THE EFFECT OF KITCHEN - SCALE PRODUCE PREPARATION TECHNIQUES ON THE RISK OF LISTERIOSIS IN CANCER PATIENTS B y Carly Gomez A THESIS S ubmitted to , Michigan State University in partial fulfillment of the requirements for the degree of Biosystems Engineering Master o f Science 2020 ABSTRACT THE EFFECT OF KITCHEN - SCALE PRODUCE PREPARATION TECHNIQUES ON THE RISK OF LISTERIOSIS IN CANCER PATIENTS B y Carly Gomez The foodborne pathogen L. monocytogenes generally infects immunocompromised individuals, but cancer patients in particular are infected more frequently, with higher morbidity and mortality . Because of the known risk of L. monocytogenes , and other pathogens, in produce, immunocompromised individuals are often placed on neutropenic diets that ex clude fresh produce. Therefore, t h is study aimed to first evaluate several kitchen - scale treatments as potential interventions to reduce the population of L. monocytogenes in prepared produce ( apples, cucumbers, and celery) , then develop a data - driven risk model for listeriosis in cancer patients who consume ready - to - eat (RTE) salads , as influenced by the kitchen - scale treatments . Surface blanching and su rface blanching followed by peeling w ere the most effective treatment s in both cucumbers ( mean reduction s of 5. 1 and 5. 9 , respectively) and apples ( mean reductions of 3.5 and 4. 2 log cfu /g, respectively) ( P < 0.05). All treatments lacked efficacy for celery, with reductions significantly less ( P < 0.05) than in other products, likely due to considerable inoc ulum internalization. For refrigerated salads with no treatment, the median risk of invasive listeriosis over a period of one chemotherapy cycle was predicted to be at most 5.6 × 10 - 10 . This decreased to 7.3 × 10 - 11 when salad components were surface blanc hed. Results from this study can be used to develop improved risk management strategies and risk communication materials for cancer patients and their caretakers. iii ACKNOWLEDGEMENTS The unique, cross - disciplinary nature of this project would not be possi ble without the guidance and suppor t of my tireless advisor, Dr. B radley Marks. Throughout my many years in his lab, Dr. Marks encouraged me to pursue projects in areas that intrigue me and address pertinent societal needs. This fostered my passion for res earch , and I am grateful for his continued expertise. My committee members, Dr. Jade Mitchell and Dr. El liot Ryser , have also serv ed as significant mentors and improved my abilities in not only risk modeling and microbiology, but also critical thinking. I would also like to thank my lab mates and fellow BAE graduate students , whose assistance and friendship I greatly appreciate. Our lab managers, Mi chael James and Nicole Hall , were a tremendous help throughout all of my e xperiments and always kept things r unning smoothly. Additionally, I would like to acknowledge the Farrall Hall faculty and staff who help to mak e Farrall Hall and the BAE program an excellent place to work and study. Finally, I am incredible grateful for t he love and encouragement of my fam ily and friends , who enthusiastically support all of my endeavor s and make time outside the lab so much fun . This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Progr am under Grant No. DGE - 1848739. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. iv TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ....................... vi LIST OF FIGURES ................................ ................................ ................................ ................... viii CHAPTER 1: INTRODUCTION ................................ ................................ ................................ 1 1.1 The Problem ................................ ................................ ................................ .......................... 1 1.2 Research Gaps ................................ ................................ ................................ ....................... 3 1.3 Goal and Objectives ................................ ................................ ................................ .............. 4 CH APTER 2: LITERATURE REVIEW ................................ ................................ ................... 5 2.1 Pathogen Reduction on Produce ................................ ................................ ........................... 5 2.1.1 Chemical Treatments ................................ ................................ ................................ ..... 5 2.1.2 Flash Boiling/Blanching ................................ ................................ ................................ 6 2.1.3 Water Soaks and Rinses ................................ ................................ ................................ . 6 2.1.4 Physical Processes ................................ ................................ ................................ ......... 6 2.1.5 Fact ors Affecting Pathogen Removal ................................ ................................ ............ 7 2.2 Existing Listeriosis Risk Studies ................................ ................................ .......................... 8 2.2.1 Animal Study Models ................................ ................................ ................................ .... 9 2.2.2 Epidemiological Data Models ................................ ................................ ...................... 10 2.2.3 Alternative Model ................................ ................................ ................................ ........ 12 2.3 Summary ................................ ................................ ................................ ............................. 13 CHAPTER 3: KITCHEN - SCALE TREATMENTS FOR REDUCTION OF L. MONOCYTOGENES IN PREPARED PRODUCE FOR IMMUNOCOMPROMISED POPULATIONS ................................ ................................ ................................ .......................... 14 3.1 Materials and Methods ................................ ................................ ................................ ........ 14 3.1.1 Produce ................................ ................................ ................................ ........................ 14 3.1.2 Utensil and Supply Preparation ................................ ................................ ................... 15 3.1.3 Bacterial Strains ................................ ................................ ................................ ........... 15 3.1.4 Inoculum Preparation ................................ ................................ ................................ ... 15 3.1.5 Dip Inoculation ................................ ................................ ................................ ............ 16 3.1.6 Treatments ................................ ................................ ................................ .................... 16 3.1.7 Additional Experiments ................................ ................................ ............................... 17 3.1.8 Peeling ................................ ................................ ................................ .......................... 17 3.1.9 Post - Treatment Sample Preparation, Recovery, and Enumeration .............................. 18 3.1.10 Statistical Analyses ................................ ................................ ................................ .... 18 3.2 Results and Discussion ................................ ................................ ................................ ....... 19 3.2.1 Inoculation and Microbial Background Populations ................................ ................... 19 3.2.2 Apples ................................ ................................ ................................ .......................... 20 3.2.3 Cucumbers ................................ ................................ ................................ ................... 21 3.2.4 Celery ................................ ................................ ................................ ........................... 22 3.2.5 Overall Treatment Comparisons ................................ ................................ .................. 23 v 3.2.6 Conclusions ................................ ................................ ................................ .................. 26 CHAPTER 4: DEVELOPING A RISK MODEL FOR LISTERIOSIS IN CANCER PATIENTS WHO CONSUM E READY - TO - EAT SALAD ................................ ................... 27 4.1 Materials and Methods ................................ ................................ ................................ ........ 27 4.1.1 Ri sk Modeling Tool ................................ ................................ ................................ ..... 27 4.1.2 Dose - Response Mo deling ................................ ................................ ............................ 28 4.1.3 Exposure Assessment ................................ ................................ ................................ ... 29 4.1.4 Risk Characterization ................................ ................................ ................................ ... 36 4.2 Re sults ................................ ................................ ................................ ................................ . 36 4.2.1 Input Distributions ................................ ................................ ................................ ....... 36 4.2.2 Risk Distributions ................................ ................................ ................................ ........ 41 4.2.3 Sensitivity Analysis ................................ ................................ ................................ ..... 44 4.3 Discussion ................................ ................................ ................................ ........................... 45 4.4 Conclusions ................................ ................................ ................................ ......................... 51 C HAPTER 5: CONCLUSIONS ................................ ................................ ................................ 53 CHAPTER 6: FUTURE WORK ................................ ................................ ............................... 55 APPENDICES ................................ ................................ ................................ ............................. 57 Appen dix A: Produce Preparation Experiment Raw Data ................................ ........................ 58 Appendix B: L. monocytogenes Exposure Dose Summary ................................ ...................... 70 Appendix C: L. monocytogenes Exposure Dose Distributions ................................ ................. 71 Appendix D: L. monocytogenes Exposure Dose and Risk Distributions, Salad Without Lettuce ................................ ................................ ................................ ................................ ................... 77 REFERENC ES ................................ ................................ ................................ ............................ 79 vi LIST OF TABLES Table 1: Initial populations of L. monocytogenes on inoculated products ................................ ... 19 Table 2: Reduction of L. monocytogenes on apples, cucumbers, and celery following different kitchen - scale treatments ................................ ................................ ................................ ................ 20 Table 3: Reduction of L. monocytogenes on apples following peeling with standard handheld or apple peelers ................................ ................................ ................................ ................................ .. 21 Table 4: Summary of data used to create prevalence and concentration distribution for leafy greens ................................ ................................ ................................ ................................ ............ 30 Table 5: Summary of literature review for risk management parameters ................................ .... 35 Table 6: Parameters used to model the concentration of L. monocytogenes on tomatoes at retail (cfu/g) ................................ ................................ ................................ ................................ ............ 37 Table 7: Distributions used in risk calculations ................................ ................................ ........... 39 Table 8: Risk per chemotherapy cycle for each risk management strategy and k approach ........ 41 Table 9: Apple sample and dilution weights ................................ ................................ ................ 58 Table 10: Plate counts for apple treatments ................................ ................................ ................. 59 Table 11: Cucumber sample and dilution weights ................................ ................................ ....... 60 Table 12: Plate counts for cucumber treatments ................................ ................................ .......... 61 Table 13: Celery sample and dilution weights ................................ ................................ ............. 62 Table 14: Plate counts for celery treatments ................................ ................................ ................ 63 Table 15: Celery inoculum internalization experiment sample weights and dilutions ................ 64 Table 16: Plate co unts for celery inoculum internalization experiment ................................ ...... 66 Table 17: Apple peeling exp eriment sample weights and dilutions ................................ ............ 68 Table 18: Apple peeling experiment plate counts ................................ ................................ ........ 69 Table 19: Daily exposure dose (cfu/g) for L. monocytogenes in fresh salad ............................... 70 Table 20: Daily exposure dose (cfu) for L. monocytogenes in cucumber and tomato salad ....... 77 vii Table 2 1: Risk per chemotherapy cycle for eac h risk management strategy and k approach, cucumber and tomato salad ................................ ................................ ................................ ........... 78 vi ii LIST OF FIGURES Figure 1: Kitchen - scale pathogen reduction treatments ................................ ................................ 5 Figure 2: L. monocytogenes population and distance from inoculated celery end in inoculum uptake experiment . ................................ ................................ ................................ ........................ 22 Figure 3: Components of exposure assessment ................................ ................................ ........... 31 Figure 4: Histograms of risk distribution for refrigerated control salads, calculated using k approaches 1 (a) and 2 (b) ................................ ................................ ................................ ............. 42 Figure 5: Box and whisker plots of risk distributions for each storage and treatment scenario, calculated using k approach 2. ................................ ................................ ................................ ...... 43 Figure 6: Spearman rank correlation coeff icients for refrigerated control (a) and bla nched (b) salads, calculated using k approach 2 ................................ ................................ ........................... 44 Figure 7: Exposure dose histogram for refrigerated, untreated salad ................................ .......... 71 Figure 8: Exposure dose histogram for elevated temperature, untreated salad ........................... 71 Figure 9: Exposure dose histogram for refrigerated salad treated with sanitizer soak ................ 72 Figure 10: Exposure dose histogram for el evated temperature salad treated with sanitizer soak ................................ ................................ ................................ ................................ ....................... 72 Figure 11: Exposure dose histogram for refrigerated salad treated with tap w ater rinse ............ 73 Figure 12: Exposure dose histogram for elevated temperature salad treated with tap water rinse ................................ ................................ ................................ ................................ ....................... 73 Figure 13: Exposure dose histogram for refrigerated salad treated with tap water soak ............. 74 Figure 14: Exposure dose histogram fo r elevated temperature salad t reated with tap water soak ................................ ................................ ................................ ................................ ....................... 74 Figure 15: Expo sure dose histogram for re frigerated salad treated by blanching ....................... 75 Figure 1 6: Exposure dose histogram for elevated temperature salad treated by blanching ........ 75 Figure 17: Exposure dose histogram for refrigerated salad treated by peeling ........................... 76 Figure 18: Exposure dose histogram for elevated temperature salad treated by peeling ............ 76 1 CHAPTER 1: INTRODUCTION 1.1 The Problem The i ncidence of listeriosis has increased in recent years (4, 38, 70, 105) , with Listeria monocytogenes h aving been repeatedly isolated from fresh, ready - to - eat (RTE), and minimally processed produce (1 3, 82, 132, 134, 137, 153, 174, 203) . Fruits and vegetables can become contaminated with L. monocytogenes during preharvest from multiple environment al sources in the field (13, 193 ) . Fresh produce also can be come contaminated with Listeria monocytogenes p ostharvest from contact with contaminated wash water and various surfaces, such as shredders, conveyors , and coolers (6, 147) . Consequently, L. monocytogenes has been implicated in several large outbreaks involving produce, including packaged salads (33) , ready - to - eat (RTE) salad (172) , caramel apples (6) and cantaloupe (122) . In the outbreak linked to cantaloupe, data on concurrent medical conditions were available for 123 victims, 108 of which were immunocompromised in some way (122) . Foodborne o utbreaks also have occurred in hospitals, prompting discussion on food safety and prophylaxis for susceptible populations (69, 85, 91, 117, 160, 163, 197) . Healthy individuals typically de velop a noninvasive form of listeriosis, termed febrile gastroenteritis, wh ich causes self - limiting diarrhea and fever (4, 82, 173, 198) . I mmunocompromised members of the population, including cancer patients, pregnant women, the elderly, those with HIV/AIDS, and those with autoimmune diseases such as lupus develop invasive liste riosis, which manifests with more severe symptoms, such as meningoencephalitis and septicemia, and a high mortality rate of 20 - 40% (4, 35, 38, 58, 77, 82, 168, 173, 198) . Canc er patients are particularly vulnerable (71, 84, 106, 161, 162) , with higher mort ality rates than other groups (77, 78, 162) . The relative risk of listeriosis for cancer patients can be up to 2 1,139 (77) and 17.6 (130) when compared to healthy individuals l ess than 65 years old and individuals with other immunocompromising conditions, r espectively. Cancer patients are markedly more susceptible to listeriosis because their treatment (63) . Chemotherapy inhibits neutrophil genera tion , leading to neutropenia, a state of low neutrophil count in which patients are at an in creased risk of infection (42) . Additionally, acid - neutralizing drugs cause hypochlorhydria (stomach pH > 4 .0 ), leaving patients susceptible to listeriosis and other bacterial infections (11, 39, 85, 120, 178) . Cancer treatment s also invoke inflammatory re sponses and vascular changes that disrupt gastrointestinal tissue (8) . Patients may develop gastrointestinal mucositis - inflammation and ulcers throughout the digestive tract - which creates a pathway for bac teria to translocate from the digestive system to the bloodstream (50, 188, 194) . Finally, cancer patients are frequently treated with broad - spectrum antibiotics (67, 99) , the wide target range of which leads to the death of endogenous gut bacteria, elimin ating natural competition as a defense mechanism (31, 41, 63, 126) . Neutropenic diets (NDs) are a common risk management strategy for foodborne illness in cancer patient s, despite never being proven to reduce rates of infection (46, 83, 103, 129, 180, 189 ) . NDs vary greatly between institutions (20, 27, 68, 164) , but most commonly eliminate the consumption of high - risk foods that are not cooked or pasteurized prior to con sumption, including fresh produce and RTE salad products (83, 166) . This diet remains controversial because these food groups are vital sources of fiber and vitamin C, which support intestinal integrity (76) , reduce bacterial translocation (30, 44, 167) an d improve immune function (5, 26, 72, 107, 116, 124) . Adherence to the diet is notorio usly difficult (83, 103) , and has negative effects on quality of life (123, 128) . Thus, it may be more beneficial to implement food safety 3 strategies that focus on improv ed food handling practices , such as proper storage and kitchen - scale preparation. Howe ver, t he efficacy of such strategies in reducing pathogen levels and illness risk needs to be assessed through experimental testing and subsequent risk analysis. The ris k to human health posed by foodborne pathogens can be assessed using quantitative micr obial risk assessment (QMRA), a process comprised of the following steps: hazard identification, dose - response assessment, exposure assessment, risk characterization , and risk management (79) . The purpose of hazard identifi cation is to describe the pathoge n and its known effects on its host. Dose - response assessment quantifies the probability of a n adverse outcome of interest , based on applied dose. For foodborne pathogens, these models depend on the amount of pathogen consu med, variations in strain virulen ce , and host susceptibility (184) . Exposure assessment determines the amount of pathogens ingested - the f requency at which consumers ingest the foods and the level of contamination on implicated foods. Survival of the path ogen is also considered, for exam ple in various storage conditions and after any pathogen control treatments. E xposure and dose - response assessments are combined in the risk characterization step, in which risk of a designated outcome and associated uncert ainty are estimated. These estima tes are used to guide action decisions in risk management. 1.2 Research Gaps Multiple risk assessments have been developed for listeriosis in generalized low - risk and high - risk populations (12, 49, 59, 88, 111, 145, 150, 152, 181, 186, 195, 199) , but cur rent literature lacks a data - driven model that is specific to cancer patients. Existing dose - response models rely on studies in mice, which may not be applicable to humans (104, 186) , studies in guinea pigs (195) and primates (165) with an endpoint of stil lbirth in pregnant animals, which is 4 not applicable to the target population of cancer patients , and epidemiological data (22, 59, 111, 199) , w hich lack detailed exposure assessments specific to cancer patients . Inst itutional discrepancies in the implemen tation and administration of the neutropenic diet highlight a clear misunderstanding of its purpose in mitigating foodborne illness. T here is a critical need for studies that specifically address the role of the ND in preventing foodborne diseases, as exis ting studies only address general infections, the cause of which are much more difficult to attribute to diet. Data on alternative risk management strategies, such as hyper - hygienic produce preparation methods, are li mited. Several studies have been conduc ted on reduction of L. monocytogenes in produce; unfortunately, many of these studies were focused on large - scale commercial manufacturing practices (2, 18, 36, 64, 121, 131, 139, 140, 142, 149, 169, 202) , which do not translate directly to preparation in healthcare or home settings. In order to inform risk models and develop accurate food safety preparation guidelines , i t is important to quantify the L. monocytogenes reductions that may be achieved using kitchen - scale produce preparation techniques . 1.3 Go al and Objectives Ultimately, the goal of this work is to assess the risk for listeriosis in cancer patients who consume fresh produce prepared by several differing kitchen - scale methods. This will be ac hieved through two main objectives : i) describe L. m onocytogenes survival on fresh produce subjected to hyper - hygienic preparation processes; and ii) develop a risk model for listeriosis in cancer patients who consume fresh produce, in the form of RTE salads , as affected by hyper - hygienic produce preparatio n and modified neutropenic diets . Th ese findings will provide a basis for evidence - based food safety measures in this vulnerable population. 5 CHAPTER 2: LITERATURE REV IEW The literature review performed for this study examined foodborne pathogen reduction t echniques and existing listeriosis risk mo dels . 2.1 Pathogen Reduction o n Produ ce 2.1 .1 Chemical Treatments The treatments that have been studied for kitchen - scale foodborne pathogen reduction can be grouped into five categories: chemical treatments, flash boiling/blanching, water soaks and rinses, organic acid soaks and rinses, and physical removal methods ( Figure 1 ) . Produce soaks and scrubs are often used commercially and have been shown to result in moderate pathogen reductions ; h owever, these surfactants, detergents, and solutions, such as sodium hypochlorite, chlorine, and hydrogen peroxide are not recommended by the FDA for consumer use (2, 18, 64, 142, 169, 185, 2 02) . Recently, fruit and vegetable washes that target microbes, d irt, wax, and pesticides have become available to consumers. Existing studies on such products report contradictory efficacy results (16, 62, 81, 98, 113, 115, 139, 176) , indicating a need fo r further valid a t ion of consumer produce wash es as antimicrobial treatment s . The antimicrobial effects of organic acid soaks (propionic, acetic, lactic, malic, and citric acids) have also been tested in produce, with less to similar efficacy as water soaks and rinses (131, 140, 169) . Unfortunately, the most significant reductions occurred at acid concentrations Figure 1 : Kitchen - scale pathogen reduction treatments 6 greater than 1.0%, at which point smell, taste, and texture may be compromised (131) . Additionally, these approaches may not be ideal for kitchen - scale treatment, as consumers may not have the proper training or e quipment to handle high concentrations of chemicals. 2.1.2 Flash Boiling/Blanching Blanching (flash boiling) , which is frequently used in commercial food process ing to inactivate en zymes and expel gasses, can also have antimicrobial benefits (121) . Blanchi ng of produce can reduce foodborne pathogen populations by as much as 8 log CFU /g (21, 36, 114, 121) . However, conflicting results concerning product integrity have been reported fo llowing treatment (when trying to preserve a functionally raw product); thu s, further work is needed to validate surface blanching treatments aimed specifically at reduc ing bacterial surface contamination (rather than enzyme inactivation). 2.1.3 Water Soak s and Rinses Most consumers and food service personnel report using water t reatments to clean and sanitize their produce (108, 113, 142) , as water washing/ rinsing is the method recommended by the U.S. Food and Drug Administration (FDA) (183, 185) . Additionally, while water soaking treatments may moderately reduce pathogen levels on contaminated product, previously un contaminated products can readily became contaminated from the s ame wash water (90, 141) . Rinsing treatments have shown similar efficacy in removing L. monocytogenes from produce (62, 98) , and do not involve handling p otentially contaminated wash water. 2.1.4 Physical Processes Various p hysical processes also have been investigated as potential microbial reduction strategies for contaminated produce (53) . In general, the frictional forces of rubbing or scrubbing in addi tion to rinsing greatly improve pathogen reduction, but again efficacy depends largely on 7 product surface characteristics (53, 98, 115, 141) . Peeling the surface of firm fruits and vegetables has also been investigated as a means of pathogen reduction and was found to have moderate efficacy, although allowing for considerable survivors (53) . Al though not tested on products inoculated with L. monocytogenes , these results suggest that friction - based treatments can also decrease pathogen populations 2.1.5 Fact ors Affecting Pathogen Removal Storage temperature a ffects the efficacy of pathogen - reduct ion treatments on produce. Nastou et al. (131) reported that water soaking resulted in significant pathogen reductions when lettuce and parsley were stored at 5 ° C and 15 ° C, but not at 30 ° (64) reported that the efficacy of antimicrob ial treatments decreased faster when produce was stored at 8 ° C opposed to 3 ° C. These results demonstrate that proper storage temperatures are crucial to support kitch en - scale treatments. Storage time also influences the effectiveness of pathogen removal tr eatments. Sapers (155) and Lopez et al. (113) explained that increased time between a contamination event and anti microbial treatment increased the resistance of path ogens to remov e from produce. Francis (64) also reported that L. innocua regrew in as few as four days after treatment. L onger storage times can make it more difficult to reduce L. monocytogenes during treatment and can reduce the positive eff ects of the treatment. The morphology of fruits and vegetables is highly vari able, with the calyx, stem , and other structures more conducive to attachment and growth of L. monocytogenes . Kilonzo - Nthenge (98) demonstrated that i n broccoli, higher inoculati on was achieved for the calyx than the stem, and , in apples, water treatments were not as effective in L. innocua removal from the calyx and stem ends compared to the remaining apple surface. Sapers et al. (156) also reported 8 that E. coli cells remain ing o n apples after treatment were clustered in the stem and calyx ends. R emoving these portions of the fruit during preparation may reduce the consumable dose . Some produce types are anti - listerial by nature, whether it be due to the composition of their tissu es or the anta gonistic nature of their native microflora (51) . In 2007, Liao (109) demonstrated that carrots were anti - listerial for both of these reasons. When sliced carrots were dipped into a L. monocytogenes suspension, a decrease of greater than 2 log cfu was obser ved in the suspension . Additionally, when the carrots were sanitized to curtail native microflora, L. monocytogenes growth increased by greater than 2 log cfu , and when carrot native microflora was introduced onto bell peppers (a product with no inherent a ntilisterial activity), L. monocytogenes growth on the peppers was inhibited. Erickson (51) hypothesized that similar characteristics may exist in other types of produce, as certain varieties of cabbage have been shown to facilitate L. monocy togenes growth during storage , and others have cause d a decline in L. monocytogenes population. For these reasons, L. monocytogenes contamination may vary greatly between products, and inoculation levels should be verified prior to treatment testing. Addit ionally, t esting on a wide array of produce types would improve understanding of this characteristic . 