58119 nd nd 63314 54492 272313 43319 479289 452988 nd nd nd 62292 nd 40046 5738 nd 36166 153405 29801 nd nd 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine 714501 983032 476970 440364 285099 16114 366939 2062 2562 nd 28643 88106 13625 22665 21270 unknown 116518 4997 7855031 53961 nd nd 240576 269086 91553 4105 111164 65327 vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic nd nd 1367 nd nd nd 631237 18115 224360 762184 124552 37303 sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent Fruity, green, earthy beany camphoreous, herbal, terpenic 31 56 57 71 72 58 108 95 81 91 104 112 128 81 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 109596 nd White kidney cv. ‘WK 1601-1’ (2023) 100417 nd nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD 161 Table S2 (cont’d) Compound Name ALDEHYDE Average Area Counts NRF RF NRP RP BP Odor description Unique Mass (m/z) Annotation 2-Methyl butanal 980101 2830556 29762620 764076 73372 Hexanal 13507409 16622968 19030729 2363567 462365 (E)-2-hexenal Heptanal Benzaldehyde Octanal Nonanal Decanal ALCOHOL Butanol 3-Methylbutanol 1-Pentanol 1-Hexanol 214434 483883 792017 590368 2016933 468517 726045 105150 367084 999790 311744 711598 2287858 935210 3351046 627321 18634031 415142 8809168 761974 3273328 691314 1425902 161234 7037358 378704 1856843 612755 nd 59951 770733 192083 818245 126165 2310032 5795043 172424 2144094 147230 9417954 nd nd 740840 1135970 2851557 30693176 902191 8176701 57578 89651 1-Octen-3-ol 1706338 2892669 8618808 8901172 290539 Maltol KETONE nd 557840 nd 269580 nd 2-Butanone 2-Heptanone 6-Methyl-5-hepten-2-one 3,5-Octadien-2-one AROMATIC COMPOUNDS 2-Ethylfuran o-Xylene Styrene Geosmin Azulene/Naphthalene 783225 263549 47903 910734 1353038 157287 209683 nd 47110 1878721 880920 272487 1576192 4161124 232588 330352 nd 103987 53263 600186 4 2118573 1047131 nd nd nd 77458 nd 45385 11967 nd 434440 71014 nd nd nd 26163 207648 96223 800942 293805 nd nd nd 93382 162 malty, musty, fermented vegetable, aldehydic, clean sweet, vegetable, bitter almond aldehydic, fatty, herbal sweet, cherry, nutty aldehydic, fatty, herbal aldehydic, fatty, rose sweet, aldehydic, floral sweet, fermented, oily musty, vegetable, cocoa sweet, fermented, yeasty sweet, pungent, herbal vegetable, mushroom, chicken sweet, cotton candy, caramellic camphoreous, acetone, fruity sweet, spicy, banana musty, banana, fruity fruity, green, grassy malty, cocoa, nutty geranium sweet, plastic, floral musty, earthy, fresh dry, resinous, pungent 57 57 55 70 77 44 57 41 31 42 31 56 57 71 72 58 108 95 81 91 104 112 128 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST Table S2 (cont’d) 2-Pentyl furan TERPENOIDS L-limonene ALKANES Decane SULFUR COMPOUNDS Dimethyl Disulfide Methional Methanethiol 1-(Methylthio)-propane NITROGEN COMPOUNDS 2-Butyl-3,5-dimethyl pyrazine 2,5-Dimethyl pyrazine 928363 971437 677011 444757 554267 10776 164140 2247 3598 nd Fruity, green, earthy beany camphoreous, herbal, terpenic 70840 91904 65388 20317 8766 unknown 81 MS,NIST 136 MS,NIST,RT,STD 71 94 48 48 61 MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST,RT,STD MS,NIST MS,NIST 73026 10267 232405 65359 vegetable, onion, cabbage cabbage, pungent vegetable, sulfurous, eggy garlic, acidic nd nd roasted, nut flavor nutty, peanut, musty 122 108 MS,NIST MS,NIST,RT,STD nd nd 837963 37128 84519 nd 602517 2019627 90758 nd 40087 182613 nd nd 438465 272947 nd nd 2559976 66396 407053 733109 189835 271301 163 Table S3: Discriminant ions (DI) profiled in pulse flour