i LISTERIA MONOCYTOGEN ES TRANSFER DURING SLI CING AS IMPACTED BY INTRINSIC CHARACTERISTICS OF FRESH PRODUCE By Hamoud Abdulaziz Alnughaymishi A DISSERTATION Submitted to Michigan State University in partial fulfi llment of the requirements for the degree of Food Science Doctor of Philosophy 2018 ii ABSTRACT LISTERIA MONOCYTOGEN ES TRANSFER DURING SLI CING AS IMPACTED BY INTRINSIC CHARACTERISTICS OF F RESH PRODUCE B y Hamoud Abdulaziz Alnughaymishi Listeria monocytogenes outbreaks and recalls associated with fresh - cut produce are a major public health concern. Several studies have investigated th e extent of microbial cross - contamination during slicing of fresh - cut produce . H owever , few have examined how product characteristics influence pathogen transfer. In response, a series of studies were conducted to assess the impact of inherent product char acteristics on Listeria transfer during mechanical slicing. Using cucumbers and zucchini squash as model products based on their inherent compositional differences , the transfer of L. monocytogenes from inoculated cucumber s and zucchini to various surface s of rotating and stationary slicers was assess ed . After slicing one inoculated product followed by fifteen uninoculated ones , Listeria populations on different parts of the stationary slicer decreased significantly ( P 0.05 ). When the spread of Listeria was assessed during slicing of zucchini and cucumbers at different slicing speed s , both high and low speed resulted in statistically similar ( P > 0.05) . Another objective of this study was to evaluate the effect of wate r content on transfer of Listeria during slicing. Floral foam , to which different amounts of water were added, was used as a model system in order to obtain different percent moisture levels of 95.1 , 96.7 and , 97.6 % under the same conditions. The decay rat e observed at all three percent moisture conditions were statistically similar ( P > 0.05) . The next study focused on quantifying the impact of various physicochemical characteristics (water content, pH, cutting force , soluble solids content, surface hydr ophobicity , iii and surface roughness) of produce (pears, onions, radishes, tomatoes, potatoes, carrots, zucchini, cantaloupe, apple , cucumber , gray zucchini and, sweet potatoes ) on L. monocytogenes transfer during slicing. To evaluate the effect o f pear firmness on bacterial transfer, three pear firmness categories were determined ; firm (10 - 15 N), medium (6 - 9 N) , and soft (< 6 N). For pear slicing, one pear was dip - inoculated with an avirulent L. monocytogenes cocktail (M3, J 22F and J29H) as well as a 3 - strain Salmonella cocktail (Montevideo, Poona, Newport) at ~ 7 .5 log CFU/ pear and air - dried in a bio - safety cabinet for 1 h before slicing . The inoculated product was sliced using a NEMCO slicer # 59155491, followed by 15 u ninoculated pears, all of which yielded quantifiable numbers of bacteria after slicing. S tatistically similar ( P > 0.05) decay rates were observed for f ir m, medium , and soft pears , respectively . Finally, onions, radishes, tomatoes, potatoes, carrots, zucchini, cantaloupe, apple, sweet potato, gr a y zucchini , and cucumber were assessed for Listeria transfer, after which a two - parameter exponential decay model was fit to the Listeria popu lations obtained during subsequent slicing of 15 uninoculated samples of the same product type . The decay rate (parameter B) ranged from 0.008 ± 0.002 to 0.09 ± 0.01 for cucumbers and radish, respectively . The root mean square error ( RMSE ) ranged from 0.25 to 0.68 log CFU/produ ct across the different types of produce, indicat ing a relatively good fit . When the inherent physicochemical characteristics were fitted into a generalized linear model to describe their impact on the decay rate during slicing, the model was heavily dependent on the product type with a statistical significan ce ( P ) . iv To my parents, Abdulaziz Alnughaymishi and Huda Alshalan, and my wife Balqes Albarrak v ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor Dr. Elliot Ryser for his endless support, guidance, and trust throughout my Ph.D. study, for which I will be forever grateful. Since I joined his lab on A great relationships that will last forever. No matter what time I visited him in his office, he's always there to offer his support. I would also like to thank my committee members Dr. John Linz, Dr. Bradley Marks, Dr. Randy Beaudry and Dr. Leslie Bourquin. They have contributed tremendously to improve my research and study. D espite th e huge number of interaction s between us through committee meetings and one on one meeting s , they provided me with extraordinary support and challenged me and my ideas throughout my Ph . D . which contributed to my evolution as a researcher. I want to thank all the previous lab mates Gordon Davidson, Haiqiang Wang, Lin Ren, Andy Scollon, Victor Jayeola, Ryann Gustafson, Haley Smolinski, Nurul Hawa Ahmad, and Gayathri Gunathilaka for their support and the beautiful memories they helped me create during my st ay at MSU. Special thanks to Ian Hildebrandt, Beatriz Mazon, Francisco Garces - Vega , and Daewoo Pak for the countless meetings we had to help me better understand my data. Lastly, I would like to thank all my family members and friends for their continuou s support and love specially my parents Abdulaziz Alnughaymishi and Huda Alshalan, who always believed in me. I want to thank my wife Balqes Albarrak for all her love and support. She is not only my wife, she is my best friend, partner, my companion and my inspiration. I love you! vi TABLE OF CONTENT S LIST OF TABLES ......................................................................................................... ............ viii LIST OF FIGURES ................................................... ............................................................... . . x KEY TO SYMBOLS AND ABBREVIATIONS .................................................................. . . x ii INTRODUCTION...................................................................... .................................................. 1 CHAPTER 1: Review of Pertinent Literature ................................ ................................ ........... 4 1.1 FRESH - CUT PRODUCE ................................ ................................ ................................ 5 1.2 FOODBORNE DISEASE RELATED TO FRESH PRODUCE ................................ 8 1.3 PRE - HARVEST VS POST - HARVEST CONTAMINATION ................................ ..... 13 1.4 LISTERIA MONOCYTOGENES AND FRESH - CUT PRODUCE ................................ 14 1.5 SALMONELLA AND FRESH - CUT PRODUCE ................................ ........................... 19 1. 1.6 BACTERIAL TRANSFER DURING SLICING AND DICING ................................ .. 23 1.7 FACTORS EFFECTING TRANSFER DURING SLICING ................................ ......... 25 1.8 MODELING OF BACTERIAL TRANSFER DURING SLICING .............................. 29 1.9 QUANTITAVE MICROBIOLOGICAL RISK ASSESSMENT ................................ ... 31 CHAPTER 2 : Microbial Cross - Contamination of Cucumber, Zucchini, and Floral Foam During Slicing as Impacted by Mechanical Slicer Type, Slicing Speed and Water Content ................................ ................................ ................................ ................................ .... 34 2.1 OBJECTIVE ................................ ................................ ................................ ................... 35 2.2 MATERIALS AND METHODS ................................ ................................ ................... 36 2.2.1 Cucumber and zucchini ................................ ................................ ........................... 36 2.2.2 Bacterial strains ................................ ................................ ................................ ....... 36 2.2.3 Identification of conta ct areas between rotating slicer and product: ...................... 37 2.2.4 Listeria distribution on individual slices ................................ ................................ . 37 2.2.5 Listeria transfer from inoculated cucumbers and zucchini to a rotating and stationary hand slicer ................................ ................................ ................................ ............. 38 2.2.6 Cleaning and decontaminating the slicer ................................ ................................ 39 2.2 .7 Listeria transfer from surface - inoculated cucumber and zucchini to the cut surface using a rotating and stationary hand slicer ................................ ................................ ............ 39 2.2.8 Listeria transfer from inoculated to uninoculated cucu mbers and zucchini during sequential slicing using a rotating and stationary slicer ................................ ........................ 40 2.2.9 Impact of cutting speed on L. monocytogenes transfer during slicing .................... 40 2.2.10 Density, cutting force, and water content of cucumbers and zucchini ................... 41 2.2.11 Impacted of water content on bacterial transfer using floral foam as a mod el ....... 42 2.2.12 Microbiological analysis: ................................ ................................ ........................ 43 2.2.13 Statistical analysis ................................ ................................ ................................ ... 43 2.3 RESULTS ................................ ................................ ................................ ....................... 45 2.3.1 Listeria distribution on individual slices ................................ ................................ . 45 vii 2.3.2 Listeria transfer from inoculated produce to a rota ting and stationary hand slicer 46 2.3.3 Listeria transfer from surface - inoculated cucumber and zucchini to the cut surface using a rotating and stationary hand slicer ................................ ................................ ............ 48 2.3.4 Listeria transfer from inoculated to uninoculated cucumbers and zucchini during sequential slicing using a rotating and stationary slicer ................................ ........................ 49 2.3.5 Impact of cutting speed on L. monocytogenes transfer during slicing .................... 51 2.3.6 Produce density, cutting force and water content ................................ ................... 53 2.3.7 Impact of water content on Listeria transfer using floral foam as a model ............ 54 2.4 DISCUSSION: ................................ ................................ ................................ ............... 56 CHAPTER 3 : Quantify Listeria and Salmonella transfer during slicing of different fresh cut produces as impacted by produce firmness and other physiological characteristics ........... 59 3.1 OBJECTIVE ................................ ................................ ................................ ................... 60 3.2 MATERIALS AND METHODS: ................................ ................................ .................. 61 3.2.1 Microbial cross - contamination of pears during slicing as impacted by pear firm z ness ................................ ................................ ................................ ................................ 61 3.2.2 Pears firmness categories ................................ ................................ ........................ 61 3.2.3 Produce selection and slicing ................................ ................................ .................. 62 3.2.4 Bacterial strain and pro duce inoculation ................................ ................................ . 62 3.2.5 Quantify Listeria transfer during slicing of different fresh cut produces ............... 63 3.2.6 Physicochemical chara cteristics measurements of produce ................................ ... 63 3.2.7 Surface roughness determination ................................ ................................ ............ 64 3.2.8 Surface hydrophobicity assay ................................ ................................ ................. 64 3.2.9 Microbiological analysis ................................ ................................ ......................... 64 3.2.10 Statistical analysis ................................ ................................ ................................ ... 65 3.2.11 A primar y exponential decay model ................................ ................................ ....... 66 3.2.12 A secondary multiple linear model ................................ ................................ ......... 66 3.3 RESULTS: ................................ ................................ ................................ ..................... 67 3.3.1 Microbial cross - contamination of pears during slicing as impacted by pear's firmness: ................................ ................................ ................................ ................................ 67 3.3.2 Quantify Listeria transfer during slicing of different fresh cut produ ce: ................ 74 3.4 DISCUSSION: ................................ ................................ ................................ ............... 89 CHAPTER 4 : Conclusions and Recommendations for Future Work ................................ .... 93 4.1 CONCLUSIONS OF THIS DISSERTATION ................................ .............................. 94 4.2 RECOMMENDATIONS FOR FUTURE WORK ................................ ......................... 95 A PPENDICES ................................ ................................ ................................ ............................. 98 5 APPENDIX A : Microbial Cross - Contamination of Cucumber and Zucchini during Slicing as Impacted by Mechanical Slicer Type, Slicing Speed and Water Conten t ........................ 98 6 APPENDIX B : Quantify Listeria transfer during slicing of different fresh cut produces as . impacted by produce firmness and other physiological characteristics .............................. 104 7 APPENDIX C : Survival and Growth of Foodborne Pathogens In Fresh Juice ................. 110 REFERENCES ................................ ................................ ................................ .......................... 120 viii LIST OF TABLES Table 1 - 1 : Sample of estimated costs and burden of foodborne disease (Jakob and Tritscher 2014) . ................................ ................................ ................................ ................................ .............. 9 Table 1 - 2 : Reported foodborne disease outbreaks and outbreak - associated illnesses, by food categor y Foodborne Disease Outbreak Surveillance System, United States, 2012, 2013 and 2014 . ................................ ................................ ................................ ................................ .............. 11 Table 1 - 3 : Listeria outbreaks associated with fresh produce . ................................ ....................... 19 Table 1 - 4 : Salmonella outbreaks associated with fresh produce (CDC 2018b) . .......................... 