2.2 Existing Listeriosis Risk Studies R isk estimates depend heavily on dose - response models , which relate amount of pathogen consumed to the occurrence an d severity of detrimental health outcomes (92) . Because risk resulting from a single exposure is often so low ( e.g., 1 in 10,000) that experiments would require an impractical number of subjects (> 10,000 ), a dose - response model is crucial for estimating r isk at low doses (79) . Response d ata can be collected from either animal studies or epidemiological data and used to derive a model, from which responses at low doses can be 9 extrapolated. In animal studies, the effects of various high pathogen doses are ob served in a surrogate animal having similar pathology to humans. It can be difficult to find such an animal, as animal exposure routes, morbidity, and mortality rates often differ from humans. Epidemiologic al data (cases, illnesses, and deaths) are combine d with consumption and contamination data to attribute recorded cases to an estimate of pathogen dose consumed. These models often include a lot of uncertainty, as estimates are dependent on the quality of case, consumption, and contamination data reported . Existing dose - response models for listeriosis are based on either approach ; both , as well as an alternative approach , will be explored in this review. Exposure assessments are another crucial element of risk analysis, as they determine the dose of pathog en subsequently used in the dose - response model. Unfortunately, current risk studies do not consider the consumption of salad products by cancer patients specifically; however, relevant alternat ives will be examined. 2.2.1 Animal Study Models In 1989, Gol nazarian et al. (73) developed dose - response models for infection and death in healthy and immunocompromised mice that ingested L. monocytogenes suspended in food. These data were used in the 20 03 U.S. Food and Drug Administration (FDA) and Food Safety and Inspection Service (FSIS) risk assessment (186) to establish the dose - response curve. Because L. monocytogenes colonization of mouse organs may not translate directly to invasive listeriosis in humans, death was chosen as the endpoint for this model. The variation in virulence was determined by performing feeding studies in mice with multiple strains of L. monocytogenes isolated from food. However, when the resulting mouse model was implemented with the exhaustive exposure assessment data , the calculated L D 50 (dose that is lethal for 50% of the population) was overestimated by a factor more than 1,000,000 (186) . Consequently, a dose - 10 response scaling factor derived from FoodNet surveillance data was applied to the mouse curve, so that model predictions agree d with public health data. Lecuit et al. (104) suggest ed that mice may not be an adequate surrogate for human listeriosis studies. They note that in humans, E - cadherin (an epithelial surface receptor protein) is a receptor for internalin (an L. monocytogen es surface protein) , which allows L. monocytogenes to translocate through the intestinal epithelium and cause systemic infection. Mouse E - cadherin is not a receptor for internalin, thus preventing translocation of the bacterium through the intestinal epith elium. Because mice cannot model the suspected route of systematic infection in humans, caution is needed when using the data from mice to predict human illness (199) . Since this discovery, other animals have been explored as models for dose - response stud ies. In 2007, Williams et al. (196) determined an effective dose for stillbirths and maternal organ infection in pregnant guinea pigs that consumed whipping cream inoculated with L. monocytogenes . Later, Smith et al. (165) created a dose - response model for stillbirths in orally exposed preg nant rhesus monkeys. These models predicted LD 50 values that were much lower than those predicted by the FDA/FSIS model, and are similar to those predicted by the Food and Agriculture Organization of the United Nations (F AO) and World Health Organization ( WHO ) model (reviewed in the following section). Unfortunately, stillbirth is not the endpoint of interest in this study, so these models are not applicable to cancer patients. 2.2.2 Epidemiological Data Models In 1996, F arber et al. (59) assessed the risk of listeriosis fro m pâté and soft cheese in Canada using a - - response model , which was chosen for its flexibility and capacity to be transformed to other well - known models . Parameters were characterize d based on chosen estimates for ID 10 and ID 90 (doses that will result in illness for 10 and 90% of the 11 population) , that resembled approximate doses that caused illness in outbreaks. The prevalence of L. monocytogenes reported in pâté and soft cheese , comb ined with disappearance data ( food , were used to assess exposure . T his model relied on many generalizations , particularly for exposure , which greatly influences overall risk characterization. Therefor e, a risk model with a more credible exposure assessment and dose - response paramet erization approach would likely be more accurate . Using f ood survey data that quantified L. monocytogenes in smoked fish a long with detailed data on national listeriosis cases , Buchanan et al . (22) developed a conservative dose - response model for listeriosis . This model relied on the following assumptions: i ) all cases of listeriosis were in immunologically high - risk populations and due to consumption of RTE sm oked fish ; ii) an exponential dose - response model can be used for listeriosis ; and iii) only servings with L. monocytogenes populations greater than 4 logs caused illness . These assumptions prevented the model from accounting for differences in p revalence and growth potential between f oods and in susceptibility due to various immunocompromising conditions . Lindqvist and West (111) used an approach with similar assumption s, but with a Weibull - gamma model for high - and low - risk groups , with reported results much more conservative than The s izable difference in estimated risk between the two approaches reveal ed ina dequate dose - response data. In both cases, i mproved information on prevalence, level of contamination, and strain virulence would enhance risk model validity , a s would specific ity to fresh produce and cancer patients for the target application in this stud y. In 2004, FAO/WHO developed an exponential dose - response model with an endpoint of invasive listeriosis (199) using the exposure data from the 2003 FDA/FSIS risk assessment (186) . This dose response model utilized two values of parameter k (the probabili ty that one 12 organism will survive to cause i llness) to account for variations in host susceptibility, corresponding to general high - and low - risk populations. Be ing based on thorough epidemiological data , the dose - response model in this risk assessment is considered highly reliable and has been used in several subsequent risk assessments (88, 145, 150, 152) . However, this risk assessment did not calculate risk due to fresh produce, but instead targeted pa steurized milk, ice cream, fermented meat, and smoked fish. More recently, Pouillot et al. (146) developed specialized exponential listeriosis dose - response models by adjusting the k parameter for host susceptibility and strain virulence. Using strain char acterization from the FDA/FSIS risk assessment (186) , as well as epidemiological data, they created dose - response parameters for 11 population subgroups, including hematological and nonhematological cancer patients. The characterizations of this dose - respo nse parameters are valuable for the current work. 2.2 .3 Alternative Model The most recent listeriosis dose - response model was developed by Rahman et al. (148) . This model utilizes a novel mechanistic approach to more accurately assess risk based on host - pathogen dynamics. S everal different parameters account for host - specific physiological characteristics , such as stomach acid, commensal bacteria in the small i ntestine, and host immune cells. Values for these parameters were derived from a butter outbreak in Finland that primarily affected immunocompromised p atients (blood and organ transplant patients). This is a promising start for a model applicable for cance additional para meters are needed to address specific cancer physiolog ies (hypochlorhydria, 13 impaired neutrophil generatio n). Unfortunately, t his is not currently possible due to the lack of clinical data. 2.3 Summary This literature review identified several important gap s regarding this project. First, most commercial pathogen reduction strategies for produce are not applic able to home or healthcare settings, due to the use of pernicious chemicals. Studies on applicable treatments, such as commercial produce washes and wa ter soaks and rinses, have yielded conflicting results. Other approaches, such as blanching and peeling h ave potential for effective removal but have not been tested on products inoculated with L. monocytogene s . Therefore, the efficacy of these treatments needs to be experimentally assessed on a variety of produce. Risk models are contingent on their dose - res ponse model and exposure assessment, but many existing dose - response models for listeriosis cannot be applied to cancer patients due to their use of co ntroversial animal surrogates (mice), inappropriate endpoints (stillbirth), or susceptibility generalizat ions. Additionally, some risk models rely on exposure assessments based on consumption of other foods (soft cheese, smoked fish), which differs from fr esh produce. One study characterized dose - response parameters for cancer patients but did not conduct a f ull risk assessment. Thus, there is a need for a listeriosis risk model specific to raw produce and cancer patients. This thesis addresses the aforemen tioned literature gaps through systematic testing of kitchen - scale microbial reduction strategies and the development of a listeriosis risk model specific to cancer patients who consume fresh produce. 14 CHAPTER 3: KITCHEN - SCALE TREATMENTS FOR REDUCTION O F L. MONOCYTOGENES IN PREPARED PRODUCE FOR IMMUNOCOMPROMISED POPULATIONS Immunocompromised individuals are typically placed on diets that exclude raw produce due to the expected risk of foodborne pathogens, one of the most dangerous being L. monocytogenes . Pathogen reduction and sanitization treatments may be effective in raw p roduce, but related studies are few , with conflicting results. The goal of this study was to assess the efficacy of several kitchen - scale treatments , suitable for use by hospital staf f, caretakers, and patients themselves, for the reduction of L. monocytog enes on fresh produce, using cucumbers, apples, and celery as representative products. This study directly addresses the research gap for L. monocytogenes reduction on produce in heal thcare and home settings which is needed to develop the risk assessment i n Objective 2. The results of this study will help in the development of data - driven food safety guidelines for immunocompromised individuals and later be used to inform risk models . 3.1 Materials a nd Methods 3.1. 1 Produce Three different p roducts tested were chosen based on surface morphology: (i) miniature cucumbers (rough surface), ii) apples (smooth surface with stem and calyx ends ), and iii) celery (porous, rigid surface). Fresh a local supermarket, stored in a walk - in cooler at 4 ° C no longer than one week before treatment. Pre - cut, ready - to - eat (RTE) celery sticks were purchased packaged from a local supermarket and stored in a walk - in cooler at 4 ° C, with all treatment s completed within 10 days of purchase . All p roducts were visually inspected , and any samples showing surface/structural damage or decay were discarded. 15 3.1.2 Utensil and Supply Preparation All m etal utensils, including cooling racks, knives, stirring spoons, tongs, spatulas, apple slicers/corers, and manual peelers, were autoclave d at 121 ° C for 15 min before use . Plastic c utting boards and containers with non - metal components , such as the apple peeler , were thoroughly cleaned , disinfected in 75% ethanol , and d ried for 5 min before use. The same protocol was used if utensils had to be reused within the same replication . 3.1.3 Bacterial Strains Three avirulent Listeria monocytogenes strains (J22F seroty pe 4b (56) , J29H serotype 4b (56) , and M3 serotype 1/2a (96) ) were acquired from Dr. Sophia Kathariou from the Department of Food, Bioprocessing, and Nutrition Sciences at North Carolina State University. The stock cultures were stored at - 80°C in tryptic soy broth (TSB; Difco, BD, Sparks, MD) containing 10% (v/v) glycerol. Working cultures were streaked onto tryptic soy agar containing 0.6% yeast extract (TSAYE; Difco, BD), incubated at 37°C for 24 h, and transferred monthly. 3.1.4 Inoculum Preparation Fo r each strain, an isolated colony from the working stock was subjected to two 37°C / 24 h transfers , in 9 and 1000 mL of tryptic soy broth containing 0.6% yeast extract (TSBYE; Difco, BD) , respectively . Subsequently, the three strains were combined in a ste rile container to yield 3000 mL of inoculum. The L. monocyto genes population in the inoculum was ~10 9 log cfu / ml , as determined by plating samples diluted in sterile 0.1% phosphate - buffered saline (PBS) (MP Biomedicals, Irvine, CA) on TSAYE containing 0.02 5% (w/v) esculin (97% esculin hydrate; Sigma - Aldrich, St. Lo uis, MO) and 0.05% (w/v) ammonium iron citrate (Sigma - Aldrich). This medium (eTSA) is a non - selective, differential medium that produces gray - green, black - haloed 16 Listeria colonies with indented bl ack center s. Because preliminary work revealed low bacterial attachment to apples, the wax was removed before inoculation by very briefly submerging each apple in boiling water for 5 s and wiping with a paper towel . 