using e-nose. The results shown are the average of triplicate measurements of peak areas from discriminant ions for non-roasted flour (NRF) and roasted flour (RF) of eight pulse cultivars: Navy (N), Otebo (O), Cranberry (CR), Chickpea (CHKP), Manteca (MN), Mayacoba (MY), White Kidney (WK), Great Northern (GN). Potential compound profiles and odors associated with the respective DI were identified using the AroChemBase database (Version 4.6, Toulouse, France) (Chapter 4). DI KI 453 478 N_ NRF N_ RF CHKP_ NRF 12278 19326 9550 CHKP_ RF 19425 CR_ NRF 5394 Area of discriminant peaks O_ RF GN_ NRF O_ NRF GN_ RF CR_ RF WK_ NRF WK_ RF MN_ NRF MN_ RF MY_ NRF MY_ RF 15683 9926 13575 13022 14497 16353 67702 10487 16886 10666 16951 112750 133015 42872 51490 90084 121512 83894 86030 82998 137414 107212 125410 128736 127389 95010 115969 542 11494 14390 8340 13269 9061 13344 8100 9031 9885 11855 15574 12358 12344 14190 11237 12390 596 8186 17927 7681 7992 13450 16493 10171 10600 7718 16889 10829 13604 12225 19642 11209 19731 609 6090 0 0 0 0 0 0 4674 0 1274 0 4237 0 0 0 0 621 650 0 0 7902 4692 5858 2958 8063 1576 7929 3669 10107 4325 9556 2946 11058 2686 7729 8361 0 0 1470 6131 0 11865 0 7697 0 6646 2582 9403 0 6541 659 8126 11405 5688 6879 6841 10614 3276 0 6804 8961 8996 9879 6936 10068 7737 10312 681 698 6584 4921 9877 9183 1431 3894 1311 3255 8900 6520 8537 7102 3044 1908 3491 3973 4117 4260 7121 7600 6668 5094 10766 12142 11033 7244 7266 9721 8898 5627 9767 8913 732 2687 7575 4905 6393 3264 7510 2613 7454 3959 7499 4291 8379 2469 9555 2890 8316 800 855 913 953 991 17898 25758 10419 12102 24796 16261 5330 8713 14535 14709 17381 18531 21000 21332 23965 22591 853 1874 703 947 941 984 526 308 653 598 765 1475 615 1201 1245 1589 6549 12317 4029 7064 6797 6517 569 1381 4367 5186 4565 10632 4693 8567 10094 10994 895 833 1050 1095 759 974 1193 1021 1200 858 789 967 1156 1157 989 1095 13030 16128 5744 8203 9721 16563 6522 6809 11905 12550 11956 11300 9907 14110 11226 16307 1046 4443 4491 1475 2193 4661 4749 2628 2796 4713 4301 4550 4370 4295 4384 4811 4616 1102 8750 12455 1919 3636 7950 11614 5728 6391 6654 8923 11102 5369 6654 9382 8866 10460 164 Profiled compounds identified using AroChemBase V7 database Odor description acetaldehyde (aldehydic, fruity…); methanethiol (sulfurous…) propanal (nutty, earthy...); dimethyl sulfide; 2-methylpropanal ( fruity, malty, toasted…); 1-propanol (alcoholic, ethereal…) butanal (malty, malty, pungent...); 1- propanethiol (cabbage, onion...); 2-Butanone (chocolate, butter, fruity…) ethyl acetate(apple, fruity...); butan-2- one (cheese, sharp…) 2-methylfuran (chocolate, burnt, sweet...); but-2-enal (floral, pungent...) 3-methylbutanal (almond, toasted, malty...); 2-methylbutanal (almond, toasted, malty...); 1- butanol (cheese,strong, sweet, oily, medicinal… ) pent-1-en-3-ol (burnt...); 3-methyl-1- butanol pentanal (nutty, almond...) propanoic acid (soy, rancid, pungent…); pyrazine (roasted, nutty, pungent...); 2- ethyl furan (malty, sweet, burnt…) hexanal (leafy, sharp...); 2- methylpropanoic acid acetate 2-pentanol (beany, fruity, vegetable...); 2,5-dimethylpyrazine (nutty...); ethyl pyrazine; methylthio-propanol (vegetable, cooked potato, ) benzaldehyde (almond...); a-pinene 2-pentylfuran (beany, sweet, metallic, vegetable…); 1-Octen-3-ol (earthy, fatty, grassy…); acetophenone (almond, cheese, musty, sweet...); 2-octenal (walnut, earthy...); limonene 2-isopropyl-3-methoxypyrazine (pea, beany...); tetramethylpyrazine (nutty, burnt...); n-nonanal (sweet...)