22 Table 2 - 1 : Transfer model parameters (A and B) for Listeria from inoculated zucchini and cucumber to the stationary and stationary slicer during sequential slicing and percent transfer (n = 3) ................................ ................................ ................................ ................................ ................ 51 Table 2 - 2 : Transfer model parameters (A and B) for Listeria from inoculated zucchini and cucumber to the slicer during sequential slicing at high and low speed (n = 3) ........................... 53 Table 2 - 3 : Mean (± SE) peak positive force, density, and water content ................................ ..... 53 Table 2 - 4 : Model parameters (A and B) for transfer of Listeria from inoculated cucumber to 15 uninoculated pieces of floral foam at percent moisture levels of 95.1, 96.7 and, 97.6% (n = 3). 55 Table 3 - 1 : Inoculation level for product after dip - inoculating in ~ 6 log cfu/ml, initial transfer from inoculated product ( ~ 7.5 Log CFU/product) to slicer, the percentage of the Listeria population transferred from one inoc ulated to 15 uninoculated samples, and the percent recovery of Listeria ................................ ................................ ................................ ................................ ...... 77 Table 3 - 2: Transfer model parameters (A and B) and predicted decay rate during transfer of Listeria from inoculated p roduce to the slicer during sequential slicing (n = 3) .......................... 78 Table 3 - 3 : Multiple comparison summary for the decay rate parameter (B) ............................... 83 Table 3 - 4 : Physicochemical characteristics of produce ................................ ................................ 86 Table 3 - 5 : Regression analysis of variance ................................ ................................ .................. 87 Table 3 - 6 : Effect te sts of the regression analysis ................................ ................................ .......... 87 Table A - 1: Mean L. monocytogenes distribution on produce slices from inoculated and uninoculated cucumber and zucchini after slicing with a rotating slicer. ................................ ..... 99 ix Table A - 2 : Listeria distribution (mean ± SE) on different components of the rotating slicer before and after slicing 15 uninoculated zucchini and cucumber. ................................ ................ 99 Table A - 3 : Listeria distribution (mean ± SE) on different components of the stationary slicer before and after slicing 15 uninoculated zucchini and cucumber. ................................ ................ 99 Table A - 4: Listeria populations (mean ± SE) on different locations of a zucchini and cucumber slice. ................................ ................................ ................................ ................................ ............ 100 Table A - 5: Listeria transfer from an inoculated stationary slicer (~ 7 log CFU/ pro duct) to 15 inoculated zucchini and cucumber. ................................ ................................ ............................. 100 Table A - 6 : Listeria transfer from an inoculated rotating slicer (~ 7 log CFU/ product) to 15 inoculated zucchini and cucumber. ................................ ................................ ............................. 100 Table A - 7 : L. monocytogenes transfer from inoculated to uninoculated zucchini during slicing at high speed and low speed. ................................ ................................ ................................ .......... 101 Table A - 8: L . monocytogenes transfer from inoculated to uninoculated cucumber during slicing at high speed and low speed. ................................ ................................ ................................ ....... 102 Table A - 9 : Listeria distribution (mean ± SE) on different components of a st ationary slicer before and after slicing 15 uninoculated pieces of floral foam at water saturation levels of 97.6, 96.7, and 95.1%. ................................ ................................ ................................ ......................... 102 Table A - 10 : Sequential transfer during slicing of floral foam at water saturation levels of 97.6, 96.7, and 95.1% ................................ ................................ ................................ .......................... 102 Table B - 1 : Salmonella distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninocul ated firm, medium and soft pear. Columns with asterisks are ................................ ...... 105 Table B - 2 : Salmonella sequential transfer during slicing of pears. ................................ ............ 105 Table B - 3 : Listeria distribution (mean±SE) on different components of a stationary slicer before and after slicing 15 uninoculated firm, medium and soft pear. Columns with asterisk s are ................................ ...... 106 Table B - 4 : Listeria sequential transfer during slicing of pears. ................................ .................. 106 Table B - 5 : Listeria distribution (mean±SE) on different component of a stationary slicer before and after slicing 15 uninoculated produce. ................................ ................................ ................. 107 Table B - 6 : Sequential transfer of Listeria during slicing of fresh cut produce. ......................... 108 Table C - 1 : Physicochemical Measurements of produce. ................................ ............................ 116 x LIST OF FIGURES Figure 1 - 1 : Typical fresh - cut process flow chart for fruits, vegetables, and root crops ................. 6 Figure 1 - 2: Surveillance pyramid Hoffmann and Scallan (2017) ................................ ................ 10 Figure 1 - 3 : Gram staining of Listeria monocytogenes ................................ ................................ . 15 Figure 1 - 4 : Salmonella CDC ( 2014 ) ................................ ................................ ............................. 21 Figure 2 - 1 : Components of the NEMCO model #N55200AN rotating slicer: (A) blade plate, (B) pusher plate, and (C) bottom plate. ................................ ................................ ............................... 38 Figure 2 - 2 : Components of NEMCO model # 59155491 stationary slicer: (A) pusher, and (B) blade ................................ ................................ ................................ ................................ .............. 38 Figure 2 - 3 : Computed tomographic (CT) images for (a) cucumber a nd (b) zucchini .................. 41 Figure 2 - 4 : Experimental design for the floral foam experiment ................................ ................. 43 Figure 2 - 5 : Mean (± SE) L. monocytogenes di stribution on slices from inoculated and uninoculated cucumber (a) and zucchini (b) after slicing with a rotating slicer. Means with different capital letters for inoculated slices are significantly different ( P different letters for un inoculated slices are significantly different ( P ............................. 46 Figure 2 - 6 : Listeria distribution (mean ± SE) on different components of the rotating slicer (A) and stationary slicer (B) before and after slicing 15 uninoculated zucchini and cucumbers. Columns with asterisks are significantly different (P 0.05 ) from the corresponding component ................................ ................................ ................................ ................................ ....................... 48 Figure 2 - 7 : Listeria populations (mean ± SE) on different locations of a cucumber (A) and zucchini slice (B). Columns with asterisks are significantl y different ( P corresponding location. ................................ ................................ ................................ ................. 49 Figure 2 - 8 : Listeria transfer from an inoculated product (~ 7 log CFU/product) to 15 inoculated zucchini and cucumber using a station ary slicer ................................ ................................ ........... 50 Figure 2 - 9 : Listeria transfer from an inoculated product (~ 7 log CFU/product) to 15 inoculated zucchini and cucumber using a rotating slicer ................................ ................................ .............. 51 Figure 2 - 10 : L. monocytogenes transfer from inoculated to uninoculated cucumber and zucchini during slicing at high (3.3 cm/sec) and low speed (2.0 cm/sec) ................................ ................... 52 Fig ure 2 - 11 : Listeria distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated pieces of floral foam at percent moisture levels of 95.1, 96.7 and, 97.6% ................................ ................................ ................................ ............................ 54 xi Figure 2 - 12 : Sequential transfer of Listeria during slicing of floral foam at percent moisture levels of 95.1, 96.7 and, 97.6% ................................ ................................ ................................ ..... 55 Figure 3 - 1: Salmonella distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated firm, medium and soft pear. Columns with asterisks are significantly different ( P ................................ ........ 68 Figure 3 - 2: Reduction in Salmonella populations on the before and after slicing 15 un inoculated firm, medium, and soft pears. ................................ ................................ ................................ ....... 69 Figure 3 - 3 : Sequential Salmonella transfer during slicing of pears ................................ .............. 69 Figure 3 - 4: Li steria distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated firm, medium, and soft pears. ................................ ....... 70 Figure 3 - 5: Reduction of Listeria popul ations on the slicer (mean ± SE) before and after slicing 15 uninoculated firm, medium and soft pears. ................................ ................................ .............. 70 Figure 3 - 6 : Listeria transfer during slicing of pears ................................ ................... 71 Figure 3 - 7 : Predicted Salmonella transfer from one inoculate pear (firm, medium, and soft) to 15 uninoculated sample. y predicted is the line of prediction; y observed is the observed line for 3 trails; Confide nce intervals is the confidence intervals for the line of prediction. ....................... 72 Figure 3 - 8 : Predicted Listeria transfer from one inoculate pear (Firm, medium, and soft) to 15 uninoculated sample. y p redicted is the line of prediction; y observed is the observed line for 3 trails; Confidence intervals is the confidence intervals for the line of prediction. ....................... 7 3 Figure 3 - 9: Listeria distribut ion (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated product samples. ................................ ........................... 76 Figure 3 - 10: Reduction of Listeria populations on the slicer (mean ± SE) before and after slicing 15 uninoculated product samples. Means with different letters for produce are significantly different ( P ................................ ................................ ................................ ...................... 76 Figure 3 - 11 : Predicted L. monocytogenes trans fer from one inoculate (Radish, Onion, Cantaloupe, Apple, Cucumber, Pear, Tomato, Potato, Zucchini, Gray zucchini, and sweet potato) to 15 uninoculated sample. y predicted is the line of prediction; y observed is the observed line for 3 trails; Confidence intervals is the confidence intervals for the line of prediction. ............... 78 Figure 3 - 12 : Individual component of the multiple regression model ................................ ......... 88 Figure C - 1: Pathogen Growth at 4°C and 10°C Over a 5 Day Period. Juices with an asterisk are significantly different ( P < 0.05) 11 6 xii KE Y TO SYMBOLS AND ABBREVIA TIONS AFM A tomic F orce M icroscopy CDC Centers for Disease Control and Prevention CFU Colony forming unit(s) CAC Codex Alimentarius Commission CT Computed tomographic CLSM Confocal Laser Scanning Microscopy d day(s) DI de ionized water FAO Food and Agriculture Organization FDA Food and Drug Administration FSMA Food Safety Modernization Act g grams GHP good hygiene practices GMP good manufacturing practices h hour(s) HU Hounsfield unit Kg kilogram L liter s lb pounds LOD limit of detection min minutes(s) xiii ml milliliter(s) MRA microbial risk assessment NRC National Research Council PBS Phosphate Buffered Saline ppm parts per million RMSE root mean square error s second(s) SD standard d eviation SE standard error SCC soluble solids content TSAYE Trypticase Soy Agar with 0.6 % Yeast Extract TSB T ryptic S oy B roth TSBYE Trypticase Soy Broth with 0.6 % Yeast Extract US United States of America USDA United States Department of Agri culture WHO W orld H ealth O rganization W weight Y response variable R a roughness micron(s) x1, x2, x3 independent variables of the linear model model parameters 1 INTRODUCTION Since the early 1970s, a significant increase in the consu mption of fresh produce has been observed in the United States, presumably due to active promotion of fruits and vegetables as an important part of a healthy diet. According to t yearbook report , per capita consumpt ion of fresh vegetables increased from 51.2kg in 1983 to 77kg in 2013 , while per capita consumption of fresh fruits increased from 40kg to 50.1kg for the same years (Thornsbury and Jerardo 2017) . With this tremendous increase in consumption and production of fruits and vegetables, the incidence of foodborne outbreaks associated with them has also increased. The Centers for Disease Control and Prevention identified about 600 leafy ve getable - associated outbreaks between 1973 and 2012 which , included 20 , 003 associated illnesses, 1 , 030 hospitalizations, and 19 deaths (CDC 2012) . Listeria mo nocytogenes has been isolated from a wide range of fresh fruits and vegetables , including potatoes, cucumbers, tomatoes, cabbages, radishes , apples, cantaloupe , and leafy greens (Heisick et al. 1989) . Salmonella has been associated with all major food groups , including fresh produce, which has become the leading contributor t o foodborne illness , with outbreaks involving grapes, cabbage, lettuce, sprouts, herbs, leafy green salads, and coleslaw (Todd 2014) . The CDC reported more than 1 , 974 confirmed cases of illness associated with fresh - cut produce from 2010 to 2018 (CDC 2018a) . From farm to fork, fresh - cut produce can become contaminated with pathogens. D ue to the nature of post - harvest processes , such as cutting, slicing, shredding and storing, cross - contamination could ultimately lead to outbreaks and/ or recalls. Several studies investigated the extent of pathogen transfer during processing of fresh - cut produce (Van Asselt et al. 2008; Brar and Danyluk 2013; Y. Chen et al. 2 001; Luo et al. 2011; Ukuku and Fett 2002) . Such studies 2 generally have been conducted to understand bacterial attachment and growth. In contrast, very few studies (Mazon 2017; Wang and Ryser 2016) have analyzed ba cterial transfer in terms of fundamental