3.1.5 Dip Inoculation Whole products wer e removed fro m storage, equilibrated to room temperature, and submerged in the three - stain L. monocytogenes cocktail for 10 min, while being agitated with a large sterile spoon. After inoculation, the samples were placed on a sterile stainless - steel rack i n a large pla stic tub, partially covered, and stored in a walk - in cooler at 4°C for 24 h. 3.1.6 Treatments T reatments included were: (i) submerging in a commercial produce sanitizer (1 oz sanitizer, 3,800 mL tap water) with active ingredients dodecylbenzen e sulfonic acid sodium salt (1.23%) and lactic acid (17.29%) (Monogram Clean Force, Ecolab, St. Paul, MN) for 90 s ( sanitizer rinsing under running tap water for 15 s under running tap water with hand scru bbing , ( soa king in tap water for 90 s with frequent agitation ( in boiling water for 25 s ( under running tap water for 15 s i) surface blanching in boiling water for 25 s The tap water temperature was 24.5 ± 0.2°C, as verified by a thermometer (Omega, Norwalk, CT). Blanching was performed in water with a temperature of 100 ± 0.2°C. After blanching , the whole products were plac ed in appropriately sized Whirl - Pak bags (Nasco, Fort Atkinson, WI) and immersed in an ice bath for 1 min to lower the surface temperature . Standard , handheld vegetable peelers were used for cucumbers and apples , and crank apple peelers (CucinaPro Apple Pe eler , CucinaPro , Trumbull, CT) also were used for apples. Two pieces of produce were examined for numbers of Listeria in 17 each of three replicated trials per treatment as described below. Sample assay methods are also described below. 3.1.7 Additional Exper iments Additional experiments were performed for celery and apples to evaluate product - specific attributes of inoculation and recovery. I noculum uptake experiments were performed on pre - cut celery sticks to elucidate surface vs. internal bacterial inactiva tion. Prior to inoculation, ce lery sticks were labelled with ink marker 1 mm from the root end at nine 1 - cm increments. The inoculum was pipetted into a sterile glass beaker to a height of 1 mm. Thereafter, t he beaker was covered with aluminum foi l contain ing holes through which the ce lery sticks were inserted to the bottom of the beaker. After 10 min in the 1 ml of inoculum, the celery sticks were removed and placed on a sterile stainless - steel rack in a walk - in cooler (4°C for 24 h) in the same manner as the other products. Three repl ications were performed, with two subsamples in each replication. For apples, the efficacy of peeling with a crank apple peeler designed to remove an apple peel in a continuous rotational peeling action was compared with a st andard , handheld vegetable pee ler. Apples were inoculated as previously described, rinsed for 15 s with hand scrub bing under running tap water, and then peeled using either the apple peeler or a standard handheld vegetable peeler. Again, three replications were performed, with two subsamples in each replication. 3.1.8 Peeling Miniature cucumbers were peeled vertically from top to bottom with the standard handheld vegetable peeler until no skin remained. The ends w ere not peeled and were removed during sampl ing. Apples were also manually peeled from top to bottom . leaving the skin at the calyx and stem ends (about 10% of the total) remaining. The p eelers were sterilized between 18 products. Apples peeled by the apple p eeler were secured to the core prongs and we re peeled until about 5% of the total apple skin remained at the calyx and stem end s . 3.1.9 Post - Treatment Sample P reparatio n, Recovery, and Enumeration The treated samples were placed on a sanitized cutting board in a biological safety cabinet. Apples wer e cored and sliced into 12 equal size wedges using a sterile apple slicer/corer (Vremi, New York, NY), with every fourth piece (three slices total, the first chosen at random) placed into a Whirl - Pak sampling bag (Nasco). Cucumbers were cut into 1 - cm thick slices (measured with a sterile ruler) using a sterile knife. End pieces were discarded. and the next closest pieces, as well as the center piece (three slices total), were placed in a sampling bag. The same procedure was repeated for celery using segment s 3 - cm in length. In the inoculum uptake experiment for celery, the stalk was cut into pre - labelled 1 - cm long segments with a new sterile knife used for each cut to eliminate inoculum transfer. All sa mples were diluted 1:5 (w/v) in sterile 0.1% PBS and sto mached for 180 s (IUL Masticator Silver, 400 ml, IUL S.A., Barcelona, Spain). Thereafter, 1 mL of the homogenized sample was serially diluted in 9 mL sterile 0.1% PBS with appropriate dilutions plated on eTSA (1 mL for suspected low populations, 0.1 otherw ise). After 48 h of incubation at 37°C. all colonies resembling L. monocytogenes were counted and compared to the untreated control (log cfu /g). The limit of detection was 2.5 cfu /g . These methods wer e also applied to untreated products ( two per replicatio n, totaling six of each) to determine p roduct - specific m esophilic bac teria populations . 3.1.10 Statistical Anal ys e s All experiments were performed in triplicate with duplicate subsamples for each treatment. Statistical tests included analys i s of variance ( generalized linear model ANOVA) followed by a Tukey pairwise comparison , and two - sample t - tests for comparison between 19 individual treatments (all completed using Minitab 19, State College, PA), wi th a significance n treatments that resulted in undetectable levels of L. monocytogenes, the limit of detection was used as a conservative substitute data point , and t - tests were not performed for these treatments. 3.2 Results a nd Discussion 3.2.1 Inoculation and Microbial Background Population s Inoculation resulted in mean L. monocytogenes populations of 5.3, 6.6, and 7.1 log cfu /g for apples, cucumbers, and celery, respectively ( Error! Reference source not found. ). No suspect Listeri a colonies were isolated from uninoculated pro ducts. Uninoculated cucumbers and celery yielded mesophilic bacteria populations of 6.3 and 3.1 log cfu /g, respectively. For apples sampled following wax removal, background microflora was less than the limit of detection in five of six samples; one apple yielded 2.0 log cfu /g APC. Due to the high inoculum levels, background microbes were assumed to not impact L. monocytogenes enumeration in the samples after treatment. Table 1 : Initial populations of L. monocytogenes on inoculated products Product Population (95% CI) (log cfu /g) Apple 5. 3 (4.7, 5.8) Cucumber 6.6 (6.4, 6.8) Celery 7.1 (6. 9 , 7.4) A layer are sealed by the melting of the wax, which may prevent pathogen uptake (93) . However, because apples were visually inspected for external damage prior to inoculation, and cores (the main 20 pathogen entry point) were not sampled, it was assumed that th is did not alter the overall inoculation and treatment results . 3.2.2 Apples P opul ations of L. monocytogenes were significantly lower ( P < 0.05) in all treated apples than the positive controls, as determined by individual t - tests. ANOVA followed by the Tu key test indicated that blanch yielded significantly greater ( P < 0.05) reduction s (mean 4. 2 log cfu /g) than sanitizer soak, tap water rinse, tap water soak, and rinse+peel ( Table 2 ). Sanitizer soak, tap water rinse, and tap water soak did not differ significantly in efficacy. Table 2 : Reduction of L. monocytogenes on apples, cucumbers, and celery following different kitchen - scale treatment s * Within this column, products followed by a common lower - 0.05) mean reductions for all treatments ; peeling treatments were excluded for unbiased comparison across products . Mean reductions were 2.8, 2.3, and 1.2 log cfu /g for apples, cucumbers, and celery, respectively. ** Within this column, mean reductions followed by the same n the same product category. Due to the structur al differences between apple s , the specialized apple slic er and cor er did not always precisely core the apple. In preli minary work, apple cores tested separately from Product * Treatment L. monocytogenes reduction (log cfu /g) Mean (95% CI) ** Apple a Saniti zer soak 1. 2 (0. 8, 1. 6 ) C Tap water rinse 1. 3 (0.3, 2.2) C Tap water soak 1.0 (0.5, 1.5) C B lanch 4. 2 (3. 6 , 4.8) A Ri ns e+ peel 2.4 (1.3, 4.8) BC B lanch+ peel 3.5 (2. 1 , 5.0 ) AB Cucumber a Sanitizer soak 1. 5 (1. 2 , 1.8) BC Tap water rinse 1. 4 (1. 2 , 1.5) BC Tap water soak 0. 6 (0. 4 , 0. 8 ) C B lanch 5. 1 (3. 7 , 6.4) A Rinse+peel 2. 4 (1.4, 3. 4 ) B B lanch+ peel 5. 9 (5. 4 , 6. 4 ) A Celery b Santizer soak 0. 6 ( - 0.2, 1. 4 ) A Tap water rinse 0.5 ( - 0.2, 1. 3 ) A Tap water soak 0.6 (0 . 2 , 1.0) A B lanch 1.2 (0.3, 2. 1 ) A 21 apple slices had a significantly lower m ean reduction ( 0.9 and 1.6 log cfu /g for cores and slices , respectively ) of L. monocytogenes across all treatments. Other studies reported similar results (23, 24, 98, 156) . Because this portion of the apple is inedible, and therefore would not contribute to the risk of acquiring listeriosis from eating apples slices separated from the core of an intact apple , it was not included in further analysis. Nevertheless, the inability of the apple corer to precisely remove the entire core from sampled slices likel y contributed to the variab le results, and therefore to the resulting risk in actual food preparation and consumption scenarios. It was hypothesized that , compared to standard handheld vegetable peeler, the apple peeler would be more effective at reducing L. monocytogenes populations, as the cutting tool is not continuously reintroduced into the contaminated surface. Both the standard handheld and apple peelers reduced L. monocytogenes significantly ( P < 0.05) from the positive control ( Table 3 ) . A two - sample t - test indicated that there was no significant difference in the efficacies of the two tested peeling methods ( P = 0.83), suggesting little food safety benefit from using one pe eler over the other. Table 3 : Reduction of L. monocytogenes on apples following peeling with standard handheld or crank apple peelers Peeler L. monocytogenes Reduction (log cfu /g) Mean (95% CI) Standard handheld 1.6 ( 0.7 , 2. 6 ) Apple 1. 8 ( 1. 1 , 2. 5 ) 3.2.3 Cucumbers Similar to a pples, all cucumber treatments significantly lowered ( P < 0.05) L. monocytogenes populations compared to the untreated controls, with sanitizer soak, tap water rinse, and tap water soak exhibiting similar efficacy. As expected, blanch+peel was the most 22 eff ective treatment for cucumbers (mean reduction 5.9 log cfu /g) but was not significantly different from blanching alone (mean reduction 5.1 log cfu /g) ( Table 2 ). 3.2.4 Celery Tap water soak and blanch significantly decreased ( P < 0 .05) L. monocytogenes compared to the positive control ; however , tap water rinse and sanitizer soak yielded P - values of 0.23 and 0.13, respectively, meaning that these treatments resulted in no significant reduction in population. None of the treatments w ere significantly different from each other, but the maximum redu ction was achieved with surface blanching (mean reduction 1.2 log cfu /g). This limited efficacy is likely due to inoculum internalization during dip inoculation, with Listeria migrat ing throu gh the porous structure and end cuts of the celery during processing (52, 95, 158, 177) . Results of the celery i noculum uptake experiment confirmed that L. monocytogenes can migrate in celery ( Figure 2 ) . The mean L. monocytogenes pop ulation in the inoculated 1 mm end piece was 7. 5 log cfu /g. Although the numbers of L. monocytogenes decreased with distance Figure 2 : L. monocytogenes population and distance from inoculated celery end in inoculum uptake experiment . 23 from the point of inoculation ( Figure 2 ), L. monocytogenes was still detected as far as 9 cm from the cut end. These results support the internalization of Listeria into celery during submersion, with these ce lls remaining viable after t he surface treatments (175, 191) . 3.2.5 Overall Treatment Comparisons Overall, the surface decontamination treatments were mo st effective for cucumber s , and not significantly different for apples ( Table 2 ). Efficacy for celery was minimal and significantly less ( P < 0.05) than for both cucumber s and apples. As results from the inoculum uptake experiment indicated, this is assumed to be due to internalization of Listeria in celery. Reductions due to sanitizer soak were relatively low, 0. 6 - 1. 5 log cfu /g , and significantly different from the control for apples and cucumbers, but not celery. Similarly, severa l past studies of various commercial produce sanitizers reported reductions of 0.5 - 2.3 log cfu /g, which were either not significantly different from or similar to reductions due to water treatment s (62, 98, 113, 115, 139, 176) . However, Beuchat et al. (16) and Harris et al. (81) reported much higher reductions (> 4.8 log cfu /g) . According to Lopez et al. (113) these differences could be due to the manner in which these treatments are applied in laboratory, commercial, and home settings. Given their low effi cacy, these treatment s may be insufficient as a risk reduction strategy for preparing fresh produce to be consumed by immunocompromised individuals. Across all products, tap water rinse and soak yielded small reductions (0.5 - 1. 4 log cfu /g), with no signifi cant difference in efficacy between the two treatments. These results correspond with previous studies on leafy greens, carrots, broccoli, apples, tomatoes, and parsley (53, 62, 90, 98, 131, 142) , which reported reductions of 0. 1 - 3.0 log cfu /g for scrub bin gs and soak ings in sterile water or tap water. These results were slightly lower than those of Parnell et al. (141) , who 24 reported Salmonella reductions of > 4.6 log cfu / melon after scrubbing honeydew melons in water for 60 s, which was significantly more effective than soaking . However, their greater reduction can be attributed to the longer wash time, and variation in treatment efficacy due to the (90, 98, 131, 141, 142) . While recommended by the FDA (183, 185) , ri nsing produce under running tap water is minimally effective and should not be used as the sole decontamination step when preparing fresh - cu t produce for immunocompromised individuals . Rinse+peel yielded L. monocytogenes reductions of ~2.4 log cfu /g for a pples and cucumbers, with surviv ing populations ranging from 2.8 to 4.2 log cfu /g. Correspondingly, Erickson et al. (53) and Wade et al. (190) reported moderate reduction s after peeling cucumbers and carrots (2. 