physical variables , such as contact pressure, surface roughness, contact time, and surface hydrophobicity. Water content ha s been show n to facilitate bacterial transfer ( Wang and Ryser 2016; Miranda and Schaffner 2016 ; Jensen et al. 2013) . Wang and Ryser (2016) assessed bacterial transfer during slicing of different tomato varieties. Significantly lower transfer decay rates and Salmonella transfer percentages were observed for Rebelski and Bigdena as compared to Torero tomatoes. Further analysis of the three tomato varieties (Torero, Rebelski and Bigdena) indicated that Torero tomatoes, which yielded greater exte nded transfer of Salmonella during slicing , had a tougher texture and lower water content compared to the other two varieties. The free liquid less bacterial transfer to subsequent tomatoes. T he impact of c ontact time between bacteria and surfaces on transfer have also been assessed . In one recent study to quantify cross - contamination between various foods and common kitchen surfaces (Miranda and Schaffner 2016) , more bacteria were transferred to watermelon (~ 0.2 to 97%) than to any other food examined , regardless of the contact time, which may be due to watermelon's moisture , which was significantly higher (0.99 ± 0.01) than other food s tested. Therefore, it is of interest to evaluate bacterial transfer during slicing as impacted by the type of slicer and water content. Modeling bacterial transfer during slicing of fruits and vegetables can be used to determin e e xposure to foodborne pathogens. In some studies (Buchholz et al. 2012; Rodríguez et al. 2011; Scollon et al 2016; Wang and Ryser 2016) , several mathematical models were developed from experimental transfer da ta to describe bacterial spread during processing , which 3 can be used as a guide to help estimate the amount of product that may have become cross - contaminated during processing and would need to be recalled. However, none of these studies attempted to quan tify the impact of physicochemical properties (water content, pH, cutting force , soluble solids content, surface hydrophobicity and surface roughness) across a wide range of fresh produce on bacterial transfer during slicing. It is hypothesized that : 1) Listeria transfer is impacted by the type of slicer, slicing speed and product moisture content ; 2) different firmness levels of pears will affect the transfer rate of Listeria during slicing ; 3 ) different types of produce will yield different transfer decay rate s during slicing due to differences in physicochemical properties ; and 4) produce transfer decay rate during slicing can be predicted using a mathematical model based on the physicochemical properties of the produce The ultimate goal of this research was to collect quantitative data on Listeria transfer during slicing of fresh produce and collect quantitative data of physicochemical properties of fresh cut produce (water content, pH, cutting force , soluble solids content, surface hydrophobicity and surface roughness) for subsequent model development to enhance the current understanding of interaction b etween these properties and transfer during slicing. Thus, this dissertation includes five primary objectives: 1) assess the impact of slicer type and speed on the transfer of Listeria during slicing; 2) evaluate the impact of water content on the transfe r of Listeria during slicing; 3) determine the effect of pear firmness on transfer of Listeria during mechanical slicing; 4) quantify Listeria transfer during slicing across different types of fresh produce ; and 5) develop a model to describe Listeria tran sfer during slicing of fresh produce based on physicochemical properties of the produc t (water content, pH, cutting force , soluble solids content, surface hydrophobicity and surface roughness). 4 C H APTER 1: Review of Pertinent Literature 5 1.1 FRESH - CUT PRO DUCE Fresh - consumption that have been minimally processed and altered in form by peeling, slicing, chopping, shredding, coring, or trimming, with or without washing, prior to bein g packaged for use by the consumer or a retail establishment (e.g., pre - cut, packaged, ready - to - These products have an estimated consumer market value of about $27 billion (Cook 2014) . Since the early 1970s, a significant increase in the consumption of fresh produce has been observed in the United States, presumably due, in part, to active promotion of fruits and vegetables as an important part of a healthy diet. Moreover, bioactive compound s pre sent in fruits and vegetables have been repeatedly linked to a lower risk of cardiovascular disease, stroke, cancer, and type 2 diabetes (K Jordan et al. 2014) , which has increased the consumption of produce. According to t , per capita consumption of fresh fruit in 2016 was 52.4 kg , up 3% from 51 . kg in 2015 with a total of about 58.5 billion kg of vegetables produced commercially . With this increase in consumption and production of fruits and vegetables, the incidence of foodborne outbreaks associated with them has also increa sed. The Centers for Disease Control and Prevention identified about 600 leafy vegetable - associated outbreaks between 1973 and 2012 which included , 20 , 003 associated illnesses, 1 , 030 hospitalizations, and 19 deaths. (Herman, Hall, and Gould 2015) . Fresh - cut pro cessing of produce involves various steps such as peeling, trimming , deseeding, slicing , and dicing to a specific size (Figure 1 ) with each of these step s potentially impacting quality and safety (James and Ngarmsak 2011) . 6 The quality of fresh - cut produc e includes a combination of characteristics that determine the value of produce to the consumer. Characteristics such as appearance, cutting force , flavor, and nutritional quality are essential for both producers and consumers. Pre - and post - harvest conditions can affect the quality of fresh - cut produce. For instance, pre - harvest quality of produce is influenced by the cultivars, genotypes and rootstocks, climate, cultural practices, maturity and ripening process (Garrett 2002) . Post - harvest conditions such as handling practices and management of both r elative humidity and temperature can negatively affect both the internal (physiological processes) and external (microbiological, chemical, environmental and mechanical) quality of harvested produce. Figure 1 - 1 - 1 : Typical fresh - cut process flow chart for fruits, vegetables , and root crops 7 The safety of fresh - cut produce is one component of qua lity. In fact, many experts believe that safety is the most important component of quality, since unsafe food can result in serious illness or death in some cases. Physical, chemical or microbial hazards can pose a threat to consumers throughout the produc e production process. Several measures are taken in fresh - cut facilities to maintain the microbial safety of produce. Washing in water containing a sanitizer in order to minimize cross - contamination during processing is standard practice for many types of fresh produce. Chlorine is currently the most commonly used sanitizer in washing operations. Chlorine has been successfully used at concentrations of 50 to 200 parts per million (ppm) to wash fresh - cut produce. However, excess amount s of free chlorine may react with organic compounds in produce wash water to generate carcinogenic halogenated disinfection by - products (DBPs), such as trihalomethanes (THMs). To maintain high food safety standards and minimize foodborne disease outbreaks, various guidelines have been published such as the "Guide to Minimize Microbial Food Safety Hazards for Fresh Fruits and Vegetables" by The U.S. Food and Drug Administration (FDA 2008) , "Safety and Quality of Fresh Fruit and Vegetables: Manual for Trainers" by the United Nations (Lineback 2002) and "Guidance on Environmental Monitoring and Control of Listeria for the Fresh Produce Industry" by the United Fresh P roduce Association (Bierschwale N.D) . With the passage and implementation of the Food Safety Modernization Act (FSMA), the FDA has recently published the final rule for produce safety, which sets standards related to agricultural water, worker training and health and hygiene, and equipment, tools and buildings, among other proce sses that impact food safety (FDA 2018) . 8 1.2 FOODBORNE DISEASE RELATED TO FRESH PRODUCE Foodborne disease is an important public health problem worldwide, which can negatively impact travel, trade, and development. The World Health Organization (WHO) estimated that 31 foodborne hazards caused 600 million foodborne illnesses and 420,000 deaths worldwide in 2010 (Havelaar et al. 201 5) . Five different categories of foodborne disease are recognized: infections, intoxications, metabolic food disorders, allergies, and idiosyncratic illnesses. The CDC has described more than 250 different foodborne diseases, most of which are infections caused by bacteria, viruses and parasites, or noninfectious chemicals and toxins. Many of these agents commonly cause diarrhea, vomiting and in some cases death but there is no single clinical syndrome for all foodborne diseases. Every year about one in si x Americans or 48 million people become ill, 128,000 are hospitalized, and 3,000 die of foodborne illness (CDC 2017) . The burden of foodborne disease and associated economic cost have been estim ated based on (1) the annual number of illnesses caused by a particular pathogen, (2) attributions of foodborne disease to particular foods, (3) acute illness severity and outcome, and (4) chronic complications (Jakob and Tritscher 2014) . Despite several attempts to estimate the costs and burden of foodborne disease (Table 1), such studies do not reflect the magnitude of foodborne illn ess because most foodborne illnesses are under - diagnosed or under - reported (Figure 2). 9 Table 1 - 1 : Sample of estimated costs and burden of foodborne disease ( Jakob and Tritscher 2014) Method (year of study) Foodborne disease Estimated costs Country COI the cost - of - illness Six bacteria, one parasite $6.5 34.9 billion US COI STEC O157:H7 outbreak $16,7 million UK DALY The disability - ad justed life year Campylobacter sp. 1400 DALY/case Netherlands COI All foodborne disease $55.1 million New Zealand COI Foodborne disease $123 million Sweden COI STEC O157:H7 outbreak $779,728 Japan COI STEC O157 (all sources) $3 44 million US COI All foodborne disease $989 million Australia 10 Method (year of study) Foodborne disease Estimated costs Country WTP Willingness - to - pay (WTP) All foodborne disease $1.4 trillion US DALY/COI Select foodborne diseases and irritable bowel syndrome $ 81.3 million Netherlands Figure 1 - 1 - 2 : Surveillance pyramid Hoffmann and Scallan (2017) During the last few years, the CDC ha s published annual reports of Foodborne Disease Outbreaks . In 2012, more than 800 foodborne disease outbreaks were reported, resulting in 14,972 illnesses, 794 hospitalizations, and 23 deaths. Vegetable row crops and fruits respectively Table 1 - 11 accounted for 12% and 21% of the total illnesses reported that year. In 2013, 818 foodborne disease outbreaks were reported , resulting in 13,360 illnesses (including 11 and 4% from fruits and vegetables) , 1,062 hospitalizations, and 16 deaths . During 2014, 864 foodborne disease outbreaks were reported , resulting in 13,246 illn esses, 712 hospitalizations, and 21 deaths. The highest number of outbreak - associated illnesses were from seeded vegetables (e. g. cucumbers or tomatoes; 428 illnesses, 16%). Foodborne disease outbreaks and outbreak - associated illnesses, by food category from 2012 to 2014 are summarized in (Table 2). Table 1 - 2 : Reported foodborne disease outbreaks and outbreak - associated illnesses, by food category Foodborne Disease Outbreak Surveillance System, United States, 2012, 2013 and 2014 Year 2012 Food type No. Outbreaks No. Illnesses Total % Total % Oils and sugars 1 1 7 0 Fungi 5 3 15 0 Sprouts 2 1 25 1 Root and other underground vegetables 5 3 34 1 Seeded vegetables 3 2 206 5 Herbs 0 0 0 0 Vegetable row crops 23 12 377 9 Fruits 16 8 858 21 Grains and beans 8 4 190 5 Nuts and seeds 1 1 42 1 12 Total 63 33 1754 42 Year 2013 Oils and sugars 1 0 7 0 Sprouts 1 0 3 0 Root and other underground vegetables 2 1 69 2 Seeded vegetables 8 4 305 8 Herbs 1 0 38 1 Vegetable row crops 9 4 207 5 Fruits 15 7 422 11 Grains and beans 8 4 61 2 Nuts and seeds 2 1 25 1 Total 47 22 1137 29 Year 2014 Oils and sugars 1 0 2 0 Fungi 5 2 11 0 Sprouts 4 2 141 5 Root and other underground vegetables 2 1 31 1 Seeded vegetables 7 3 428 16 Herbs 1 0 7 0 Vegetable row crops 13 6 174 6 Fruits 10 5 139 5 Table 1 - 13 Grains and beans 9 4 104 4 Nuts and seeds 3 1 55 2 Total 55 27 1092 40 1.3 PRE - HARVEST VS P OST - HARVEST CONTAMINATION The normal microflora on fruits and vegetables is usually nonpathogenic to humans. However, microorganisms from many sources, whether animal, environmental or human, have the potential to contaminate fruits and vegetables during field production, harvesting, further processing and transportation (FDA, 2008). Sources of contamination can be divided into two main categories: p re - harvest and post - harvest. The former includes irrigation water, green or inadequately composted manure, a ir (dust), wild and domestic animals, human handling, and water used for other purposes (for example, pesticides, foliar treatments, growth hormones). The latter category includes human handling (workers, consumers), harvesting equipment, transport contain ers (field to packing shed), wash and rinse water, sorting, packing, slicing/dicing and further - processing equipment, transport vehicles, improper storage (temperature, physical environment), improper packaging (includ ing new packaging technologies), cross - contamination in food storage, preparation and display areas , and improper handling after wholesale or retail purchase (FDA 2015) . Although most bacterial contamination occurs during pre - harvest , contaminants can spread quickly du ring post - harvest processing. This is due to the nature of post - harvest processes such as slicing, shredding and storing which could ultimately lead to outbreaks or recalls (Beuchat and Ryu 1997) . In a su rvey of fresh and minimally - processed fruit and vegetables conducted by Abadias et al . (2008) , L. monocytogenes was present in 0.7% of 300 samples. Table 1 - 14 Although the incidence of Listeria was low, fresh - cut packaged vegetables that support growth of the pathogen could represent a risk to consumers. Several studies investigated the extent of pathogen transfer during processing of fresh - cut produce (Van Asselt et al . 2008; Brar and Danyluk 2013; Y. Chen et al . 2001; Luo et al . 2011; Ukuku and Fett 2002) . In a large - scale experiment, Buchholz (Buchholz et al. 2012b) demonstrated that E. coli O157:H7 tr ansferred from inoculated lettuce to both the shredder and conveyor belt. The study found that processing lettuce inoculated with 10 6 or 10 4 CFU/g of E. coli before shredding 90.8 kg uninoculated lettuce was sufficient to contaminate the entire product lot . Another experiment by Ukuku and Fett (2002) showed that L. monocytogenes transferred from the inoculated rind of cantaloupe to the interior flesh during cutting. Carrots, watermelon, celery and lettuce were also examined by Jensen et al. ( 2013) for the transfer of pathogens between produce and common kitchen surfaces. It was determined that more than 90% of bacteria tran sferred to the fresh - cut produce in almost all of the scenarios studied. 