2 to 3.2 log cfu /g), with substantial numbers of survivors (2.6 to 2.7 log cfu /g). In an ANOVA comparing all apple and cucum ber treatments, rinse+peel was significantly more effective ( P < 0.05) than tap water rinse, tap water soak, and sanitizer soak, but significantly less effective ( P < 0.05) than blanch and blanch+peel, indicating that the latter treatments may be most effe ctive . Surface blanching followed by peeling has not previously been reported as a pathogen reduction treatment for kitchen - scale fresh produce preparation. The results f or app les and cucumbers indicated that blanch+peel may or may not reduce pathogens more effectively than blanching alone. However, in considering candidate treatments for preparing fresh produce for immunocompromised individuals, a conservative approach of combi ning blanching and peeling may be recommended . Results for apple and cucumber blanching alone were comparable to those reported by Ceylan et al. (36) , Bacgi and Temiz (9) , and Losikoff (114) , in which blanching vegetables in hot or boiling water for 30 s r educed L. monocytogenes populations by 25 3.3 - 5 log cfu /g. In contrast, Mazzotta (121) reported instantaneous 5 - log cfu reductions when vegetables were blanched at temperatures greater than 82 ° C, and Monu et al. (127) modeled 6 log cfu /g reductions for spinac h blanched at 90 ° C for 0.00002 s. However, th ey used flattened bags of 3 - 10 g homogenized vegetable samples that were presumably more susceptible to heat treatment than whole products, which could explain the perceived increased efficacy. B lanching was sig nificantly less effective ( P < 0.05) for cele ry than for apples and cucumbers , which again is likely due to internalization of the inoculum. After blanch ing, the apple ski n was slightly darker . However, all products appear ed to maintain their texture , wi th blanching not affecting the ease of peeling apples or cucumbers. Briedt (21) and Fan (57) also reported that a 15 s blanch in boiling water did not affect the physical properties of cucumbers and cantaloupes, respectively. Mazzotta (121) reported sim ila r results for broccoli ; however , onions and peppers were more easily compromised. V itamin C retention following blanching, which varies by product type , was not affected in cantaloupes (57) , but decreased 28% in peas (157) . Future studies assessing the imp act of blanching on produce texture and nutrient depletion are needed to make informed decisions regarding the use of produce blanching for cancer patients on neutropenic diet s . D ip inoculation may not be representative of all contamination events, jus t the scenario in which the product becomes contaminated during washing . The same level of inoculum uptake may not occur in situations where the surface of the product becomes sporadically contaminated with pa thogens. Due to the vulnerability of the target population to L. monocytogenes , using the present conservative approach is most prudent in developing data to be used in future risk models and updated recommendations for produce preparation. 26 3.2.6 Conclusi ons Limited success was achieved with the propo sed microbial reduction strategies for celery , given that the inoculum migrated through the celery from the cut end . For this reason, it is recommended that immunocompromised individuals not consume porous or pre - cut RTE produce , which has significant pote ntial for pathogen internalization. Because sanitizer soak, tap water rinse, and tap water soak did not differ in e fficacy, and were minimally effective in decreasing L. monocytogenes, these treatments are not recommended. For apples and cucumbers, blanch and blanch+peel were the most effective treatments, with mean L. monocytogenes reductions > 3.5 log cfu /g. Although these two treatments were not significantly different from one another, we recommend to conservatively blanch and peel products that are to be consumed by immunocompromised individuals. Further studies assessing product integrity , nutrient retention , and consumer appeal of such treated products will also help to inform risk models and improve food safety guidelines for kitchen/home - scale prepa ration of fresh fruits and vegetables for immunocompromised individuals. 27 CHAPTER 4: DEVELOPING A RISK MODEL FOR LISTERIOSIS IN CANCER PATIENTS WHO CONSUME READY - TO - EAT SALAD While many listeriosis risk assessments have been performed for RTE foods, i ncluding salads, none are specific to cancer patients who have an increased risk of acquiring the disease. Additionally, the current typical risk management strategy for this population is exclusion of all raw produce, which has unknown eff icacy and nutrie nt disadvantages to the patient. This study aim ed to examine the risk posed to this vulnerable population from consuming produce and the effects of several kitchen - scale risk management strategies on that risk , thereby completing the second research object ive of this thesis. The specific objectives necessary to meet this goal are: i) using the QMRA fra mework, develop a stochastic risk model that utilizes Monte Carlo simulation to output a probability distribution for risk associated with a model sal ad; and ii) incorporate the effects of salad - inclusive risk management strategies, such as kitchen - scale p roduce preparation techniques and proper storage, into the risk model to evaluate efficacy. 4.1 Materials a nd Methods 4.1.1 Risk M odeling T ool Monte Carlo simulations of both the final exposure dose s and the final output s , the risk of listeriosis, were run in @Risk for Excel version 8.0 (Palisade, Ithaca, New York) with 10,000 iterations and a seed of 123. When sample size allowed (n ), parameter di stributions were fit to data and ranked by the chi - square statistic; otherwis e , a uniform distribution was assumed. In e.g., for growth rates, a bounded distribution). F inally, in @Risk, a sensitivity analysis was conducted using Spearman rank co rrelation coefficients , which were used to compare the magnitude of uncertainty and variability for each input parameter. 28 4.1.2 Dose - Response Modeling An exponential dose - respons e model ( Equation 1 ) was used to model P(d) , the probability of developing invasive listeriosis following consumption of a given dose, d . Parameter k represents the probability of an organism surviving to cause infection. This model wa s appropriate , as it assumes a random dis tribution of pathogen throughout the food (as one may expect in a mixed salad), and that one organism may survive to cause infection (79) . For microbial risk assessments, this model is generally preferred over model s that have a minimum dose threshold (187 ) . 1 Two approaches were used to create k distributions. The first approach transformed dose - dependent k ons (FAO/WHO risk assessment, tables 2.18 and 2.21, respectively (199) ), into k values specific to cancer patients, using relative susceptibilities (RS) (Equation 2 ) (77, 130) . Relative susceptibilities were based on listeriosis risk r atios for cancer patients compared to those under 65 with no immunocompromising conditions (77) and to all immunocompromised individuals (130) . Because hematological cancer patients had the highest RS in all studies, those values were used to devise a cons ervative model. These transformed data were pooled into one k distribution. 2 The second approach reconstructed the characterizat ion of the k distribution by Pouillot et al. (146) for hematological cancer patients in @Risk. The final risk model was run separately with each k parameter approach so that the results could be compared. 29 4.1.3 Exposure Assessment The simplified model sala d product consisted of leafy greens ( lettuce, spin ach, mixed salad, arugula, kale, chicory, radicchio, endive, swiss chard, and watercress ) , tomatoes , and cucumbers. A thorough literature review was conducted to complete the exposure assessment for the mod el salad produc t . First, distributions for L. mono cytogenes populations at the point of retail were created independently for each product. It can be assumed that the populations on retail samples reflect the changes in population that occur post - processin g (186) . Data from multiple leafy green studies ( Table 4 ) were pooled (to better represent a variety of samples) to create a single distribution of L. monocytogenes concentration (log cfu /g). Requirements for data inclusion were that t he study must report both prevalence and population and collect samples under retail conditions like those in the United States. MPN/g and cfu /g units were considered interchangeable du e to similarity of study methods. For samples with undetectable levels of L. monocytogenes , concentration was recorded as the limit of detection in the corresponding study. If a range of concentrations was given, the geometric mean of that range was used. cfu /g the reported value was used. In studies where a portion of the positive samples were not enumerated, the geometric mean of the minimum and maximum concentrations from enumerated sam ples was used. The same approach was used for concentration of L. monoc ytogenes in cucumbers, although the distribution was fit to data from a single study (80) . These methods support a conservative risk model, in which results reflect the upper limit of e xposure for this population. 30 Table 4 : Summary of data used to create prevalence and concentration distribution for leafy greens Unfortunately, data on p revalence and concentration of L. monocytogenes in tomatoes in developed countries are lacking . This necessitated building a model in @Risk to simulate a tomato exposure scenario, using an approach previously applied by Todd (179) . For simplicity, the assu med contamination route was field irrigation wat er . Tomatoes then followed a simplified harvesting and processing pathway consisting of fruit growth in the field, a 200 ppm chlorinated wash , and transportation to retail stores in a delivery truck ( Figure 3 ). Product Prevalence Country Reference Leafy green vegetables 2/100 (2%) Finland (133) Minimally processed salads 1/151 (0.7%) Portugal (154) Packaged mixed salad 10/500 (2% ) Ireland (65) RTE salad and whole components 2/246 (0.8%) Spain (1) RTE prepared salad vegetables 88/2,932 (3%) United Kingdom (151) RTE leafy salad vegetables 0/35 (0%) Croatia (101) RTE leafy salad vegetables 10/452 (2.2%) Brazil (153) Minimally pr ocessed leafy vegetables 0/69 (0%) Brazil (43) RTE and unprocessed leafy greens 15/6,115 (0.2%) United States (201) Bagged leafy vegetable salads 22/2,966 (0.7%) United States (74) Whole lettuce 0/151 (0%) United Kingdom (112) 31 Figure 3 : Components of exposure assessment 32 A distribution for L. monocytogenes prevalence and concentration in irrigation water was fit to data from Allard (3) and Watkins and Sleath (192) . Tomato irrigation and growing were based on Florida practices, as Florida i s one of the two largest tomato producers in the United States (200) . Irrigation water applied per tomato plant was estimated by dividing drip irrigation water applied per acre of tomato plants (170) by the number of plants per acre (66) . It was then possi ble to calculate the L. monocytogenes concentration per contaminated tomato by multiplying the L. monocytogenes concentration by the water applied per plant, then dividing by 40 (the approximate number of tomatoes per tomato plant (179) ). This implied that all irrigation water contacted the tomato fruit, which is improbable . However, the fraction of water that touches the tomato is a complex parameter depending on plant species, growth stage, and individual irrigation system design. Therefore, for the purpo se of this risk assessment, it was conservatively assumed that all wa ter contacted the tomato fruit. Next, the changes in population of L. monocytogenes on tomatoes in the field, after a 200 ppm chlorine treatment during processing, and during truck trans portation to retail were sequentially added to the exposure dose dose. Prior studies on L. monocytogenes survival on tomatoes were performed at 5 ° C (144) and 21 ° C (15) . According to expert opinio n (179) , temperatures in the field and delivery truck can be approximated by uniform distributions between 10 and 40 ° C and 0 and 40 ° C, respectively. Because 21 ° C is close to the geometric mean of these distributions, survival at 21 ° C was assumed to be repr esentative in the aforementioned cases. Based on growth study results (15) , it was assumed that L. monocytogenes populations on tomatoes in the field were constant after 8 days. Data from whole tomatoes that had and had not been treated with chlorine were used for L. monocytogenes survival during truck transport and in the growing field, respectively. 33 Survival distributions often included values that resulted in L. monocytogenes populations greater than maximum achievable populations ( ~ 10 9 cfu /g on various fresh produce (86) ) or , if reduction due to the chlorine trea tment was greater than L. monocytogenes on the tomato, below zero. Therefore, after each concentration calculation (in the field, after a chlorine treatment, and after transportatio n to retail), the distribution was truncated to exclude such values , using an IF function in @Risk. This function set values greater than 10 9 and less than 0 to 10 9 and 0, respectively. Next, it was assumed that salad components were purchased from retail within one day of arrival, and e xposure doses for each salad component were altered by various consumer risk management strategies . First, retail exposure doses were modified according to consumer storage practices. Refrigerated and elevated - temperature ho me storage conditions were considered (4 - 5 ° C and 10 ° C for leafy greens and cucumbers, and 4 ° C and 21 ° C for tomatoes, respectively). To calculate survival at the specified temperatures, populations of L. monocytogenes during storage (log cfu /g) were extracted from reference source data tables or graphs using DataThief III (182) , a nd change in population was divided by the number of days in the study. Population change per day w as then multiplied by the number of days s tored by consumers, the distribution for which was previously described in a USDA/FSIS risk assessment (186) . The o riginal distribution used a , but to re construct this distribution in @Risk, the mean of those values was used. Change in population during storage (log cfu ) was added to the retail exposure dose. L. monocytog enes reduction during each of the following kitchen - scale preparation techniques was then applied to each product: i) commercial sanitizer soak (1.5 to 2 min) , which was Veggie Wash (2.0 oz/gal of water, Beaumont Products Inc., Kennesaw, Ga.) for tomatoes 34 and leafy gree ns and Ecolab sanitizer (Monogram Clean Force) for cucumbers ; ii) tap water rinse (15 s); iii) tap water soak (1 to 5 min); iv) surface blanch (25 to 30 s); and v) peel. Because greens cannot be blanched without compromising integrity, or pee led , s urface b lanch and peel were only applicable to cucumbers and tomatoes . I n these cases, tap water rinse was assumed for greens. Because few kitchen - scale foodborne pathogen removal studies exist for L. monocytogenes , others using L. innocua and/or Sal monella were included to fill distributions for some treatments, assumin g the efficacy of the treatment would not vary substantially from the target scenario. Due to limited data for tomatoes specifically, distributions for tomato blanching and peeling inc luded data from peas, potatoes, and cucumbers, and carrots and cucumbers, respectively. Products and references for each risk management parameter are listed in Table 5 . Akin to the initial tomato concentration distribution, exposur e do se distributions were truncated at each calculation to exclude impossible values. 