1.4 LISTERIA MONOCYTOGENES AND FRESH - CUT PRODUCE The official discovery of Listeria dates back to 1924, when Murray, Webb, and Swann isolated L. monocytogenes as the etiological agent of a septicemic disease affecting rabbits and guinea pigs in their laboratory at Cambridge in England . This strain was named Bacterium monocytogenes , as it was observed to infect monocytes of the blood. Although the first cases of human listeriosis were r eported in 1929 in Denmark , L. monocytogenes was not recognized as a s was directly linked to the consumption of contaminated coleslaw salad in Canada (Magalhaes et al . 2014) . 15 Listeria monocytogenes is a member of the genus Listeria , a group of Gram - positive bacteria closely re lated to Bacillus and Staphylococcus . The genus Listeria includes 15 different species , two of which are considered pathogenic. L. monocytogenes is pathogenic to humans, causing listeriosis, and L. ivanovii is mainly pathogenic to animals, although a few cases of human infection have been reported (Jordan et al . 2015) . Listeria monocytogenes strains van be divided into serotypes based on somatic (O) and flagellar (H) antigens. Listeria monocytogenes is catalase - positive, oxidase - negative, regular short rod with a diameter of approximately 0.5 µm and a length of 0.5 2.0 µm (Figure 3). The or ganism is a facultative anaerobe that does not form a capsule or spores, and is motile by peritrichous flagella when cultured at 20 25 o C and non - motile at 37 o C. L. monocytogenes has the ability to hydrolyze esculin and sodium hippurate but not urea, gela tin, or casein (Motarjemi et al , . 2014) . Figure 1 - 1 - 3 : Gram staining of Listeria monocytogenes rne pathogen of great concern. Listeria monocytogenes can grow at 1.5 to 45 o C, with optimum growth between 30 and 37 o C. It also can grow over a wide range of pH values (4.3 to 9.6) with 16 optimum growth between pH 6.0 and 8.0 (Uyttendaele et al. 2014) . Unlike most human pathogens, L. monocytogenes can grow at refrigeration temperatures and has been associated with sporadic outbreaks. Listeria monocytogenes is capable of causing serious invasive illness (listeriosis) with a fatality rate of about 20%, especially in older adults, pregnant women, newborns, and adults with weakened immune systems. While the infectious dose of Listeria is unknown, data from previous outbreaks suggests that levels of L. monocytogenes in foods identified as being responsible for outbreaks or sporadic cases are often greater than 100 CFU/g (Rees and Do yle 2017) . Consequently, the presence of L. monocytogenes in food at levels less than 100 CFU/g is thought to have a very low probability of causing disease and that less than 1000 CFU is of no of L. monocytogenes in any cooked, RTE food is a violation (The analytical method that FDA uses can detect 1 CFU of L. monocytogenes per 25 g of food to determine whether L. monocytogenes is present in the food (i.e., 0.0 4 CFU /g)). After ingesting food contaminated with Listeria , the organism passes through the stomach and crosses the intestinal barrier via M - cells. It is then transported by the lymph or blood to the mesenteric lymph nodes, spleen, and liver. The fact tha t L. monocytogenes is a facultative intracellular pathogen allows it to replicate in macrophages and a variety of non - phagocytic cells, such as epithelial and endothelial cells. After entering the cell, Listeria escapes early from the phagocytic vacuole, m ultiplies in the host cell cytosol, and then moves through the cell by induction of actin polymerization. The bacteria then protrudes into cytoplasmic evaginations, and these pseudopod - like structures are phagocyt iz ed by the neighboring cells (Simjee 2007) . L. monocytogenes virulence factors are involved in the cell - to - cell spread, which helps the pathogen 17 avoid the extracellular environment and immune system during its spread in the host. These virulence genes form a 9 - kb gene cluster known as the Listeria pathogenicity island 1 (LIPI - 1). Despite extensive research, L. monocytogenes has remained one of the most problematic pathogens in the food industry . Lis teria typically contaminates food from direct contact with equipment and the general environment after foods have been processed . Moreover, L. monocytogenes can persist in food processing facilities for years with the same strains having been isolated fro m both the food - processing environment (e.g., drains, equipment, etc.), and food - contact surfaces (e.g., slicing machines), rather than raw materials (Motarjemi et al . , 2014) . The finding that persistent strains are often recovered from the environment and equipment after cleaning and sanitizing, emphasizes the risk of growth and establishment of L. monocytogenes , particularly in sites difficult to access leading to ongoing food product contamination. Listeria can exist in the environment either as planktonic cells or as communiti es in biofilms, where they are attached to a surface and enclosed in a matrix predominantly made up of polysaccharide material (Gandhi and Chikindas 2007) . Microbial biofilms demonstrate an enhanced resistance to sanitizers, disinfectants and antimicrobial agents and can form on a wide range of surfaces in food processing facilities , and industrial equipment . Biofilms in food processing environments occur on product contact surfaces or areas where food is stored or on food processing surfaces such as conveyer (Gandhi and Chikindas 2007) . Biofilms of Listeria are of particular concern , since they are more resistant to disinfectants and sanitizing agents compared to planktonic cells and this makes their elimination from food processing facilities a 18 big challenge. Although biofilm formation is more studied in term s of bacterial attachmen t, it could contribute directly or indirectly to bacterial transfer . Listeria monocytogenes has been isolated from a wide range of fresh fruits and vegetables including potatoes, cucumbers, tomatoes, cabbages, and radishes (Heisick et al . 1989) . Crépet et al . (2007) calculated the probabilities of fresh unprocessed and minimally processed vegetables being contaminated, based on data from 165 prevalence studies of L. monocytogenes in fresh vegetables (25,078 samples). Their results showed that the probabilities of contamination with populations hig her than 10, 100 or 1000 viable L. monocytogenes organisms/g were 1.44, 0.63 and 0.17% respectively, indicating that there is approximately a 1.44% chance of fresh produce being contaminated with 10 cells/g. Moreover, based on product type, the mean log c oncentrations of L. monocytogenes on leafy salads, sprouts and other vegetables was 3.36, 3.09 and 3. 43 log CFU / g , respectively . Although listeriosis is responsible for only 0.02% of gastroenteritis, it accounts for about 25% of deaths as a result of gastroenteritis (R oss 2000) . Several studies have attempted to evaluate the cost of listeriosis case in the USA (Mead et al. 1999; Scallan et al. 2011) . These costs include human cost, litigation, industry cost, product recalls and regulatory costs ( Jordan et al . 2014) . For example, in 2008, 57 cases of listeriosis and 24 deaths in Canada were linked to contaminated delicatessen meat from one meat processing plant. It was estimated that the costs for this outbr eak reach $2.2 million (Thomas et al. 2015) . In south Africa earlier this year, the World Health Organization ( WHO) reported the largest Listeria outbreak ever recorded with more than 1000 confirmed cases and more than 67 deaths. Although not confirmed by health officials, food is the suspected source of the outbreak (WHO 201 8) . In 2015, a multistate outbreak of listeriosis was linked to commercially produced, prepackaged caramel apples resulting in 7 deaths and 34 19 hospitalizations according to the CDC (CDC 2015) . More recently, an out break of listeriosis linked to packaged Dole Food Company salads resulted in 19 hospitalizations and one death, across nine U.S. states. The c ompany stopped all production at the processing facility and recalled all packaged salads on the market CDC (CDC 2016) . Table 3 summarize s the fresh cut outbreaks and recalled reported by CDC since 2011. Table 1 - 3 : Listeria outbreaks associated with fresh produce: Year Produce Cases Death Hospitalizations Number of states Recall 2016 Frozen vegetables 9 3 9 4 Yes 2016 Packaged Salads 19 1 19 9 Yes 2014 Caramel Apples 35 7 34 12 Yes 2014 Bean Sprouts 5 2 5 2 Yes 2011 Cantaloupes 147 33 143 28 Yes 1.5 SALMONELLA AND FRESH - CUT PRODUCE Salmonella h as been recognized for over 100 years as a cause of illnesses ranging from mild to severe food poisoning (gastroenter itis), and even more severe typhoid (enteric fever), paratyphoid, bacteremia, septicemia and a variety of associated longer - term conditions 20 (sequelae). Some of these severe conditions can result in high rates of mortality and can occur in outbreaks involvi ng large numbers of people, particularly in relation to typhoid outbreaks and septicemic conditions (Blackburn 2009) . The history of Salmonella (Blackburn 2009) dates back to the late 1800s when an which, at that time, was named Bacterium suipestifer but later renamed as the type species of the genus named after him, Salmonella cholerae - suis . It was not until the 1960s, however, that the name Salmonella became the widely accepted for this genus of t he family Enterobacteriaceae. Salmonella spp. (Figure 4) are facultatively anaerobic, Gram - negative, straight, small (0.7 - 1.5 x 2.0 - 5.0 µm) rods, which are usually motile by peritrichous flagella. Being Gram - negative, Salmonella is more resistant to ant ibiotics and sanitizers than Gram - positive bacteria. This is primarily due to their thin peptidoglycan layer, which is located between two thin membranes. The thin outer membrane surrounding the peptidoglycan layer is impermeable and resists toxic material s that could damage the cell (Mitchell 2015) . Salmonella is an infectious organism that multiplies in the small intestine, colonizing and subsequently invading the intestinal tissues, producing an enterotoxin and causing an inflammatory reaction and diarrhea. Moreover, the organism can enter the blood stream and/or the lymphatic system and ca use more severe illnesses (Blackburn 2009) . 21 Every year approximately 1.2 million illnesses and 450 deaths occur due to non - typhoidal Salmone lla in the United States according to the CDC ( 2018) . The most common serotypes of Salmonella that causes human infection are Enteritidis, Typhimurium, Newport, and Javiana. Th ese Salmonella serotypes account for about half of culture - confirmed Salmonella isolates reported by the CDC ( 2018) . The symptoms of Salmonella infection include sudden o nset of diarrhea (which may be bloody), abdominal cramps, fever (almost always present), nausea, vomiting and less frequent ly, headaches . Salmonella has been associated with all major food groups including fresh produce, which has become the leading c ontributor to this foodborne illness with outbreaks involving grapes, cabbage, lettuce, sprouts, herbs, leafy green salads, and coleslaw . The CDC ha s reported more than 1974 confirmed cases of illness associated with fresh cut produce from 2010 until 2018 ( T able 4 ) . Tomatoes have been most commonly associated with Salmonella with 5,324 cases of illness in the U.S. and 35 outbreaks between 1990 and 2012 according to the Center for Science in the Public Interest (2013) . Figure 1 - 1 - 4 : Salmonella CDC (2014) 22 Table 1 - 4 : Salmonella outbreaks associated with fresh produce (CDC 2018b) Year Produce Cases Death s Hospit a lizati ons # of states Serotype Recall 2018 Raw Sprouts 8 0 0 3 Montevideo No 2017 Maradol Papayas 220 1 68 23 Thompson, K iambu, Agon a, and Gaminar a Yes 2017 Maradol Papayas 20 1 5 3 Anatum Yes 2016 Alfalfa Sprouts 36 0 7 9 Abony Yes 2016 Alfalfa Sprouts 26 0 8 12 Muenchen and Kentucky No 2015 Cucumbers 907 6 204 40 Poona Yes 2014 Cucumbers 275 1 101 29 Newport 23 Year Produce Cases Death s Hospit a lizati ons # of states Serotype Recall 2014 Bean sprout s 115 0 28 12 Enteritidis No 2013 Cucumber 84 0 17 18 Saintpaul No 2012 Mangoes 12 7 0 33 15 Braenderup Yes 2012 Cantaloupe 261 3 94 24 Typhimurium and Newport Yes 2011 Whole, Fresh Imported Papayas 106 0 10 25 Agona Yes 2011 Cantaloupe 20 0 3 10 Panama No 2010 Alfalfa Sprouts 44 0 7 11 Newport Yes 1.6 BACTERIAL TRANSFER DURING SLICING AND DICING A serie s of previous studies investigated bacterial transfer during slicing of delicatessen meats (Sheen and Hwang 2008; Vorst et al . 2006) , (Lin et al. 2006) , (Chen et al. 2014) , shredding of leafy greens (Beuchat and Doyle 1995) , (Buchholz et al. 20 12c) , (Nou and Luo 2010) and slicing/dicing of tomatoes (Wang and Ryser 2014, 2016) . These studies demonstrated that the Table 1 - 24 likelih ood for cross - contamination during slicing is high. However, due to the nature of bacterial transfer, large variations within replicates were observed for most of the transfer studies, particularly at lower initial inoculation levels. After a series of r ecalls in 2012 involving diced yellow onions contaminated with L. monocytogenes from one manufacturer, Scollon et al . ( 2016) conducted a study to quantify the extent of L. monocytogenes transfer during mechanical slicing of onions. After slicing 20 onions, L. monocytogenes was quantifiable on bo th the pusher plates and blades of the slicer, allowing for further transfer. Their research clearly showed the potential for cross - contamination from inoculated to uninoculated onions during sequential slicing. Similarly, Kaminski et al . (2014) investigated the transfer of L. monocytogenes to previously uncontaminated product during mechanical dicing of celery and found Listeria present throughout 15 uninoculated batches . In an attempt to better understand bacteria l transfer, Wang and Ryser (2016) assessed bacterial transfer during slicing of different tomato varieties. Significantly lower transfer decay rates and Salmonella transfer percentages were observed for Rebelski and Bigdena as compared to Torero tomatoes. Further analysis of the three tomato varieties (Torero, Rebelski and Bigdena) indicated that Torero tomatoes, which yielded greater transfer, had a tougher texture and lower water content compared to the other two varieties. Th is free liquid released durin g slicing can subsequent tomatoes (Wang et al . 2016) . Preliminary work in our lab also has shown that different t ypes of produce with varying characteristics ( water content, pH, cutting force , soluble solids content, surface hydrophobicity and surface roughness ) have different transfer rates. This difference in transfer rate could be attributed to any of the above fa ctors. Hence, more research is 25 needed to better understand bacterial transfer during processing with this information leading to improved sanitation programs and risk assessments. 1.7 FACTORS EFFECTING TRANSFER DURING SLICING The rate at which pathogens transf er during slicing depends on a number of factors including the chemical and physical properties of the food, the equipment surface and materials, and the microorganism(s) involved in addition to environmental and operational conditions. Sheen and Hwang (2010) summarized the factors affecting microbial transfer which include (1) food composition (moisture, fat contents, formulation), (2) food texture (homogeneity, hydrophobicity, roughness), (3) the blade for slicing ( blade speed (rpm), slicing speed (i.e., slices per minute), blade size, blade sharpness and the material of the blade ) , (4) bacterial factors (age, strain, inoculation level, stress respo nse, attachment to surfaces), and (5) the environmental conditions (e.g., temperature). Identifying which of these factors has the most significant impact on bacterial transfer provides critical quantitative data for mathematical modeling that will be use ful in refining current risk assessments. Surface characteristics of fresh - cut produce play a n important role in the way bacteria are able to attach, transfer and proliferate. Several factors including produce type, maturity, variety , and growing condition can change the surface characteristics of fresh - cut produce by altering surface hydrophobicity, surface constitutional characteristics, and surface topography (Wang et al. 2009) evaluated by Fernandes et al. ( 2014) who found that the average roughness ( Ra ) of mango es (4.54 ± 1.95 mm) was significantly dif ferent ( P 0.05) compared to tomato es (2.88 ± 2.15 mm). However, the numbers of bacteria on both fruit surfaces were similar ( p > 0.05), reaching 5.95 ± 26 0.36 log CFU cm 2 and 5.81 ± 0.39 log CFU cm 2 on mango es and tomato es , respectively suggest ing that bac terial adhesion is a multifactorial process. In another study , Adhikari et al . ( 2015) examined t he effectiveness of UV - C inactivation of pathogens on different products including apples, pears, strawberries, red raspberries and cantaloupes . These researchers reported greater pathogen on products having smooth rather than rough surfaces with rough sur face products presumably providing greater shelter for pathogens from UV - C . Similarly Syamaladevi et al . , ( 2013) looked at the influence of surface characteristics of pears on the kinetics of UV - C inactivation of E. coli and concluded that the physical and morphological characteristics ( i.e. surface roughness ) of pears influence d the ability of UV - C to a chieve specific levels of reduction in E. coli population. Using Confocal Laser Scanning Microscopy (CLSM) to quantify produce surface roughness, Wang et al . (2009) showed a positive linear correlation between average surface roughness ( Ra ) and the adhesion rate of E. coli O157:H7 for Golden Delicious apples (1.43 ± 0 .13 avocadoes (14.18 ± , while surface hydrophobicity for the same produce was 77.27 ± 4.57, 78.23 ± 8.37, , respectively. The population s of E. coli O157:H7 on fresh fruit surfaces after a 5 - min washing treatment w ere 2.61 ± 0.20, 3.99 ± 0.33, 5.19 ± 0.19 and 6.03 ± 0.29 log CFU/cm 2 , respectively. Another study conducted by the same group examined the relationship between surface roughness of apples, avocadoes, and cantaloupes and the removal of pathogens during washing (H. Wang et al. 2007) . Produce with lowest surface roughness (apples had the highest pathogen reduction rate during washing , whereas produce with the high est roughness (cantaloupe It is likely that the rougher the produce surface , the more protection is provided to the E. coli O157:H7 . 27 Adhesion of Salmonella Enteritidis to lettuce leaves was evaluated in the conte xt of leaf roughness by Lima et al. (2013) . Lettuce grown hydroponic ally had a significantly rougher surface ( mean Ra of 1211 ± 171 nm) comp ared with conventional cultivation (293 ± 59 nm). The number of adherent S. Enteritidis cells was 0.64 and 0.14%, respec tively , for hydroponic and conventional systems. Adherence may be facilitated by increased contact area between the microorganisms and the surface. Other factors that may affect bacterial transfer during slicing of produce such as produce f irmness and juiciness have not been extensively studied . Firmness of fruits and vegetables is dependent upon cell morphology, cell size, shape, packing, wall thickness and strength, extent of cell - to - cell adhesion, and turgor status as described by Toivonen and Brummell (2008a) . Usually these factors are interrelated. For instance, a tissue with small cells would have more cell wall material which means a greater area of cell - to - cell contact and fewer intercellular air spac e s , leading to a firmer and less juicy tissue (Toivonen and Brummell 2008c) . Juiciness , however, is determined by tissue breakdown during mechanical action such as chewing, biting or slicing. This breakdown occurs whe n cell walls are split open releasing juice, or when cell separation occurs along the middle lamellae causing the tissue to split with minimum cell rupture. More specifically, during slicing, tissue failure involves cell separation, cell breakage, or a com bination of both. If the cell walls are stronger than the forces holding cells together, separation will occur and the tissue in this case is usually firm such as in unripe fruit. Alternatively, if forces attaching cells together are stronger than the cell walls themselves, then failure will occur releasing juice from the cell (Waldron, et al. 200 3) . Other than the tomato work done by Wang and Ryser (2016) , no other studies have assessed effect of firmness and juiciness on bacterial transfer . However, the effect of fruit ripeness on the survival and growth of L. 28 monocytogenes on fresh - cut conference pear slices was studied by Colas - Meda et al . (2015) . Pears of f our different ripeness stages - matu re - green ( 54 - 60 N), partially ripe (43 - 53 N), ripe (31 - 42 N) and overripe ( > 42 N) ) , were dip - inoculated in a 10 5 CFU/ml Listeria suspension and stored for 8 day at 5, 10 and 20 C. L. monocytogenes grew under all experimental conditions, showing an increas e of approximately 2 log CFU / g at 5 C. N o significant difference s in L. monocytogenes population s were seen between the different ripeness stages after 8 days of storage at 5 o C . This study , however, did not look at the effect of pear ripeness on Listeri a transfer or attachment. Understanding the impact of firmness on bacterial transfer during slicing could potentially improve our ability to better predict the extent of transfer. T he effect of contact time between pathogens and the surface needs to be mo re closely examined to better understand the dynamics of bacterial transfer. In a recent study to quantify cross - contamination between various foods and common kitchen surfaces at different contact time s (Miranda and Schaffner 2016) , more bacteria transferred to watermelon (~ 0.2 to 97%) than to any other food examined , regardless of the contact time, which may be due to watermelon's moisture which was significantly higher (0.99 ± 0.01) than the other food tested . However, Rodriguez et al . ( 2008) examined the impact of contact time on attachment of Listeria biofilms to stainless steel surfaces using atomic force micr oscopy (AFM) and concluded that contact time did not affect the , indicat ing that transfer is likely to be more effected by physicochemical rather than cellular factors . W hen Jensen et a l. (2013) determined the cross - contamination rates be tween a variety of fresh - cut produce including mini - peeled carrots, celery, watermelon, and romaine lettuce and common kitchen surfaces, they found that bacterial transfer depended on produce type, surface moisture, and drying time. Freshly inoculated cele ry or lettuce transferred more bacteria (2 to 25% of the 29 inoculum) compared to freshly inoculated carrots and watermelon ( 1 to 8%). However, the study did not measure the physicochemical characteristic s of the fresh - cut produce used that could have helped explain the differences in transfer rate s . Several blade characteristics including thickness, roughness and sharpness affect the extent of bacterial transfer during slicing of meats, fruits , and vegetables. Wang and Ryser (2016) studied the impact of slic e thickness on Salmonella transfer during slicing tomatoes using effect on transfer. These and many other studies yielded contradicting conclusion s , since the experimental procedures for these studies varie d . The d ifferent inoculation methods, contact time s and organisms used are likely responsible for the variation s seen. . Thus, further investigation s into such scenarios are essential to a better understand ing of the dynamic s of bac terial transfer and how transfer relate s to the physicochemical characteristic s of fresh produce, which will result in better practices to limit Listeria spread. 1.8 MODELING OF BACTERIAL TRANSFER DURING SLICING Predictive microbiology is a relatively new s cientific branch of food microbiology that uses mathematical models to quantitatively assess microbial behavior in foods. These models help food microbiologists describe different microbial processes, including kinetic processes such as microbial death and growth, or physical processes such as bacterial transfer (Perez - Rodrig uez and Valero 2013) . Three types of models are recognized primary, secondary and tertiary. Primary models aim to describe the kinetics of a process using as few parameters as possible while still being able to accurately define microbial growth and in activation. Secondary models describe the 30 effect of environmental conditions (i.e., physicochemical and biological factors) on the parameters of the primary model. (c) Tertiary models based on computer software programs provide an interface between the und erlying mathematics and the user, allowing model inputs to be entered and the estimates to be observed through simplified graphical outputs (Whiting and and Buchanan 1993) . There exists a specific need to understand bacterial transfer during the slicing and cutting process since many studi es have shown that this process is a major source for contamination. Based on previous studies, bacterial transfer during slicing follows a logarithmic decline, and hence, exponential decay models have been successfully applied to describe bacterial transf er (Perez - Rodriguez and Valero 2013) . Wang and Ryser (2016) modeled the transfer of Salmonella during slicing of tomatoes using an exponential model (y = a*e( - x/b)), where Y (dependent variable) is the log CFU/tomato transferred and X (independent variable) is the order number for the specif ic uninoculated tomato that was sliced. The model fit the data from different test conditions and was suitable for predicting Salmonella transfer during slicing of tomatoes with a root mean square error (RMSE) < 0.5 under all test conditions. Similarly, Sc ollon et al . (2016) investigated the transf er of L. monocytogenes during slicing of onions and observed a logarithmic decrease from initial inoculum levels of 8.6, 6.8, and 5.9 log CFU /onion, respectively, with 20 slices being obtained. When fit to the transfer data, an exponential decay model yiel ded good fits, with RMSE values < 0.3 for all three inoculation levels. Bacterial transfer can occur at different stages across the food chain. Foods can be re - contaminated after an inactivation process, during food transportation/preparation , or at retai l or the time of consumption. Cross - contamination, however, refers to indirect and direct transfer of microorganisms from a contaminated food surface to other recipient food surfaces in food - 31 related environments (Perez - Rodriguez and Valero 2013) . Cross - contamination in household settings also has been subjected to modeling. For example, Zilelidou et al . (2014) investigated the different E. coli O157:H7 and L. monocytogenes transfer rates (Tr) between cutting knives and lettuce le aves. The quantitative data regarding the extent of E. coli O157:H7 and L. monocytogenes transfer from contaminated lettuce to kitchen knives and subsequent transmission to fresh lettuce were used to develop a semi - mechanistic model describing bacterial tr ansfer. The model sufficiently described the transfer rates with RMSE values of 0.426 - 0.613 and 0.531 - 0.908 for L. monocytogenes and E. coli O157:H7, respectively. However, the model underestimated bacterial transfer during extrapolation experiments. Although the current models seem to well - represent the observed transfer rates on an empirical basis, bacterial transfer models are still in their early stages. Filling current knowledge gaps of how environmental and intrinsic factors influence the transfe r phenomenon will improve how we approach public health. 1.9 QUANTITAVE MICROBIOLOGICAL RISK ASSESSMENT like commercial chemicals and environmental pollutants as carcinog ens. In response, the National Research Council (NRC) formed the Institutional Means for Assessments of Risks to objectivity of scientific assessment that forms the basis for federal regulatory policies applicable (Simjee 2007) work were summarize d and published in 1983 as an NRC report entitled Risk Assessment in the Federal Government: Managing the Process 32 on the color of its cover), marks the beginning of a formalized concept of risk assessment (Simjee 2007) . identification of biological, chemical, and physical agents ca pable of causing adverse health effects and which may be present in a particular food or group of foods); (2) exposure assessment (the qualitative and/or quantitative evaluation of the likely intake of biological, chemical, and physical agents via food as well as exposures from other sources if relevant); (3) hazard characterization/ dose response (the qualitative and/or quantitative evaluation of the nature of the adverse health effects associated with the hazard); and (4) risk characterization (the integr ation of the hazard identification, hazard characterization, and exposure assessment determinations to provide qualitative or quantitative estimates of the likelihood and severity of (Simjee 2007) . Use of risk analysis to develop food standards was proposed by the Expert Consultation from the Food and Agriculture Organization (FAO), the World Healt h Organization (WHO), and the Codex Alimentarius Commission (CAC). Risk assessment is a crucial tool in the development of food safety policies and procedures as well as validating some of the existing safety programs. There are two types of microbial ri sk assessment (MRA): qualitative and quantitative. Qualitative risk assessment describe s the likelihood of illness (high vs low) , whereas quantitative risk assessment predict s the number of illnesses , provides numerical expressions of risk , and indicat es t he attendant uncertainties. Quantitative Microbial Risk Assessment ( QMRA ) modeling, a relatively new approach in the field of microbial risk, uses probability models to 33 evaluate the likelihood of adverse human health effects from exposure to pathogenic mic roorganisms (Simjee 2007) . Modeling bacterial transfer during slicing of fruits and vegetables is an essential tool for determining exposure to foodborne pa thogens. In some of the aforementioned transfer studies, several mathematical models were developed from the experimental transfer data to describe bacterial spread during processing , which can be used as a guide to help estimate the amount of product that may have become cross - contaminated during processing and would need to be recalled. Further research is needed to derive more reliable mathematical models that take into account environmental and intrinsic factors that can influence bacterial transfer phe nomenon for different types of fresh - cut produce. A model covering a wide range of fresh - cut produce based on product characteristics would be very useful for estimating the extent of transfer of other types of produce without the need to quantify the tr ansfer for the many dozens of product types . 