35 Table 5 : Summary of literature review for risk management parameters 36 Distributions for consumption (g/day) of leafy greens and tomatoes were fit to data on consumption of these foods by outpatient cancer patients not placed on neutropenic d iets (87) . It was assumed that cucumber consumption was the same as tomatoes. The post - treatment L. monocytogenes doses for each salad product ( cfu /g) were then mul tiplied by corresponding consumption (g/day), resulting in a daily individ ual L. monocytogen es dose for each product. The daily product exposure doses were summed to yield the final daily exposure dose for a mixed salad. This value was used in further risk calculations. 4.1.4 Risk Characterization Daily risk was calculated using Equation 1 . Due to the previously discussed immunocompromising effects of chemotherapy, it can be assumed that the duration of the exposure period is one chem otherapy cycle, which can last from 3 - 4 weeks (129) . This time period had a uniform distribution , and daily risk was compounded to calculate risk per chemotherapy cycle (Equat ion 3 ) . 3 4.2 Results 4.2.1 Input Distributions All distributions used to simulate the concentration of L. monocytogenes on tomatoes at ret ail are summarized in Table 6 . The L. monocytogenes concentration in irrigation water was modeled by a = = 169.53. The amount of irrigation water applied to tomato crops, tomato plants per acre , weight of a tomato, number of days in the field af ter contamination, and time spent in the retail delivery truck were all modeled with uniform distributions . L. monocytogenes growth on tomatoes in the field and delivery truck, and survival 37 during a 200 p pm chlorine wash , were modeled with uniform and tria ngular distributions, respectively. Table 6 : Parameters used to model the concentration of L. monocytogenes on tomatoes at retail ( cfu /g) Parameter n Value/Distribution Units Sou rce Starting concentration of L. monocytogenes in irrigation water 25 shift=0.04 cfu /L 61, 192 Irrigation water applied to crop 29 Uniform: min=2271.246, max=15141.64 L/acre 170 Tomato plants per acre 1 Uniform: min=3630, ma x=5800 Plants/acre 66 Tomatoes per plant 1 40 Tomatoes/plant 179 Weight of tomato 1 Uniform: min=120, max=140 g/tomato 15 Days in field after contamination 1 Uniform: min=1, max=50 Days 159 L. monocytogenes growth on tomatoes, field, days 0 - 8 4 Uniform : min=0.2, max=0.2375 Log cfu /g/day 15 L. monocytogenes growth on tomatoes, field, days 8+ 1 0 Log cfu /g/day 15 L. monocytogenes reduction on tomatoes, 200 ppm chlorine 5 Triangular: min=2.2273, likeliest=3.55, max=3.55 Log cfu /g 16, 17 L. monocytogenes transfer to uninoculated products, 200 ppm chlorine 1 0 % (0 - 1) 19, 102 Time in truck (transportation to retail) 1 Uniform: min=0, max=7 Days 179 L. monocytogenes growth on tomatoes, delivery truck 4 Uniform: min=0.175, max=0.2 Log cfu /g/day 15 The re maining distributions used in the exposure assessment are summariz ed in Table 7 . Distributions for k parameter approaches 1 and 2 were inverse Gaussian and lognormal, respectively. The distribution for k using approach 1 was invers × 10 - 11 37 × 10 - 12 , and shift= - 3.9456 × 10 - 13 . The mean and median of the distribution were 3.616 × 10 - 11 and 7.758 × 10 - 12 , respectively. Adopted from Pouillot et al., (146) the second approach k distribution was parameter ized using the 50, 99, and 99.9 percentil es, 9.51 × 10 - 12 , 38 5.44 × 10 - 8 , and 9.33 × 10 - 7 . The resulting mean and median were 9.605 × 10 - 9 and 9.510 × 10 - 12 , respectively . 39 Table 7 : Distributions used in risk calculations Parameter n Product Distribution L. monocytogenes concentration ( cfu /g) 13,617 Leafy greens - Tomatoes See Table 3 for all input distributions 100 Cucumbers Consumption (g/day) 100 Leafy greens - 8.2991 100 Tomatoes, cucum bers - 1.5298 k , approach 1 8 - - - 12, shift= - 3.94562E - 13 k , approach 2 - - Lognormal: 50%=9.51E - 12, 99%=5.44E - 8, 99.9%=9.33E - 7 Days in risk period (chemotherapy cycle) 1 Uniform: min=21, max=28 Post - retail storage time (days) 1 Pert: min=0.5, likeliest=3.5, max=10 L. monocytogenes growth (log cfu /g/day) 19 Leafy greens, 4 - 5 ° C Extreme value: a=0.039401, b=0.065095 28 Leafy greens, 10 ° C Pert: min= - 0.43015, likeliest= 0.32794, ma x=0.38253 1 Tomatoes, 5 ° C Static value=0.08 4 Tomatoes, 21 ° C Uniform: min=0.175, max=0.2 3 Cucumbers, 4 - 5 ° C Uniform: min=0.18, max=0.198333 4 Cucumbers, 10 ° C Uniform: min=0.041429, max=0.858 L. monocytogenes reduction, sanitizer soak (log cfu /g) 6 Leafy greens 4 Tomatoes Uniform: min=1.15, max=2.98 6 Cucumbers Pert: min=0.92434, likeliest=1.3828, max=2.4758 L. monocytogenes reduction, tap water rinse (log cfu /g) 6 Leafy greens 4 Tomatoes Uniform: min=1.82, max=2.44 6 Cucumbers Triangle: min=1.1569, likeliest=1.1569, max=1.75039 L. monocytogenes reduction, tap water soak (log cfu /g) 5 Leafy greens Pert: min=0.5, likeliest=0.5, max=2.9438 3 Tomatoes Uniform: min=0.69, max=2.6 5 6 Cucumbers Triangle: min=0.17726, likeliest=0.75956, max=0.75956 L. monocytogenes reduction, surface blanch (log cfu /g) 14 Tomatoes Extreme value: a=5.8666, b=1.9165 6 Cucumbers L. monocytogenes reduction, peel (log cfu /g) 7 Tomatoes Triangle: min=0.55093, likeliest=3.1402, max=3.1402 6 Cucumbers max=3.1402 40 L. monocytogenes concentration on leafy greens was modeled with a log - normal distribution with a mean of 8.07 and a standard deviation of 487.94. Concentration on cucumbers was described by an exponential distribution with a shift of 0.034024. Consumption data for both leafy gre ens and tomatoes were fit to Pearson 5 distributions. Post - retail storage time was fit to a Pert distribut ion with minimum and maximum of 0.5 and 10 days, respectively, and a most likely value of 3.5 days. Growth in leafy greens at 4 - 5 ° C and 10 ° C was model ed by the extreme value distribution and the Pert distribution, respectively. Uniform distributions were u sed to model growth on cucumbers at both 4 - 5 ° C (0.18 to 0.19833 log cfu /g/day) and 10 ° C (0.041429 to 0.858 log cfu /g/day). Growth on tomatoes at 5 ° C was represented by a static value of 0.08 log cfu /g/day, and at 21 ° C, the uniform distribution ranged fro m 0.175 to 0.2 log cfu /g/day. The effects of the sanitizer soak, tap water rinse, and tap water soak in greens were modeled by exponential, Laplace, a nd Pert distributions, respectively. In tomatoes, reductions due to sanitizer soak, tap water rinse, and tap water soak were fit to uniform distributions due to lack of data. For the same treatments in cucumbers, reduction distributions were Pert, triangle , and triangle, respectively. Across all products, mean reductions due to the se treatments ranged between 0.57 and 2.13 log cfu /g. Blanching data were fit to extreme value and logistic distributions for tomatoes and cucumbers , respectively. Blanching was b y far the most effective treatment, with mean reductions of 6.97 and 5.16 log cfu /g for tomatoes and cucumbers, respectively. The effects of peeling for tomatoes and cucumbers were fit to triangle and beta general distributions, respectively, and mean redu ctions ranged between 2.18 and 2.28 log cfu /g. Median daily exposure doses ca n be seen in Table 19 . 41 4.2.2 Risk Distribu t ions Summarized distributions of risk per chemotherapy cycle for all treatments, storage conditions, and k app roaches are shown in . Across all scenarios, median risk ranged from approximately 7 × 10 - 11 to 1 × 10 - 7 ( about 1 in 14 trillion to 1 in 10 million) . Table 8 : Risk per chemotherapy cycle for each risk manag ement strategy and k approach Treatment Storage Temp k Approach 5% 50% 95% Control Refrigerated 1 2.6E - 10 1.3E - 08 9. 5 E - 07 2 4.2E - 11 1. 5 E - 08 1.6E - 05 High 1 8.9E - 10 1.0E - 07 6. 8 E - 05 2 1.5E - 10 1.3E - 07 5. 3 E - 04 Sanitizer soak Refrigerated 1 1.3E - 11 7. 4 E - 10 9.7E - 08 2 2. 1 E - 12 8. 7 E - 10 1.2E - 06 High 1 4.1E - 11 5.8E - 09 3. 1 E - 06 2 7. 8 E - 12 7.2E - 09 2.6E - 05 Tap water rinse Refrigerated 1 9.5E - 12 5.2E - 10 4.0E - 08 2 1. 5 E - 12 5.6E - 10 6.4E - 07 High 1 3.0E - 11 3. 9 E - 09 3.0 E - 06 2 5. 5 - 12 5.2E - 09 2.2E - 05 Tap w ater soak Refrigerated 1 4. 5 E - 11 2.5E - 09 1. 8 E - 07 2 7.4E - 12 2. 8 E - 09 3.0E - 06 High 1 1. 5 E - 10 2.0 E - 08 1. 7 E - 05 2 2. 5 E - 11 2. 6 E - 08 1. 2 E - 04 Surface blanch tomato and Refrigerated 1 4. 2 E - 13 5. 7 E - 11 2. 1 E - 08 cucumber, rinse greens 2 7. 9 E - 14 7.3E - 11 1. 9 E - 07 High 1 1. 2 E - 12 2.7E - 10 1.4E - 07 2 2. 7 E - 13 3.3E - 10 1. 2 E - 06 Peel tomato and cucumber , Refrigerated 1 3. 2 E - 12 2. 6 E - 10 3.2E - 08 rinse greens 2 5. 1 E - 13 3.0E - 10 4.8E - 07 High 1 1. 1 E - 11 1. 7 E - 09 1.1E - 06 2 2. 1 E - 12 2.2E - 09 8. 9 E - 06 The approach used to model k had minimal effect on the predicted risk of listeriosis ( Figure 4 ) . W ithin a specified treatment and storage temperature, distribution of k was 42 observably tighter about the mean for the second k approach. T he second k approach resulted in median risk less than one order of magnitude higher and 95 th percentile risk up to two orders of magnitude higher than that calculated using the first approach . Because of this marginal increase, risk distributions created using the second k approach were further visualized in boxplots ( Figure 5 ) . When the k approach and treatment were kept constant, temperature abuse of the salad Figure 4 : Histograms of risk distribution for refrigerated control salads, calculated using k approaches 1 (a) and 2 (b) a) b) 43 resulted in a median risk approximately two orders of magnitude higher than for refrigerate d salad. When the salad was properly refrigerated, control scenarios resulted in the greatest median risk, which was 1.493 × 10 - 8 ( approximately 1 in 67,000,000 ) and was one to two orders of magnitude greater than risk from salads t hat were subjected to sanitizer soak, tap water rinse, and tap water soak. Peeling refrigerated salad ingredients decreased median risk by about two orders of magnitude, to a maximum of 3.013 × 10 - 10 . For refrigerated salads in which tomatoes and cucumbers were blanched and greens were rinsed, median risk dramatically decreased to 7.347 × 10 - 11 (about 1 in 14 trillion ). T his scenario had the lowest risk of those tested . Figure 5 : Box and whisker plots of risk distribution s for each storage and treatment scenario, calculated using k approach 2. 44 4.2.3 Sensitivity Analysis Sensitivity analyses revealed that consistently, for both k parameter approach es, the parameters with the top Spearman rank correlation coefficients varied depending on whether ingredients were blanched ( Figure 6 ). In such cases, the parameters with th e highest correlation coefficients were k (0.47 to 0.76 ), initial contamination on greens (0.43 to 0.66), greens Figure 6 : Spearman rank correlation coefficients for refrigerated control (a) and blanched (b) salads, calculated using k approach 2 b a 45 consumption (0.24 to 0.37), L. monocytogenes growth on greens (0.15 - 0.27), and L. monocytogenes removal during rinsing ( - 0.11 to - 0.15). For all other treatments and both k parameter approaches, t he most impactful coefficients were k (0.47 to 0.88), initial contamination on greens (0.16 to 0.47), post - retail storage time (0.10 to 0.46), tomato consumption (0.17 to 0.37), initial cucumber contamination (0.07 to 0.27), and greens consumption (0.07 to 0.23). When these salads were stored at elevated temperatures, post - retail L. monocytogenes growth on cucumbers was also a highly influential parameter (0.24 to 0.46) . 4.3 D iscussion The k parameter depends on both host and pathogen factors, as well as th e more complex host - pathogen interacti on. Distributions for k in this study were highly variable, but less so when the second k approach was used. This is because the characterization of the second k distribution was derived specifically for cancer patient s , based on a wide array of epidemiological data (146) , and c ould better describe the host - pathogen interaction . Still, t he sensitivity analysis revealed that k had the greatest influence on risk. Because the exponential dose - response model solely relies o n k , the distribution of which is highly variable , it is likely that a more complex dose - response model is needed to account for interactions between L. monocytogenes and cancer patients. Values for k in this study were comparable to previously reported k - values. Buchanan et al. (22) (111) reported k values for immunocompromised populations of 1.179 × 10 - 10 and 5.6 × 10 - 10 , respectively. Chen et al. (37) studied the effect of L. monocytogenes genotypic subtype on parameter k and reported average k values for t he general at - risk population of 1.32 × 10 - 8 to 5.01 × 10 - 11 , depending on genetic lineage. In the present study, av erage k values ranged from 9.61 × 10 - 9 to 3.62 × 10 - 11 . The similar k range between the se 46 studies indicates that the current risk model refle cts the wide range of L. monocytogenes genotypic subtypes present in foods. It was expected that, because of the inc reased listeriosis risk faced by cancer patients (77, 130) , k values for cancer patients would be higher than those for the general at - risk population. However, Pouillot et al. (146) found that mean k values for different immunocompromising conditions were within the same range described here. Some of the k values found in this and previous studies imply that a dose larger than 10 9 cfu /g would be required to produce substantial risk. As the FAO/WHO noted in their risk assessment (199) , this is hypothetical, and one should conclude that most of the population w ill not devel op invasive listeriosis , despite the high dose. They also speculated that this contributes to the sporadic nature of listeriosis. A n average of 245 listeriosis cases per year occurred i n France from 2001 and 2008, with 84 concurrent cancer and listeriosis cases (77) , indicating that about 34% of listeriosis cases were in cancer patients. A total of 660 cases of invasive listeriosis were reported in th e United States in 2019 (32) , with about 34% (224) involving cancer patients based on the French estimate . Pinner et al. (143) rators and found that 32% of the positive samples were vegetables. Therefor e, for convenience it can be assumed that 71 of the 224 concurrent listeriosis cases were due to vegetables. It is estimated that 650,000 cancer patients are treated with chemothera py in the U.S. per year (34) . When the median daily risk for refrigerated, untreated salad (6.086 × 10 - 10 for k approach 2) was compounded for a year (using Equation 3) and multiplied by the 650,000 patients undergoing chemotherapy, the result was 0.14 case s of listeriosis. While it is unlikely that all 71 expected cases from vegetables can be attributed to raw salad products, the estimated 0.14 cases still appears low . This could be due to simplification of the model, particularly in the exposure assessment . For instance, only one 47 exposure route (irrigation water) was considered for tomatoes for ease of calculations, even though it is possible for tomatoes to become contaminated through soil, plastic mulch, during harvest , and during postharvest handling (17 9 ) . Additionally, contamination and growth can occur during packing and processing (6, 147) , but this was not considered. This model also did not account for dynamic growth rates, which would have increased predicted growth during early storage. Considerin g the conservative assumptions in the exposure assessment (use of limit of detection instead of zero in concentration distributions and assuming all irrigation water contacts the tomatoes) this underestimation is even more profound , a s it should represent t he high end of risk . Clearly, a more robust model that incorporates adequate growth and exposure data is needed. Information on the reduction of L. monocytogenes on produce by blanching is limited with data from only two studies included in this analysis. Reductions of L. monocytogenes on tomatoes after (36) data for peas (about 7.5 to 8.3 log cfu /g) and potatoes (about 7.9 to 8.8 log cfu /g), which is only feasibl e in cases of extreme contamination. Th ese results led to relatively low exposure doses , which were reflected in the low risk calculations. Therefore, while blanching was the most effective treatment in reducing risk, its perceived efficacy for tomatoes li kely was influenced by biased data. Mor e complete data are needed to better assess the listeriosis risk posed by blanched produce. Nonetheless, r isk calculated in the present study is consistent with past risk assessments and gives insight into the severit y of impacts that immunosuppression dur ing cancer treatment might have on the risk of foodborne illness . The 2003 FDA/FSIS assessment (186) calculated the risk per serving of refrigerated, untreated vegetables for perinates (4.8 × 10 - 10 ), the elderly (8.2 × 10 - 12 ), and intermediate - aged individ uals (8.4 × 10 - 13 ). Perinatal risk was analogous to 48 refrigerated, untreated salad risk in the present study (5.465 × 10 - 10 to 6.086 × 10 - 10 ), reinforcing that adults with cancer have a significantly greater listeriosis risk than those with other immunocomp romising conditions. Ding et al. (49) presented a daily median risk of 2.47 × 10 - 6 for high - risk groups who consum ed refrigerated lettuce that had been soaked in tap water. They noted that the model was oversimplified and overestimated the actual number o f listeriosis cases in Korea. The corresponding risk from the present study was much lower, ranging from 1.032 × 10 - 10 to 1.135 × 10 - 10 . The discrepancy can be attributed to their model using L. monocytogenes contamination data from lettuce at the farm wit hout accounting for further processing, during which the product would typically undergo a chlorine treatment, subs tantially reducing the pathogen concentration (16, 17, 139) . The current study used real or calculated L. monocytogenes concentration data fr om retail, so this step was incorporated . Carrasco et al. (29) conducted a risk assessment on RTE salads in Spain a nd determined the mean risk per serving to range from 2.40 × 10 - 2 to 2.60 × 10 - 2 for high - risk populations. Th eir calculated risk is much gre ater than the mean daily risk for refrigerated, untreated salad in this study (2.456 × 10 - 7 to 4.451 × 10 - 6 ) ; however, a different dose - response equation (and therefore risk distributions) was used. It is typically inappropriate to represent ri sk using a m ean because risk distributions are often highly skewed , which heavily biases the mean. This bias influences the risk and can lead to overly conservative risk management strategies, such as the neutropenic diet. Also, Carrasco et al. (29) recogn ized that be cause the ir conservative growth model did not account for the slower growth under retail conditions, their model overpredicted the number of listeriosis cases by about three orders of magnitude (when compared to epidemiological data), resulting in an infla ted daily risk. 49 When salad ingredients were refrigerated and rinsed, which is the current FDA recommendation (183) , median computed risk was remarkably low (about 5 × 10 - 10 , or 1 in 2 trillion). I n nosocomial listeriosis outbreaks in which the food vehicle was identified, investigations revealed temperature abuse of the food and/or lack of hospital and patient - specific food safety guidelines (40, 160) . These results suggest that dietary inclusion of produce might be safe when appropriate food safety guidel ines are strictly followed . The risks of chemotherapy - related morbidities , such as venous thromboembolism (97) and osteonecrosis (7) , which can be disabling and/or lethal, are far greater (2.2 × 1 0 - 2 and 1.1 × 10 - 2 , respectively) and are accepted by patien ts at the onset of treatment. However, the benefit of chemotherapy treatment is undoubtedly greater than the nutritional and wellness benefits from consuming raw produce. It would be imprudent to make a risk management decision without first quantifying th ese benefits in decision analysis. Thus, while the risk of listeriosis from salad consumption in this study was small, further work is needed to determine if the risk is acceptable fo r the target population . S ensitivity analysis revealed that the model va riables with the greatest impact on the result changed depending on the use of blanching as a treatment, because as previously noted, predicted L. monocytogenes reductions after blan ching tomatoes and cucumbers generally removed almost all L. monocytogene s cells. In all scenarios, k was a highly influential parameter, which was expected, as it varies considerably with individual physiology, pathogen strain , and the host - pathogen inter action . The i nitial concentration of L. monocytogenes in leafy greens was also consistently a key parameter. For all products, median initial concentrations were similar, but because the distribution in leafy greens included more extreme values, the Listeria concentration was occasionally much higher than for other products, wh ich was carried through the model. Because this distribution was fit to the most extensive, thorough, dataset used in the 50 model, t he large effect on the result can be ascribed to inherent variability. In salad s containing blanched tomatoes and cucumbers, l eafy green consumption and post - retail growth were prominent parameters. This is reasonable because leafy greens were the only unb lanched product, and therefore likely the main contributor of L. monocytogenes . Both these distributions included data from mu ltiple (>5) studies, so the influence is due to innate variability. Because greens were a major contributor in such cases, the ri sk model was rerun for salads composed of solely tomatoes and cucumbers. Median risk for a refrigerated, blanched, salad decre ased from 3.3 × 10 - 10 to 4.9 × 10 - 14 . T his difference was less (approximately one order of mag nitude) for all other treatments. However, this suggests that for maximum risk reduction, it may beneficial to exclude products that cannot be blanched. Risk and exposure dose distributions for the tomato and cucumber salad are shown in Appendix D. For products that were not blanched, consumption of both leafy greens and tomatoes were influential parameters. This is plausible, as individual diets are variabl e , par ticularly during cancer treatment when food aversions are common. However, because consumption data were from self - report ing surveys, patient estimates may lack precision and contribute to uncertainty. Because the survey was conducted amongst outpatient pa tients not following neutropenic diets, the results are likely ap plicable to similar patients treated without diet restrictions. In hospitals that enforce the neutropenic diet, salad ingredient consumption would be lower and likely less variable. A nother k ey variable was consumer storage time, which was based on data fr om the FDA/FSIS 2003 risk assessment (186) . Collecting more data for this distribution would reduce uncertainty, but some inherent variability would always remain, as this is another paramete r that varies from person to person. 51 The current model does not account for the positive effects that consuming produce has on the immune system (5, 26, 30, 44, 72, 76, 107, 116, 124, 125, 167) , which would theoretically reduce infection susceptibility. Wh ile these effects are widely known, t hey have not been quantified in a way that translates to risk analysis. Quantitatively evaluating these effects could provide further justification for produce inclusion in the diets of cancer patients and be utilized i n decision analysis necessary to mak e informed risk management decisions . Furthermore, food safety risk communication for cancer patients is currently inadequate; 34% of cancer patients know they face an increased risk of foodborne illness (55) , and the FD People with Canc (183) does not address food safety guidelines much beyond reinforcement or amplification of standard recommendations for healthy individuals. Therefore, an important future step for this work would involve the devel opment of patient - centered informatio nal material that accurately communicates the risk reported in this study. 4.4 C onclusions The model presented in this study is the first to consider the unique, increased listeriosis risk faced by cancer patients, whic h was shown to be similar to that of perinates. Simplification of the exposure assessment led the model to slightly underestimate cases when compared to an appraisal of epidemiological data but results generally agree d with the few past studies on listerio sis risk from salads. The strong infl uence of the k parameter on risk combined with its high variability may indicate that a more advanced model would more accurately estimate risk. The median listeriosis risk resulting from consuming refrigerated, untreat ed salad was far below other chemotherapy - related risks cancer patients routinely accept. This risk is even lower when salad components are blanched , and lower still when products that cannot be blanched are excluded from the salad , although the efficacy o f blanching should be reevalu ated once more 52 data become available. Consequently, this study supports the body of literature that questions the infection - reduction effectiveness of modified diets that exclude raw produce , if appropriate and sufficient patho gen control and reduction str ategies are employed . However, quantitative decision analysis is needed to make valid risk management decision s . Future work will includ e adding more exposure scenarios in the tomato exposure analysis, using decision analysis t o compar e the benefits and risk of consuming raw produc e , and creating effective risk communication materials for both patients and their caretakers. 53 CHAPTER 5: CONCLUSIONS T his thesis presented the results from two novel studies : the effectiveness of kitchen - scale treatments in decreasing L. monocytogenes on fresh produce , and the development of a listeriosis risk model for cancer patients who con sume these products in prepared salads. Cancer patients are a vulnerable population cu rrently not adequa tely served by available food safety information . This thesis found that rinsing, the current FDA recommendation for home produce preparation, is minimally effective in reducing L. mono cytogenes populations and subsequent listeriosis risk for various types of produce . Other techniques seldom tested until the current study , such as blanching and/or peeling , were more effective in reducing L. monocytogenes and risk . However, more data are needed to validate these findings. L. monocytogenes was particularly difficult to remove from celery due to internalization, as may be the case with other porous products . Yet , a comparable porous product, leafy greens, was considered in the risk assessment, resulting in low overall risk. However, b ecause they could not be blanched, leafy greens were the main contributor to risk of L. monocytogenes in salads with blanched tomatoes and cucumber s . Once they were removed, risk decreased substantially. Decision analysis is needed to determine whether such a strategy would be ben eficial for cancer patients. Th e risk model predicted a daily median listeriosis risk congruent with that previously reported for perinates . This is suggestive of the unique, inc reased risk that cancer patients face , and confirms the need for specialized risk analys es and food safety interventions for this group. Present hospital - enforced food safety intervention methods , such as the neutropenic diet, are restrictive and not based on quantitative risk analysis . The second portion of the study found that me dian risk s from r efrigerated, untreated , and blanched salad s w ere six to nine orders of 54 magnitude lower , respectively, than risk s from debilitating a nd /or potentially lethal conditions resulting from chemotherapy , which are regularly accepted by cancer pat ients . This result suggest s that excluding produce as a risk - reduction strategy should be reconsidered , and that shifting the focus to proper storage and hyper - hygienic preparation might result in sufficiently acceptable risk. This can be assessed in futur e decision analysis. This thesis considered one pathogen, L. monocytogenes , with particular evidence o f concern. Other foodborne pathogens pose a risk to cancer patients who consume produce , and while they were not examined in the present study, result s ma y be applicable to such situations. 55 CHAPTER 6: FUTURE WORK The present study compared the efficacy of kitchen - scale microbial reduction strategies for apples, cucumbers, and celery . To better account for differences in produce morphology, f uture studies should include additional products, both porous and non - porou s, as growing conditions and surface morphology play large role s in L. monocytogenes growth and removal. These results could be used to inform product - specific risk models and provide greater in sight into safe dietary produce inclusion . This thesis also did not consider declines in nutrient content and product integrity resulting from produce treatment, which affect the value of produce inclusion in terms of the immune - system and mental health benefits for patients. Investigating these f ood science a nd nutrition facets would help to help maximize the impact of this multidisciplinary project. The exposure assessment c onducted in this study was simplified and excluded several key routes of conta mination. An improved version would consider contamination o f tomatoes from soil, plastic mulch, during harvest, and in the packing house . It would also consider the various environments in which tomatoes are grown (green houses, fields, etc.), and how the y affect risk. M odeling growth on all products with dynamic growth rates would support better understand ing of how transportation times and consumer storage affect L. monocytogenes concentration and risk , lending to more factual recommendations. Finally, it is crucial to use decision analysis methods to quantify the nutrition and wellness benefits associated with consuming raw produce. Doing so will facilitate the development of accurate risk management strategies and help determine acceptable ris k for can cer patients. 