34 CHAPTER 2 : 2 Microbial Cross - Contamination of Cucumber , Zucchini , and Floral Foam D uring Slicing as Impacted by Mechanica l Slicer Type, Slicing Speed and Water C ontent 35 2.1 OBJECTIVE The objective of this study was to assess the transfer of L. monocytogenes from inoculated cucumber and zucchini to various surfaces of rotating and stationary slicers, as well as to subsequently uninoculated products during slicing at different speeds. Another aim was to assess the relationship between water content and bacterial transfer during slicing. 36 2.2 MATERIALS AND METHODS 2.2.1 Cucumber and zucchini seve ral lots of c ucumber (Cucumis sativus) and z ucchini (Cucurbita pepo) were purchased from a local supplier (Stan Setas Produce Company, Lansing, MI) over a period of four months with each lot stored at 4 o C for no more than 7 d before use. The root and spro ut ends of each product were removed using a sterile kitchen knife. P roduct s were tempered to room temperature (23 o C ± 2 o C) and dimensions were recorded prior to slicing. 2.2.2 Bacterial strains Three avirulent L. monocytogenes strains - M3 serotype 1/2a (Hl y - , parent strain Mackaness), J22F serotype 4b (Hly + , purB mariner - based mutant of H7550 - Cd S , parent strain NCTC 10527), and J29H serotype 4b (Hly - , parent strain NCTC 10527) (obtained from Dr. Sophia Karthariou, North Carolina State University, Raleigh, NC) were used in all slicing experiments. All strains were stored at - 80 o C in trypticase soy broth containing 0.6% (w/v) yeast extract (TSBYE, Becton, Dickinson and Company, Sparks, MD) and 10% (v/v) glycerol. Each strain was initially streaked for isolat ion to plates of trypticase soy agar containing 0.6% (w/v) yeast extract (TSAYE, Becton, Dickinson and Company) and incubated for 24 h at 35 o C. Thereafter, an isolated colony of each strain underwent two consecutive 24 h/35 o C transfers in TSBYE. When use d as cocktails, the three avirulent strains were combined in equal volumes and appropriately diluted to obtain populations of ~ 6.0 log CFU/ml for produce inoculation, with these levels confirmed by plating appropriate dilutions on Modified Oxford Agar ( MOX, Neogen Corp., Lansing, MI). cucumber and zucchini were dip - inoculated to contain ~ 7 .5 log CFU/ product Listeria . 37 2.2.3 Identification of contact areas between rotating slicer and product: To identify the product contact areas of a manual rotating slicer (NEMCO slicer, Model #N55200AN, Nemco Food Equipment Inc., Hicksville, OH), and stationary slicer (NEMCO model # 59155491, Nemco Food Equipment Inc.), Glo Germ reagent (Glo - Germ Company, Moab, UT) was used as reported previously (Buchholz et al . 2012a , Vorst et al ., 2 006) . One cucumber or zucchini was immersed in 0.5% (w/v) Glo - Germ solubilized in 5% ethanol, dried for 90 min, and then manually sliced, after which the components of the slicer were viewed under UV light (352 nm, Sankyo Denki Co., Ltd, Tokyo, Japan). Th ree product contact areas: the blade plate, pusher plate , and bottom plate were identified for subsequent sampling (Figure 1). The same procedure was used for a stationary NEMCO slicer (Model # 59155491) that had a set of fixed blades. As shown in Figure 2, two contact areas were identified: the blades and pusher. 2.2.4 Listeria distribution on individual slices One cucumber or zucchini ( 15 cm in length) was dip - inoculated for two minutes in the 3 - strain avirulent L. monocytogenes cocktail to contai n ~ 7.5 log CFU/c ucumber , air - dried for 1h and sliced using the rotating slicer. From each intact cucumber and zucchini, 6 slices were generated (1 st , 2 nd , 15 th ,16 th , 29 th and last slice), each individual slice (0.5 cm in width) was quantitatively a nalyzed for Listeria by surface - plating appropriate dilutions on M OX . After slicing the inoculated produce, one uninoculated produce of the same product type was sliced, after which the same slice order ( 1 st , 2 nd , 15 th ,16 th , 29 th and last sl ice), was sampled and quantitatively analyzed for Listeria . This study was done in triplicate and analysis of variance and the Tukey - Kramer HSD test were performed using JMP 12.0 (SAS Institute Inc., Cary, N.C.). 38 Figure 2 - 1 : Components of the NEMCO model #N55200AN rotating slicer: (A) blade plate , (B) pusher plate, and (C) bottom plate. Figure 2 - 2 : Components of NEMCO model # 59155491 stationary slicer: (A) pusher, and ( B) blade 2.2.5 Listeria transfer from inoculated cucumbers and zucchini to a rotating and stationary hand slicer Tra nsfer of L. monocytogenes to the previously identified contact areas of a rotating and stationary slicer, which yielded 0.5 and 0 . 8 cm - thick slices respectively , was assessed after slicing one inoculated cucumber and zucchini squash. Similarly, after slicing one inoculated followed by 15 uninoculated samples, the same areas of the slicer were sampled to quantify Listeria. For the sampling protocol, one dip - inoculated product was sliced to contaminate the C B A A B 39 slicer. Using one - ply c omposite tissues moistened with 1 ml of sterile phosphate buffer, samples were taken and tested for Listeria (Vorst et al . 2004) . 2.2.6 C leaning and decontaminating the slicer After use, the slicer was completely disassembled, and the slicer pusher and blade were brushed under running water for 2 min. Slicer parts were disinfected with the 10% acidified bleach(vol/vol), and all components were rinsed with deionized water, spread with 70% ethanol (vol/vol) and dried under uv light f or 20 min before use. Follow - up sampling Using one - ply composite tissues moistened with 1 ml of sterile phosphate buffer and tested for Listeria indicated that the slicer was free of Listeria . 2.2.7 Listeria transfer from surface - inoculated cucumber and zucchini to the cut surface using a rotating and stationary hand slicer Transfer of L. monocytogenes from the outer skin/r ind of cucumber and zucchini squash to the cut surface (flesh) was assessed using both the manual rotating and stationary slicer. One cucumber and zucchini were dip - inoculated to contain ~ 7.5 log CFU/ product , air - dried for 1 h , and then sliced using th e rotating or stationary slicer. A total of 5 slices from the middle , each 5.0 ± 0.2 cm in diameter, was generated from each cucumber and zucchini using both slicers . After aseptically removing the skin/rind, all samples were collected and quantitatively a nalyzed for Listeria by surface - plating appropriate dilutions on M OX . 40 2.2.8 Listeria transfer from inoculated to uninoculated cucumbers and zucchini during sequential slicing using a rotating and stationary slicer In these experiments, one cucumber or zucchini squash was dip - inoculated in the avirulent 3 - strain L. monocytogenes cocktail to contain ~ 7. 5 log CFU/ product , air - dried for 1 h , and sliced using the rotating hand slicer or stationary slicer to contaminate the slicer . The reafter, Listeria transfer from the slicer to 15 uninoculated product of the same product type was assessed by sampling each one of the 15 uninoculated product. F or the rotating slicer, t he 1 st and every fifth slice from ea ch of the 15 uninoculated product (total of 6 slices /product ) were collected and quantitatively analyzed for Listeria by surface - plating appropriate dilutions on M OX . for the stationary slicer, the first, middle and last slice from each of the 15 uninoculated product were composited and examined for numbers of Listeria . 2.2.9 Impact of cutting speed on L . monocytogenes transfer during slicing One cucumber or zucchini squash was dip - inoculated in the 3 - strain avirulent L. monocyto genes cocktail to contain ~ 7. 5 log CFU/ product , air - dried for 1 hour , and sliced using the rotating slicer at a constant speed . The slicer shown in Figure 2 was modified by the MSU Biosystems and Agricultural Engineering D epartment by attachi ng electric powered hydraulic s to the slicing blades in order to maintain a constant cutting speed during slicing . After contaminating the slicer by slicing one inoculated sample , 15 uninoculated zucchini or cucumbers were sliced at either high ( 3.3 cm/se c ) or low speed ( 2 .0 cm/sec). The first, middle and last slice from each of 15 uninoculated products were collected and analyzed for Listeria by surface - plating on M OX . 41 2.2.10 D ensity , cutting force , and water content of cucumbers and zucchi ni Computed tomographic (CT) scans were performed on three locally obtained cucumbers Buckinghamshire, United Kingdom). Two - dimensional CT images were acquired ever y 0.625 mm, at a voltage and current of 120keV and 240mA, respectively (Figure 3). The overall mean bulk density for both products (n = 3) was indirectly calculated from all of the 2D CT images (number varied depending on product size) using MATLAB V2012a (Table 1). The Hounsfield unit (HU), known as the CT number, is a quantitative scale for describing radiodensity. Once the mean HU was obtained from the images, the bulk density was calculated by using the following equation (Orsi and Anderson 1999) : Figure 2 - 3 : C omputed tomographic (CT) images for (a) cucumber and (b) zucchini Alongside the CT imaging, standard water displacement method was used to measure the density of cucumber and zucchini. b r iefly, water was added in a graduated cylinder in which a b 42 cucumber a nd zucchini was placed. The amount of displaced water (in milliliters) was recorded as the volume of the product. density was calculated as: p=m/v Texture analyses were based on the force required to shear the sample using a texture analyzer TA - XT2i (Text ure Technologies Corp, Scarsdale, New York) with a custom stainless - steel knife blade (length 22.5 cm, width 17.5 cm) made specifically to simulate a knife cut. Each produce sample (n = 3) was cut five times at a cutting blade speed of 40 mm/sec with the b lade traveling a total distance of 85 mm. P eak positive force was measured and expressed in N. M oisture content was calculated based weight loss after drying at 100 - 105°C for 12 - 24 h . Samples of l ocally obtained cucumbers and zucchin i squash (5 - 7 g each) were placed in aluminum pans , weighed to 4 decimal points , dried overnight in a a forced - air oven at 100°C, removed , and placed into desiccators until completely cooled. Thereafter , the dried product samples were weighed and the moisture content was calculated using the following equation: In addition, the amount of free liquid released during slicing was quantified based on the weight loss of the sample before and after slicing . 2.2.11 Impacted of water content on bacterial transfer using floral foam as a model The effect of water content on transfer of Listeria during slicing was also evaluated using a floral foam (OASIS® Fl oral Products, Kent, OH) . Floral foam to which different amounts of water were added, was used as a model system in order to obtain different moisture contents under the same conditions. The stationary slicer was first contaminated by slicing one inoculate d 43 cucumber. Ten uninoculated pieces of floral foam (length 15 cm, width 2 cm and height 2 cm ) were used to which sterile water was added (150, 100 and, 75 ml) to achieve percent moisture of 97.6, 96.7 and, 95.1 % respectively . The flo ral foam was then sliced and samples were collected and quantitatively analyzed for Listeria by surface - plating appropriate dilutions on M OX . In addition , an uninoculated control experiment was conducted by slicing an uninoculated cucumber to ensure cleann ess of the slicer. The experimental design for this objective is illustrated in Figure 4. Figure 2 - 4 Experimental design for the floral foam experiment 2.2.12 Microbiological analysis: All produce samples were homogenized by stomaching in 50 ml of p hosphate buffered saline (PBS) in a Whirl - pak bag ® for 1 min and then quantitatively analyzed for Listeria by surface - plating appropriate dilutions on Modified Oxford Agar (MOX) . All colonies resembling Listeria were counted after 48 h of incubation at 35 o C . 2.2.13 Statistical analys is All experiments were performed in triplicate. Listeria populations were converted to log CFU/cm 2 or CFU/ product and subjected to ANOVA using JMP 12.0 (SAS Institute Inc., Cary, 44 N.C.). Statistical significance was set at P 0.05 . T he Tukey - Kramer HSD test w as performed using JMP. In order to describe the sequential transfer of Listeria during slicing , the Listeria transfer curve data (log CFU/ product ) were fitted to a two - parameter exponential decay mod el Eq [1] Y = A · e xp ( B · X) where Y (dependent variable) is the log CFU/produce transferred and X (independent variable) is the order number for the specific uninoculated produce that was sliced. A ( Listeria population transferred to the first produ ct ) and B ( decay rate ) are two model parameters. The parameter ± standard error for the aggregate of replications and the root mean square error (RMSE) of the model were obtained using JMP 12.0. In addition, t he percentage of the Listeria population trans ferred from one inoculated to 15 uninoculated samples was calculated as follows : = X 100 45 2.3 RESULTS 2.3.1 Listeria distribution on individual slices After slicing one inoculated cucumber ( 7.6 ± 0. 