56 The ultimate goal of this research , which will be carried out in subsequent doctoral work, is the development of improved training and risk communication materials for hospital staff, caretakers, and patients , and quantit ative assessment of the risk - reduction benefits of these interventions . This will involve retesting the efficacy of produce treatments when performed by these groups and working with risk communication experts to create effective communication materials. S uch materials could influence the widespread use of the neutropenic diet and hopefully improve patient diet s and overall health outcomes . 57 APPENDICES 58 Appendix A: Produce Preparation Experiment Raw Data Table 9 : Apple sample and dilution weights Treatm ent Replication Subsample Weight (g) Weight + Dilution (g) Co ntrol 1 1 38.41 193.01 2 30.04 148.92 2 1 34.64 172.95 2 28.79 143.18 3 1 32.96 165.81 2 29.67 148.16 Sanitizer soak 1 1 35.37 175.82 2 25.84 129.97 2 1 28.98 143.00 2 42.8 0 213.43 3 1 22.88 124.56 2 32.94 164.93 Tap water rinse 1 1 28.91 144.61 2 38.81 193.57 2 1 42.80 213.43 2 23.90 119.96 3 1 28.67 140.97 2 24.49 122.87 Tap water soak 1 1 33.90 167.90 2 28.68 136.28 2 1 40.58 200.75 2 29.71 148 .47 3 1 35.40 177 .12 2 26.77 133.95 Blanch 1 1 34.18 170.05 2 37.36 186.06 2 1 33.03 164.84 2 32.04 160.18 3 1 34.95 173.80 2 35.61 178.12 Rinse+peel 1 1 25.14 142.08 2 18.49 91.90 2 1 17.77 89.11 2 24.50 122.52 3 1 20.02 99.8 3 2 33.26 167.00 Blanch+peel 1 1 19.20 95.92 2 19.25 95.77 2 1 22.52 112.55 2 25.87 130.19 3 1 14.89 75.45 2 24.01 120.18 59 Table 10 : Plate counts for apple treatments * Treatment Replication Subsample Number of Seri al Dilution s ** +1 0 - 1 - 2 - 3 Co ntrol 1 1 TNTC TNTC 35, 47 2, 6 2 TNTC TNTC 86, 100 7, 12 2 1 TNTC TNTC 46, 62 6, 7 2 TNTC 46, 65 8, 9 0, 2 3 1 TNTC TNTC 23, 26 38, 7 2 TNTC 155, 160 15, 15 1, 2 Sanitizer soak 1 1 TNTC TNTC 89, 91 5 , 10 2 TNTC TNTC 32, 40 5, 7 2 1 TNTC 37, 28 1, 3 0, 2 2 TNTC TNTC 35, 39 0, 2 3 1 TNTC TNTC 25, 31 0, 3 2 TNTC 140, 160 13, 24 0, 3 Tap water rinse 1 1 TNTC TNTC 52, 77 2, 7 2 TNTC TNTC 53, 55 2, 10 2 1 TNTC TNTC 39, 60 2, 6 2 TNTC 23, 39 2, 0 1, 0 3 1 54, 81 12, 14 2, 0 0, 0 2 TNTC TNTC 140, 160 14, 20 Tap water soak 1 1 TNTC TNTC 191, 193 15, 20 2 TNTC TNTC 23, 47 4, 2 2 1 TNTC 143, 198 18, 21 0, 0 2 TNTC TNTC 36, 35 2, 6 3 1 TNTC 64, 76 4, 5 0, 0 2 TNTC TNTC 63, 93 3, 4 Blanch 1 1 9, 15 2, 4 0, 0 2 2, 4 0, 0 0, 0 2 1 9, 4 0, 1 0, 0 2 2, 1 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 2 0, 2 0, 0 0, 0 Rinse+peel 1 1 4, 11 1, 1 0, 0 0, 0 2 38, 36 3, 6 0, 0 0, 0 2 1 23, 50 2, 8 4, 2 0, 0 2 74, 51 5, 9 0, 1 0, 0 3 1 TNTC TNTC 36, 39 2, 3 2 TNTC 219, 250 21, 26 1, 2 Blanch+peel 1 1 7, 9 0, 0 0, 0 0, 0 2 1, 0 0, 0 0, 0 0, 0 2 1 82, 77 6, 9 0, 0 0, 0 2 9, 13 0, 0 0, 0 0, 0 3 1 TNTC 96, 109 11, 12 3, 1 2 0, 0 0, 0 0, 0 0, 0 * Two samples were plated per dilution **TNTC = Too numerous to count 60 Table 11 : Cucumber sample and dilution weights Treatment Replication Subsample Weight (g) Weight + Dilution (g) Co ntrol 1 1 14.00 70.39 2 14 .54 72.39 2 1 15.41 76.70 2 16.52 87.32 3 1 12.33 61.19 2 10.76 54.00 Sanitizer soak 1 1 17.82 88.59 2 11.32 56.20 2 1 10.15 50.24 2 14.22 71.36 3 1 14.64 73.17 2 15.36 76.78 Tap water rinse 1 1 14.51 72.58 2 15.06 75.92 2 1 1 8.32 93.10 2 14.51 72.25 3 1 14.57 76.75 2 15.50 80.73 Tap water soak 1 1 15.35 76.54 2 12.68 64.12 2 1 11.57 57.40 2 15.43 76.52 3 1 15.43 97.27 2 19.53 100.62 Blanch 1 1 12.65 63.7 7 2 14.92 74.51 2 1 14.94 74.52 2 11.90 59. 49 3 1 14.26 70.89 2 16.70 82.41 Rinse+peel 1 1 10.39 51.75 2 9.76 48.69 2 1 10.36 51.48 2 11.46 57.08 3 1 9.32 46.10 2 12.80 65.10 Blanch+peel 1 1 9.28 46.38 2 10.23 50.28 2 1 13.71 68.62 2 10.37 51.48 3 1 10.05 50.18 2 10 .72 52.99 61 Table 12 : Plate counts for cucumber treatments* Treatment Replication Subsample Number of Serial Dilutions** +1 0 - 1 - 2 - 3 Co ntrol 1 1 TNTC TNCT 83, 85 6, 6 2 TNTC TNTC 105, 114 8, 14 2 1 TNTC TNTC 112, 142 8, 12 2 TNTC TNCT 110, 122 8, 10 3 1 TNTC TNTC 36, 70 5, 8 2 TNTC TNTC 40, 44 2, 2 Sanitizer soak 1 1 TNTC TNTC 39, 37 4, 4 2 TNTC 90, 103 8, 11 0, 1 2 1 TNTC TNTC 45, 38 2, 3 2 TNTC TNTC 31, 25 0, 2 3 1 TNTC TNTC 40, 44 2, 6 2 TNTC 183, 175 21, 22 3, 3 Tap water rinse 1 1 TNTC TNTC 37, 51 4, 8 2 TNTC TNTC 49, 61 3, 5 2 1 TNTC TNTC 25, 31 1, 5 2 TNTC TNTC 57, 65 1, 2 3 1 TNTC 143, 196 10, 21 1, 2 2 TNTC TNTC 30, 33 2, 3 Tap water soak 1 1 TNTC TNTC 168, 179 19, 24 2 TNTC TNTC TNTC 33, 29 2 1 TNTC TNTC TNTC 52, 73 2 TNTC TNTC TNTC 50, 42 3 1 TNTC TNTC 65, 78 7, 8 2 TNTC TNTC 73, 86 4, 11 Blanch 1 1 38, 51 3, 5 1, 0 2 0, 0 0, 0 0, 0 2 1 5, 7 1, 1 0, 0 2 1, 0 0, 0 0, 0 3 1 3, 4 0, 0 0, 0 2 TNTC 59, 80 3, 5 Rinse+peel 1 1 TNTC TNTC TNTC 78, 80 2 TNTC 80, 91 11, 15 1, 0 2 1 TNTC 166, 163 18, 23 0, 1 2 TNTC 177, 168 18, 18 1, 0 3 1 TNTC TNTC 226, 195 17, 22 2 TNTC 31, 36 2, 0 1, 3 Blanch+peel 1 1 0 , 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 2 1 3, 4 1, 0 0, 0 2 1 0, 0 0, 0 3 1 4, 5 1, 3 0, 0 2 0 0, 0 0, 0 *Two samples were plated per dilution **TNTC = Too numerous to count 62 Table 13 : C elery sample and dilution we ights Treatment Replication Subsample Weight (g) Weight + Dilution (g) Co ntrol 1 1 13.30 66.91 2 8.29 42.18 2 1 19.39 97.07 2 11.39 57.63 3 1 12.74 63.19 2 15.29 77.47 Sanitizer soak 1 1 11.12 54.97 2 18.84 95.31 2 1 12.56 62.75 2 14 .33 71.75 3 1 19.40 102.39 2 17.77 88.00 Tap water rinse 1 1 14.68 54.97 2 16.05 80 .54 2 1 23.74 117.17 2 21.52 107.98 3 1 10.22 51.24 2 13.19 65.58 Tap water soak 1 1 13.97 69.90 2 9.66 49.29 2 1 15.18 76.16 2 14.41 74.16 3 1 9.76 48.99 2 23.85 119.26 Blanch 1 1 16.01 79.98 2 16.91 85.17 2 1 23.14 115.92 2 16.62 81.25 3 1 16.03 80.47 2 15.38 76.24 63 Table 14 : Plate counts for celery treatments * Treatment Replication Subsample Number o f Serial Dilutions** - 1 - 2 - 3 - 4 - 5 Co ntrol 1 1 TNTC TNTC TNTC 79, 97 2 TNTC TNTC 273, 292 28, 21 2 1 TNTC 161, 171 15, 20 2, 3 2 TNTC 257, 221 28, 2 0 4, 1 3 1 TNTC 173, 178 11, 14 0, 1 2 TNTC 236, 263 16, 23 1, 2 Sanitizer soak 1 1 TNTC 173, 190 18, 19 3, 1 2 TNTC TNTC TNTC 55, 50 2 1 227, 230 18, 34 3, 3 0, 0 2 TNTC 51, 60 6, 9 0, 0 3 1 108, 121 13, 14 1, 0 0, 0 2 TNTC TNTC 67, 80 8, 10 Tap water rinse 1 1 TNTC TNTC TNTC 127, 152 2 TNTC TNTC TNTC 47, 55 2 1 126, 127 13, 8 0, 0 0, 0 2 196, 210 12, 27 1, 0 1, 0 3 1 TNTC 127, 143 11, 17 1, 0 2 115, 143 11, 15 0, 4 0, 0 Tap water soak 1 1 TNTC TNTC 65, 69 6, 7 2 TNTC TNTC 70, 74 5, 6 2 1 TNTC 52, 72 7, 7 2, 1 2 TNTC 29, 38 4, 5 0, 0 3 1 TNTC TNTC 30, 39 3, 2 2 TNTC 27, 29 5, 1 0, 0 Blanch 1 1 TNTC TNTC 220, 242 2 TNTC TNTC 278, 296 2 1 TNTC 164, 168 11, 17 1, 4 2 TNTC 84, 115 9, 9 0, 0 3 1 125, 99 6, 15 2, 0 0, 0 2 226, 241 17, 22 1, 2 0, 0 *Two samples we re plated per dilution **TNTC = Too numerous to count 64 Table 15 : Celery inoculum internalization experiment sample weights and dilutions Distance from inoculated end ( cm) Replication Subsample Weight (g) Weight + Dilution (g) 0.1 1 1 0.92 5.30 2 1.01 6.79 2 1 0.40 2.76 2 0.58 3.02 3 1 0.61 3.11 2 0.39 3.1 1.1 1 1 1.27 6.25 2 1.99 6.79 2 1 1.58 8.25 2 0.93 4.60 3 1 1.58 7.94 2 1.35 6.78 2.1 1 1 1.53 7.67 2 2.36 11.88 2 1 1.50 8.45 2 1.25 6.45 3 1 1.78 8.88 2 1.45 7.24 3.1 1 1 1.54 8.99 2 2.69 13.55 2 1 1.46 7.29 2 1.28 6.35 3 1 1.77 8.97 2 1.80 9.00 4.1 1 1 1.58 8.00 2 1.11 6.20 2 1 1.51 7.83 2 2.11 10.68 3 1 1.86 10.00 2 1.63 8.18 5.1 1 1 1.55 8.05 2 2.48 13.45 2 1 1.41 8.20 2 1.01 5.85 3 1 1.85 9.48 2 1.77 8.99 6.1 1 1 1.74 9.13 2 2.55 13.70 2 1 1.31 6.67 2 1.07 5.52 3 1 1.71 8.53 2 1.85 9.39 65 Table 15 Distance from inoculated end (cm) Replication Subsample Weight (g) Weight + Dilution (g) 7.1 1 1 1.12 5.62 2 2.41 12.55 2 1 1.31 7.34 2 1.23 7.61 3 1 1.92 9.87 2 1.91 9.67 8.1 1 1 1.20 6.13 2 2.37 12.10 2 1 1.43 7.69 2 1.15 5.46 3 1 1.78 8.96 2 1.51 7.93 9.1 1 1 1.41 7.08 2 2.46 12.63 2 1 1.42 7.13 2 0.96 5.16 3 1 1.51 7.50 2 2.42 11.99 66 Table 16 : Plate counts for celery inoculum internalization experiment * Distance from Inoculated End (cm) Replication Subsample Number of Serial Dilutions** - 0 - 1 - 2 - 3 - 4 0.1 1 1 TNTC TNTC TNTC 179, 176 22, 18 2 TNTC TNTC TNTC TNTC 32, 34 2 1 TNTC TNTC TNTC TNTC 44, 68 2 TNTC TNTC TNTC TNTC 88, 88 3 1 TNTC TNTC TNTC TNTC 61, 80 2 TNTC TNTC TNTC TNTC 55, 69 1.1 1 1 TNTC 170, 167 11, 25 1, 6 2 TNTC TNTC TNTC 44, 46 2 1 TNTC TNTC TNTC 37, 42 2 TNTC TNTC TNTC 50, 52 3 1 TNTC TNTC TNTC 40, 44 2 TNTC TNTC TNTC 69, 78 2.1 1 1 2, 2 1, 0 0, 0 0, 0 2 TNTC 70, 71 5, 9 1, 0 2 1 22, 26 3, 4 0, 0 0,0 2 11, 5 0, 2 0, 0 0, 0 3 1 TN TC 115, 132 14, 18 1, 1 2 TNTC TNTC 87, 102 6, 14 3.1 1 1 168, 148 13, 19 2, 0 0, 0 2 TNTC 51, 61 2, 6 1, 1 2 1 0, 0 0, 0 0, 0 0, 0 2 4, 6 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 TNTC TNTC 32, 42 5, 7 4.1 1 1 2, 1 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 5.1 1 1 22, 24 3, 1 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 0 0, 0 0, 1 2 0, 0 0, 1 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 6.1 1 1 TNTC 46, 63 9, 9 1, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 0 0, 0 0, 0 2 0,0 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 1 0, 0 0, 0 67 *Two samples were plated per dilution **TNTC = Too numerous to count Distance from Inoculated End (cm) Replication Sub sample Number of Serial Dilutions** - 0 - 1 - 2 - 3 - 4 7.1 1 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 0 0, 0 0, 0 2 0, 1 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 1 0, 0 0, 0 0, 0 8.1 1 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 9.1 1 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 2 1 0, 0 0, 1 0, 0 0, 0 2 0, 0 0, 0 0, 0 0, 0 3 1 0, 0 0, 0 0, 0 0, 0 2 0, 0 0, 1 0, 0 0, 0 68 Table 17 : Apple peeling experiment sample weights and dilutions Peeler Replication Subsample Weight (g) Weight + Dilution (g) Standa rd 1 1 24.11 120.55 handheld 2 23.95 118.24 2 1 23.76 119.11 2 21.3 106.6 3 1 20.6 101.57 2 22.83 113.8 Apple 1 1 14.38 72.13 2 11.96 59.8 2 1 18.24 91.32 2 11.08 55.42 3 1 21.38 106.89 2 13.78 68.90 Positive 1 1 28.08 141.43 c ontrol 2 29.06 145.35 2 1 27.67 138.20 2 30.62 153.39 3 1 36.19 179.99 2 32.60 163.10 69 Table 18 : Apple peeling experiment plate counts* Peeler Replication Subsample Number of Serial Dilutions** + 1 - 0 - 1 - 2 - 3 Stan dard 1 1 TNTC 103, 117 8, 10 0, 0 handheld 2 38, 41 3, 7 0, 1 0, 0 2 1 TNTC TNTC 55, 70 10, 2 2 TNTC TNTC 48, 51 7, 8 3 1 20, 28 2, 5 1, 0 0, 0 2 65, 43 7, 13 0, 2 0, 0 Apple 1 1 83, 56 6, 9 1, 1 0, 0 2 33, 35 1, 3 0, 0 0, 0 2 1 T NTC 77, 81 7, 11 1, 2 2 TNTC TNTC 51, 65 4, 6 3 1 67, 72 7, 13 1, 2 0, 0 2 TNTC 38, 41 4, 6 0, 0 Positive 1 1 TNTC 109, 114 14, 15 2, 1 control 2 TNTC 195, 136 18, 36 2, 5 2 1 TNTC 89, 67 8, 9 2, 2 2 TNTC 214, 249 20, 16 0, 0 3 1 TNTC 22, 27 2, 11 1, 2 2 TNTC TNTC 113, 98 13, 16 *Two samples were plated per dilution **TNTC = Too numerous to count 70 Appendix B: L. monocytogenes Exposure Dose Summary Table 19 : Daily exposure dose ( cfu /g) for L. monocytog enes in fresh salad Treatment Storage Temp 5% 50% 95% Control Refrigerated 3.3 6.3 E01 1.5 E03 High 9.4 4. E02 1.5 E05 Sanitizer soak Refrigerated 1.6 E - 01 3.4 6.0 E01 High 4.7 E - 01 2.5 E01 7.5 E03 Tap water rinse Refrigerated 1.2 E - 01 2.3 6.5 E01 High 3.2 E - 01 3.4 E01 6.8 E03 Tap water soak Refrigerated 5. 2E - 01 1.2 E01 2.7 E02 High 1.5 8.3 E01 3.9 E04 Flash blanch (rinse greens) Refrigerated 5.2 E - 03 2.6 E - 01 5.1 E01 High 1.3 E - 01 1.2 3.3 E02 Peel (rinse greens) Refrigerated 3.6 E - 02 1.2 6.3 E01 High 1.2 E - 01 7.3 2.8 E03 71 Appendix C: L. monocytogenes Expo sure Dose Distributions Figure 7 : Exposure dose histogram for refrigerated, untreated salad Figure 8 : Exposure dose histogram for elevated temperature, untreated salad 72 Figure 9 : Exposure dose histogram for refrigerated salad treated with sanitizer soak Figure 10 : Exposure dose histogra m for elevated temperature salad treated with sanitizer soak 73 Figure 12 : Exposure dose histogram for elevated temperat ure salad treated with tap water rinse Figure 11 : Exposure dose histogram for refrigerated salad treated with tap water rinse 74 Figure 13 : Exposure dose his togram for refrigerated salad treated with tap water soak Figure 14 : Exposure dose histogram for elevated temperature salad treated with tap water soak 75 Figure 1 6 : Exposure dose histogram for elevated temperature salad treated by blanching Figure 15 : Exposure dose histogram for refrigerated salad treated by bl anching 76 Figure 18 : Exposu re dose histogram for elevated temperature salad treated by peeling Figure 17 : Exposure dose histogram for refrigerated salad treated by peeling 77 Appendix D: L. monocytogenes Exposure Dose and Risk Dist ributions , Salad Without Lettuce Table 20 : Daily exposure dose ( cfu ) for L. monocytogenes in cucumber and tomato salad Treatment Storage Temp 5% 50% 95% Control Refrigerated 6.9 E - 01 2.8 E01 3.8 E02 High 2.1 1.7 E02 1.3 E05 Sanitize r soak Refrigerated 1.7 E - 02 7.8 E - 01 1.4 E01 High 4.6 E - 02 4.9 4.4 E03 Tap water rinse Refrigerated 2.3 E - 03 9.9 E - 01 1.6 E01 High 6.0 E - 02 6.2 5.4 E03 Tap water soak Refrigerated 1.3 E - 01 6.0 9.8 E01 High 3.4 E - 01 3.8 E01 3.5 E04 Flash blanch (rinse greens) Re frigerated 7.9 E - 07 2.1 E - 04 3.9 E - 02 High 2.7 E - 06 1.6 E - 03 3.5 Peel (rinse greens) Refrigerated 2.5 E - 03 2.0 E - 01 1.2 E01 High 8.5 E - 03 1.5 1.7 E03 78 Table 21 : Risk per chemotherapy cycle for each risk management strategy and k approach, cucumber and tomato salad Treatment Storage Temp k Approach 5% 50% 95% Control Refrigerated 1 6.4 E - 1 1 5.5 E - 0 9 2.9 E - 07 2 1.3 E - 11 5 . 5 E - 0 9 5.0 E - 0 6 High 1 2.2 E - 10 4.1 E - 0 8 5.2 E - 05 2 4.6 E - 1 1 5 .3E - 0 8 3.2 E - 04 Sanitizer soak Refrigerated 1 1. 7 E - 1 2 1.6 E - 10 9. 8 E - 0 9 2 3.3 E - 1 3 1.6 E - 10 1. 6 E - 0 7 High 1 5.4 E - 1 2 1.2 E - 09 1.6 E - 06 2 1.1 E - 12 1.6 E - 09 1.1 E - 05 Tap water rinse Refrig erated 1 2.1 E - 12 1.9 E - 10 1.2 E - 08 2 3.9 E - 1 3 2.0 E - 10 1.9 E - 07 High 1 6.9 E - 1 2 1.5 E - 09 2.3 E - 06 2 1.4E - 12 2.0 E - 09 1.4 E - 05 Tap water soak Refrigerated 1 1.2 E - 11 1.2 E - 09 7.0 E - 0 8 2 2.4 E - 12 1.2 E - 09 1.2 E - 06 High 1 3.9 E - 1 1 9.0 E - 0 9 1. 4 E - 05 2 8.0 E - 1 2 1. 2 E - 08 8.2 E - 0 5 Surface blanch tomato and Refrigerated 1 0.0 4.9 E - 1 4 1.5E - 10 cucumber, rinse greens 2 0.0 4.5E - 14 1.6E - 11 High 1 0.0 3.6E - 13 1.1E - 09 2 0.0 4.4E - 13 7.7E - 09 Peel tomato and cucumber , Refrigerated 1 2.4E - 13 4.3E - 11 6.1E - 09 rinse green s 2 5.0E - 14 4.9E - 11 7.9E - 08 High 1 9.2E - 13 3.4E - 10 5.9E - 07 2 2.0E - 13 4.4E - 10 3.7E - 06 79 REFERENCES 80 REFERENCES 1. 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