1 log CFU/ cucumber ), populations on the six cucumber slices ranged from 4.2 ± 0.0 to 4.8 ± 0.1 log CFU/cm 2 with significantly fewer ( 0.05 ) Listeria transferred to slices 2 through 29 compared to the first and last slice (Figure 5 ). However, when one inoculated cucumber followed by one uninoculated cucumber was sliced , a statistically similar ( P > 0.05 ) , Listeria populations on the uninoculated cucumber ranged from 3.4 ± 0.1 to 3.8 ± 0.0 log CFU/cm 2 , were observed . When inoculated zucchini (5.8 ± 0.0 log CFU/cm 2 ) was sliced, a similar trend was observed , whereby populations of Listeria on the six slices ranged from 4.1 ± 0.1 to 5.3 ± 0.1 log CFU/cm 2 with significantly fewer ( P 0.05 ) Listeria transferred to slices 2 through 2 9 compared to the first and last slice. After slicing one inoculated followed by 15 uninoculated zucchini s , Listeria populations ranged from 2.4 ± 0.2 to 3.0 ± 0.1 log CFU/cm 2 with no significant dif ference between any of the slices. Since the inoculated sample was dip - inoculated , high Listeria populations were expected on the first and last slices due to the larger surface area for first and last compared to the middle slices. Based on statistical s imilarity ( P > 0.05 ), the 1 st , every 5 th slice of cucumber and zucchini were used for sampling whenever the rotating slicer was used. 46 Figure 2 - 5 : Mean (± SE) L. monocytogenes distribution on slices from inoculated and uninoculated cucumber (a) and zucchini (b) after slicing with a rotating slicer. Means with dif ferent capital letters for inoculated slices are significantly different ( P different letters for uninoculated slices are significantly different ( P 2.3.2 Listeria transfer from inoculated produce to a rotating and stationary han d slicer When one inoculated cucumber was sliced using the rotating and stationary slicers, the percentage of Listeria cells transferred to the slicers were statistically similar ( P > 0.05) , 0.4 ± 0.1 0 1 2 3 4 5 6 First 2nd 15th 16th 29th Last Log CFU/cm 2 Cucmber slice Inoculated Uninoculated A C AB C BC C (a) a a a a a a 0 1 2 3 4 5 6 First 2nd 15th 16th 29th Last Log CFU/cm 2 Zucchini slice inoculated uninoculated A A B B B B a a a a a a (b) 47 and 0.7 ± 0.3 , respectively. S imilarly , w hen o ne inoculated zucchini was sliced using the rotating and stationary slicers, the percentage of Listeria cells transferred to the slicers were statistically similar ( P > 0.05), 1 .4 ± 0. 2 and 0. 9 ± 0. 2 , respectively . After slicing one inoculated zucchini squa sh on the rotating slicer, the slicing plate, bottom plate , and pusher plate yielded Listeria populations of 4.2 ± 0.1, 3.4 ± 0.4, and 4.5 ± 0.1 log CFU/component, respectively (Figure 6 A). A fter slicing 15 uninoculated zucchinis , Listeria population s f or the same slicer component decreased to 3.3 ± 0.3, 3.5 ± 0.1 and 2.7 ± 0.7 log CFU/component and were not statistically different from the same component after only slicing one zucchini ( P > 0.05 ) . Similarly, for cucumbers no significant differences in Listeria populations (P > 0.05 ) were observed for any of the three components either before or after slicing 15 uninoculated cucumbers. However, when the stationary slicer was used to slice inoculated zucchini, the blade and pusher yielded Listeria popu lations of 3.9 ± 0.2 and 4.8 ± 0.2 log CFU/component, respectively. These numbers decreased significantly (P 0.05 ) for the same slicer component after slicing 15 uninoculated zucchinis to 3.1 ± 0.1 and 2.8 ± 0.1 log CFU/component (Figure 6 B). Similar trends were also seen for the same components when one inoculated cucumber was sliced followed by 15 uninoculated cucumbers. Listeria populations on the same slicer components decreased significantly (P < 0.05 ) after slicing 15 uninoculated cucumb ers . 48 2.3.3 Listeria transfer from surface - inoculated cucumber and zucchini to the cut surface using a rotating and stationary hand slicer Cucumber slices yielded statistically similar Listeria populations of 4 .7 ± 0. 1 , 4.7 ± 0.1 log CFU/ cm 2 after using the rotating and stationary slicer, respectively , with Listeria populations in the flesh of 1.7 ± 0.1, 1.4 ± 0.1 log CFU/ cm 2 also statistically similar for both slicers ( Figure 7A ) . When zucchini was sliced, the skin yielded Listeria populations of 5.3 ± 0. 05 , 5.4 ± 0.0 3 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Zucchini Cucumber Log CFU/component Slicing plate (initial) Bottom (initial) Pusher (initial) Slicing plate (final) Bottom (final) Pusher (final) A 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Cucumber Zucchini Log CFU/component Pusher (initial) * * * * B Figure 2 - 6 : Listeria distribution (mean ± SE) on different components of the rotating slicer (A) and stationary slicer (B) before and afte r slicing 15 uninoculated zucchini and cucumber s . Columns with asterisks are significantly different (P 0.05 ) from the corresponding component 49 log CFU/ cm 2 , respectively, which were statistically similar ( P > 0.05 ). However, unlike cucumber, statistically higher Listeria populations were seen in zucchini flesh using th e rotating ( 2.4 ± 0.1 log CFU/ cm 2 ) as opposed to the stationary slicer ( 1.7 ± 0. 08 log CFU/ cm 2 ) ( Figure 7B ) . . Figure 2 - 7 : Listeria populations (mean ± SE) on different locations of a cuc umber (A) and zucchini slice (B). Columns with asterisks are significantly different ( P 0.05) from the corresponding location . 2.3.4 Listeria transfer from inoculated to uninoculated cucumbers and zucchi ni during sequential slicing using a rotating and stationary slicer After slicing one inoculated zucchini or cucumber (~ 7 log CFU/product) followed by 15 uninoculated samples of the same product type, Listeria was detectable in all 15 samples using 0 1 2 3 4 5 6 Rotating slicer Stationary slicer Log CFU/cm 2 A: Cucumber Skin Flesh 0 1 2 3 4 5 6 Rotating slicer Stationary slicer Log CFU/cm 2 B: Zucchini Skin Flesh * 50 eith er the stationary or rotating slic er (Figures 8 and 9). These results were then fitted into a two - parameter exponential decay model to compare the decay rates (Table1). When the rotating slicer w as observed compared to zucchini (0.01±0.002). However, when using the stationary slicer, a statistically similar (P > 0.05) decay rate was seen for cucumber (0.008±0.002) as opposed to zucchini (0.01 ± 0.005). The RMSE for all processing variables ranged from 0.33 to 0.65 log CFU/produce, which supported the exponential decay model. The original transfer data for each variable is presented in Appendix A. Figure 2 - 8 : Listeria transfer from an inoculated product (~ 7 log CFU/product) to 15 inoculated zucchini and cucumber using a stationary slicer 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log CFU/product Uninoculated product number Cucumber Zucchini Produce inoculation level 51 Figure 2 - 9 : Listeria transfer from a n inoculated product (~ 7 log CFU/product) to 15 inoculated zucchini and cucumber using a rotating slicer Table 2 - 1 : Transfer model parameters (A and B) for Listeria from inoculated zucchini and cucumber to the stationary and stationary slicer during sequential slicing and percent transfer (n = 3) Product Slicer type A ± SE (Log CFU / P roduct B ± SE R MSE 1 (log CFU / P roduc t Cucumber Rotating 5.3 ±0.2 0.0 2 ±0.00 5 a2 0.6 5 Sta tionary 5.1 ±0.1 0.0 08 ±0.00 2 b 0.3 2 Zucchini Rotating 5.7 ±0.1 0.01±0.00 2 a 0.41 Stationary 5. 1 ±0.2 0.01±0.00 5 a 0.65 1 RMSE: root mean square error for the exponential decay model. 2 Means with the same letters for the slicer type are not significantly different ( P > 0.05) 2.3.5 Impact of cutting speed on L. monocytogenes transfer during slicing After zucchini slicing, L. monocytogenes was quantifiable in all samples examined as shown in Figure 10. Overall, the 1 st , 7 th , and 15 th uninoculated zucchini s y ielded average Listeria 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log FCU/product Uninoculated product number Cucumber Zucchini Produce inoculation level 52 populations of 5.5 ± 0. 2 , 5.2 ± 0. 2 and 4.8 ± 0.3 log CFU/zucchini, respectively, when sliced at high speed. Sequentially slicing the 1 st , 7 th , and 15 th zucchini at low speed resulted in average Listeria populations of 5.4 ± 0.3, 4.9 ± 0. 5 and 4.5 ± 0.1 log CFU/zucchini. A similar trend was observed during slicing cucumber at different speeds with the 1 st , 7 th , and 15 th uninoculated cucumber yielding average Listeria population s of 5.5 ± 0.2, 4.8 ± 0.1 an d 4.4 ± 0.1, and 5.3 ± 0.1, 5.4 ±0.3 and 4.8 ± 0.1 log CFU/cucumber at high and low speed, respectively (Figure 1 0 ). After fitting th ese data in the previous exponential decay model, the decay rates for both zucchini and cucumber (Table 2) were statistically similar ( P > 0.05) Figure 2 - 10 : L. monocytogenes transfer from inoculated to uninoculated cucumber and zucchini during slicing at high (3.3 cm/sec) and low speed (2.0 cm/sec) 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log CFU/product Zucchini number High speed Low speed 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log CFU/product Cucumber number High speed Low speed 53 Table 2 - 2 : Transfer model parameters (A and B) for Listeria from inoculated zucchini and cucumber to the slicer during sequential slicing at high and low speed (n = 3) Product Slicing Speed A ± SE (Log CFU/produce B ± SE RMSE (log CFU/produce) Zucchini High (3.3 cm/sec) 5.3±0.2 0.01±0.004 0.65 Low (2 .0 cm/sec) 5.6 ± 0.1 0.01 ± 0.003 0. 56 Cucumber High (3.3 cm/sec) 5.1±0.1 0.01±0.003 0.33 Low (2 .0 cm/sec) 5 .5 ± 0.1 0.01 ± 0.00 2 0.43 2.3.6 Produce density , cutting force and water content A significantly (P 0.05 ) greater force was required to cut through cucumber (35.3 ± 0.3 N) compared to zucchini (10.8 ± 0.6 N) (Table 3) since the density of cucumber was significantly higher. Although the water content was similar for both cucumber and zucchini (P>0. 05) , the amount of liquid released during slicing varied. When cucumber was sliced using the rotating and stationary slic er , the amount of free liquid was 7.5 ± 0.5 and 1.9 ± 0.2 g, respectively , which was significantly higher than for zucchini using the r otating (2.1±0.1 g) and stationary slicer (0 .3 ± 0.04 g ) . Table 2 - 3 : Mean (± SE) peak positive force, density, and water content Means with different letters for different produce are significantly different ( P Means with different capital letters for different slicers are significantly different ( P Product Mean peak positive force (N) D ensity (g/cm 3 ) using water displacement D ensity (g/cm 3 ) using CT scan Water content (%) Free liquid when slice d with rotating slicer (g) Free liquid when sliced with stationary slicer (g) Zucchini 10.8 a ± 0.6 0.94 a ± 0.02 0.4910 a ±0.08 95.6 a ±0.03 2.1±0.1 a 0.3±0.04 a Cucumber 35.3 b ± 0.3 0.98 b ± 0.04 0.6567 b ±0.02 94.1 a ±0.03 7.5±0.5 b 1.9±0.2 b 54 2.3.7 Impact of water content on Listeria transfer using floral foam as a model At all three floral foam percent moisture levels ( 95.1, 96.7 and, 97.6% ), both the pusher and the blades yielded statistically ( P Listeria populations after slicing one inoculated cucumber followed by 10 uninoculated pieces of floral foam as compared to Listeria populations recovered after slicing one inoculat ed cucumber (Figure 1 1 ). When the transfer data (Figure 1 2 ) were fitted to the exponential decay model to determine decay rates, (Table 4), all three percent moisture levels ( 95.1, 96.7 and, 97.6%) resulted a statistically similar decay rates. Figure 2 - 11 : Listeria distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated pieces of floral foam at percent moisture levels of 95.1, 96.7 and, 97.6% 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Percent moisture 97.6% Percent moisture 96.7% Percent moisture 95.1% Log CFU/component Pusher (initial) Blade (initial) Pusher (final) Blade (final) 55 Figure 2 - 12 : Sequential transfer of Listeria during slicing of floral foam at percent moisture levels of 95.1 , 96.7 and , 97.6 % Table 2 - 4 : Model parameters (A and B) for transfe r of Listeria from inoculated cucumber to 15 uninoculated pieces of floral foam at percent moisture levels of 95.1 , 96.7 and , 97.6 % (n = 3). P ercent moisture levels A ± SE (Log CFU/produce B ± SE RMSE (log CFU/pro duce) 97.6 % 4.5 ± 0.4 0. 07 ± 0.0 2 0.6 7 96.7 % 5 .8 ± 0.5 0. 1 ± 0.0 4 0. 64 95.1 % 4.6 ± 0. 5 0. 08 ± 0.0 5 0. 75 0 1 2 3 4 5 6 7 8 0 2 4 6 8 10 Log CFU/ floral foam Floral foam order Percent moisture 97.6% Percent moisture 96.7% Percent moisture 95.1% 56 2.4 DISCUSSION: The overall objective of this study was to assess the effect of slicer t ype and compositional differences between cucumber and zucchini on the transfer of L. monocytogenes . In this study, after slicing one inoculated sample followed by fifteen uninoculated samples, Listeria populations on different parts of the stationary slic er decreased significantly ( P 0.05 ). However, the different parts of the rotating slicer were able to retain a greater proportion of the initial Listeria population transferred to the same part. This may be partially explained by the larger contact area between the product and slicer , which allowed more Listeria to be transferred initially. In addition , the direction of the force applied during slicing in the rotating slicer could explain the larger transfer of Listeria to the rind. Observations using the stationary slicer are consistent with a previous cross - contamination study by Scollon et al. (2016) where numbers of Listeria before and after slicing 20 onions decreased significantly using a similar stationary slicing mechanism. The sequential transfer of Salmonella during to mato slicing using two different types of slicers (electric vs. manual) was previously investigated by Wang and Ryser (2016) . Similar transfer trends were seen for both slicers except for the blades of an el ectr ic slicer that yielded greater Salmonella transfer compared to the blades of the manual slicer. However, both slicers used in the Wang and Ryser (2016) experiment are similar to the stationary slicer used in this study. The decay rate and percent recovery for Listeria after one zucchini or cucumber inoculat ed at ~ 7 log CFU followed by 15 uninoculated product samples varied greatly. When slicing 15 zucchini or cucumber with the rotating slicer, the blade s contacted the product approximately 450 times compared to only 15 times for the stationary slicer due to the different 57 slicing mechanism. This could partially explain the similar decay rate for Listeria transfer when the stationary slicer was used , while the higher contact times between product and rotating slicer allowed more of a washing effect, which resulted in a higher decay rate for cucumber compared to zucchini . Another difference between the stationary and rotating slicing mechanism is the directi on of force applied during slicing which might have resulted in greater transfer with the rotating slicer since the added force applied could increase Listeria transfer . Meanwhile, the force applied from above resulted in less transfer when the stationary slicer was used. Application of pressure has been shown to effect bacterial transfer . For example, Vorst et al. (2006) reported significantly greater ( P 0.05 ) transfer of Listeria to the table and back plate of a mechanical delicatessen slicer when a force of 4.5 kg as opposed to 0 was applied against the product during slicing. Bower et al . (1996) suggest that in bacterial adhesion, the increase in pressure pushes surfaces closer, avoiding the initial repulsion forces and enabling binding forces. Tomatoes of varying texture have yielded diff erent transfer rates during slicing as reported by Wang and Ryser (2016) . They were able to show that tomato varieties with tougher texture and lower water content transferred more Salmonella during slicing compared to softer, moister varieties. Vorst et al. (2006) repor ted less L. monocytogenes transfer during mechanical slicing of delicatessen turkey breast compared to salami with similar findings also reported for delicatessen hams containing different levels of water (unpublished data). This could be explained by the cells available for transfer. Therefore, it is recommended that when using a rotating slicer, cucumber should be sliced first to minimize cross - contamination during fresh - cut pr ocessing. Decay rates after sequential transfer of Listeria during slicing of zucchini and cucumber at different speeds w ere statistically similar ( P > 0.05). This observation contradicts work done 58 by Mazon (2017) (unpublished data ) who found that bacterial transfer via dynamic contact from a stainless steel plate to a potato increased as sliding speed increased. However, the transfer process dynamics of these two studies varied greatly, which may partially explain the different results obtained as well as the difference between high and low speed slicing in our experiment , which migh t have been insufficient to impact bacterial transfer . The use of floral foam to investigate the effect of water content on bacterial transfer during slicing has not been previously attempted. The water saturation percentages were chosen to represent the varying water content of fresh produce. Although t he low numbers of Listeria recovered after slicing negatively affected the model par ameter resulting in high RMSE values , decay rate were statistically similar in all saturation level . T hese findings contradict those from other studies (Jensen et al. 2013; Miranda and Schaffner 2016; Wang and Ryser 2016) who showed that high moisture produc ts facilitate greater bacter ial transfer which is likely due to low Listeria recovery during slicing of floral foam as well as the relatively small difference in water saturation levels used in this experiment. In summary, this study clearly shows that the product and type of slicer both influence the numbers of Listeria transferred. Therefore, the order in which different types of fresh produce are sliced, along with type of slicer used, are important considerations when attempting to minimize potential cross - contamination during slicing. To further investigate these claims, the effect of various intrinsic parameters of fresh produce, including firmness and surface texture, were assessed in the following chapter. Such practical research should be of interest to the fresh - cut produc e industry , since this work serves to lay the foundation for the development of more reliable science - based transfer models for risk assessments. 59 CHAPTER 3 : 3 Quantify Listeria and Salmonella transfer during slicing of different fresh cut produces as imp acted by produce firmness and other physiological characteristics 60 3.1 OBJECTIVE The objective of this study is to quantify the impact of pear firmness and other product characteristics (water content, pH, cutting force , so luble solids content, surface hydrophobicity and surface roughness) on the transfer of Listeria and Salmonella during slicing of different types of fresh produce (onions, radishes, tomatoes, potatoes, carrots, zucchini, cantaloupe, apple, sweet potato, gr a y zucchini and cucumber) . 61 3.2 MATERIALS AND METHODS: 3.2.1 Microbial cross - contamination of pears during slicing as impacted by pear firmness The effect of pear firmness on the transfer of Listeria and Salmonella was assessed in this study. Th ree categories of pear firmness based on ripeness were assessed for slicing: firm (10 - 15 N), medium (6 - Pyrus communis 'Williams pear') w ere obtained from a local shipper and stored at 4 C until use. After reachi ng the desired firmness, one pear was dip - inoculated with the avirulent L. monocytogenes cocktail (M3, J22F and J29H) as well as a 3 - strain cocktail of Salmonella (Montevideo, Poona, Newport) at ~7 log CFU/ pear and air - dried in a bio - safety cabinet for 1 h before slicing using a NEMCO slicer # 59155491(Nemco Food Equipment Inc., Hicksville, OH). After slicing the inoculated pear to contaminate the slicer, 15 uninoculated pears of the same firmness category w ere sliced to assess the extent of bacterial trans fer during slicing. The slicer was modified by the MSU Biosystems and Agricultural Engineering D epartment to allow control of the cutting speed during slicing. In addition, every trial had an uninoculated control whereby one uninoculated product was sliced before the inoculat ed to ensure that the slicer was disinfected. Furthermore, the blades and pusher were sampled for Listeria and Salmonella before and after slicing the 15 uninoculated samples to account for all bacteria transferred. S urface roughness, h ydrophobicity and physiological characteristics of the pears in all firmness categories w ere also measured. 3.2.2 Pears firmness categories As pears ripen, the firmness changes. As described by Colás - Medà et al . ( 2015) , pears w ere ripened at 20 C for a maximum of 72 h until the desired firmness was achieved. Pear firmness was quantitatively assessed by measuring the force in N re quired to penetrate the fruit 62 using a texture analyzer TA - XT2i (Texture Technologies Corp, Scarsdale, New York) equipped with a 8 mm diameter probe. Pears subsequently categorized as firm (10 - 15 N), medium (6 - 9 N) and soft (< 6 N) were then held overnigh t at 4 C before slicing. 3.2.3 Produce selection and slicing In the second objective, different types of produce w ere selected for slicing based on their physicochemical characteristics (water content, surface roughness , and firmness.). These fr uits and vegetables include Spanish yellow onions ( Allium cepa ) , Red round tomatoes (Solanum lycopersicum L .) , R adish ( Raphanus sativus ) , C ucumber ( Cucumis sativus ) , P otato ( Solanum tuberosum ) , C arrot ( Daucus carota ) , S weet potato ( Ipomoea batatas ) , Apple ( Pyrus malus ) , Cantaloupe ( Cucumis melo var. cantaloupensis ) , Zucchini, ( Cucurbita pepo ) , Gray zucchini ( Cucurbita pepo ) , Pyrus communis ) purchased from a local supplier (Stan Setas Produce Company, Lansing , MI) . All products were sliced using a NEMCO # 59155491(Nemco Food Equipment Inc., Hicksville, OH) stationary slicer that was modified to allow slicing at a fixed speed. 3.2.4 Bacterial strain and produce inoculation Three avirulent L. monocytogenes strains - M3 serotype 1/2a (Hly - , parent strain Mackaness), J22F serotype 4b (Hly + , purB mariner - based mutant of H7550 - Cd S , parent strain NCTC 10527), and J 29H serotype 4b (Hly - , parent strain NCTC 10527) (obtained from Dr. Sophia Karthariou, North Carolina State University, Raleigh, NC) were used for inoculating the differen t produc ts . All strains were stored at - 80°C in trypticase soy broth containing 0.6% yeast extract (TSBYE) and 10% glycerol, and subjected to two successive transfers (24 h at 37°C) in TSBYE before inoculation. T he three avirulent strains were combined in equal volumes and 63 appropriately diluted to obtain populations of ~ 6.0 log CFU/ml for produce inoculation , with these levels confirmed by plating appropriate dilutions on Modified Oxford Agar (MOX, Neogen Corp., Lansing, MI). Produce was dip - inoculated in the L. monocytogenes cocktail and air - dried in a bio - safety cabinet for 1 hour before processing. similarly , a 3 - strain Salmonella cocktail including Salmonella Montevideo MDD22 (tomato outbreak, human isolate), Salmonella Poona MDD237 (cantaloupe outbreak, human isolate), and Salmonella Newport MDD314 (tomato outbreak, environmental isolate) (Dr. Lawrence Goodridge, Colorado State University, Fort Collins, CO) were used to inoculate th e pears of different firmness categories. 3.2.5 Quantify Listeria transfer during slicing of different fresh cut produces O ne inoculated sample was sliced with a mechanical slicer to contaminate the slicer after which 15 uninoculated samples of the same prod uce type were sliced to assess the extent of L. monocytogenes transfer. Moreover, the transfer of L. monocytogenes to different parts of a stationary hand slicer, which yielded 0.5 cm - thick slices, was similarly assessed by slicing one inoculated sample fo llowed by 15 uninoculated samples of the same produce type , after which the various slicer parts were sampled using the one - ply composite tissue method of Vorst et al . (2004 ) to quantify Listeria . 3.2.6 Physicochemical characteristics measurements of produ ce Before slicing, water content, the pH and the soluble solids content (SCC) w ere measured. A forced air drying oven was used to measure water content. The pH was measured by using a HANNA® HI 221 pH meter with a penetration electrode. After the pH reading, products were squeezed, and the soluble solids content (SSC) w as determined by using a handheld optical refractometer F isher S cientific ® refractometer at 20 o C. 64 3.2.7 Surface roughness determination Surface roughness measuremen ts for the internal (flesh) and external surface (skin, rind) were obtained for all products as described by Wang et al. (2009) . Briefly, a 1 c m 2 produce section cut from the interior was placed on microscope slide . Confocal Laser Scanning Microscopy (CSLM) was conducted at The Center for Advanced Microscopy ( Michigan State University ) using a Nikon C2 Confocal Microscope which a llowed 2 - to be obtained by optically slicing the sample surface. S eparation between the observation planes ts . S urface profile information was expr essed by parameter ( Ra ), which is the arithmetic average of the absolute values of the surface height deviations measured from the mean plane calculated using ImageJ software. 3.2.8 Surface hydrophobicity assay Surface hydrophobicity of the produce w as measu red using a goniometer . Briefly , small drops of deionized (DI) water 2 to 4 mm in diameter w ere created using a microsyringe. A side view photograph of the drop at a magnification of approximately 7.6 times was obtained with an inspection microscope and m irror a greater contact angle between the drop and produce surface indicate greater hydrophobicity 3.2.9 Microbiological analysis All produce samples were homogenized by stomaching in 50 ml of p hosphate buffered saline (PBS) in a Whirl - pak bag ® for 1 min and then quantitatively analyzed for Listeria by surface - plating appropriate dilutions on Modified Oxford Agar (MOX) . All colonies resembling 65 Listeria were counted after 48 h of incubation at 35 o C. Salmonella was enumerated by surface - pla ting appropriate dilutions on trypticase soy agar (BD) containing 0.6% yeast extract (BD), 0.05% ferric ammonium citrate (Sigma) and 0.03% sodium thiosulfate (Fisher Science Education, Hanover, IL) (TSAYE - FS ). Plates were incubated at 37°C for 24 h, after which all black colonies were counted as Salmonella . 3.2.10 Statistical analysis All transfer experiments were performed in triplicate. Listeria and Salmonella populations were converted to CFU per cm 2 and/or CFU per unit . The percentage of the Listeria population transferred from one inoculated to 15 uninoculated samples was calculated as follows : = X 100 Also, the percentage the Listeria population recovered from an inoculated slicer after slicing 15 uninoculated product w as calculated by adding the total CFU on inoculated product after slicing (CFU) and Listeria population transferred to 15 uninoculated product (CFU) and t otal Listeria population left on slicer (CFU) divided by the total CFU on inoculated product before sl icing (CFU) multiply by 100 . Analysis of variance and the Tukey - Kramer HSD test w ere performed using JMP to compare percent transfer and recovery. For the multiple comparison of the decay parameter for the different types of produce, the t - test was used after adjusting the p - value using the Bonferroni method. Statistical significance was set at P 0.05 . 66 3.2.11 A primary exponential decay model Listeria transfer curve data (log CFU/ product ) were fitted to a two - parameter exponential decay Eq: Y = A · e xp ( B · X) where Y (dependent variable) is the log CFU/ product transferred and X (independent variable) is the order number for the specific uninoculated produce that was sliced. A ( Listeria population transferred to the first product) and B (decay rate) are two model parameters. The parameter ± standard error for the aggregate of replications and the root mean square error (RMSE) of the model were obtained using JMP 12.0. D ata below LOD were not included in the model. 3.2.12 A secondary multiple linear model The following linear model was used to describe the effect of physicochemical characteristics , such as water content, pH, firmness and surface roughness on the bac terial decay rate during slicing : Eq: B 0 1 x 1 2 x 2 3 x 3 4 x 4 5 x 5 6 x 6 w 0 is an intercept, and B (dependent variable) is the decay rate after slicing 15 un - inoculated products . The lin ear model has six independent variables : x 1 (pH), x 2 ( w ater content (%) ), x 3 ( cutting force (N) ), x 4 ( s oluble solids content ( o Brix) ), x 5 ( s urface hydrophobicity ( o ) ), x 6 ( s urface roughness ( µm ) ) , and xk ( product type) with as the random error. the model w ere obtained using JMP 12.0 . 67 3.3 RESULTS: 3.3.1 Microbial cross - contamination of pears during slicing as impacted by pear's firmness: A t all firmness categories, both pusher and blades yielded significantly ( P 0.05 ) lower Salmonella populations after slicing one inoculated pear followed by 15 uninoculated pear s as compared to after slicing one inoculated pear (Fig ure 1). Moreover, a cross all firmness categories, the reduction in Salmonella population s on the slicer (Fig ure 2) before and after slicing 15 uninoculated pears was statistically similar ( P > 0.05 ). After slicing one inoculated followed by 15 uninoculated pears, all firmnes s categories yielded detectable levels of Salmonella in one or more replicates (Fig ure 3). For t he high firmness category, pear s 1, 9 and 15 respectively yielded average Salmonella populations of 4.7 ± 0. 1 , 3.1 ± 0. 4 , and 2.7 ± 0.0 0 log CFU/ p ear , which w ere statistically similar ( P > 0.05 ) to both medium and soft pears. Similar trends were observed when Listeria was used to contaminate the slicer. At all firmness categories, both the pusher and blades yielded significantly ( P 0.05 ) lower Li steria populations after slicing one inoculated pear followed by 15 uninoculated pear s as compared to after slicing one inoculated pear ( Fig ure 4 ). T he reduction in Listeria on the slicer (Fig ure 5) before and after slicing 15 uninoculated was also statist ically similar ( P > 0.05 ) across the three firmness categories. Finally, after slicing one inoculated followed by 15 uninoculated pears , Listeria was sporadically detected in one or more replicates , regardless of pear firmness (Fig ure 6 ) . For t he high firm ness category, pear s 1, 9 and 15 respectively yielded average Listeria populations of 2.8 ± 0.4, 1.3 ± 0.3, and 1.2 ± 0. 5 log CFU/pear, which w ere statistically similar ( P > 0.05 ) to both medium and soft pears. 68 When these results were fitted t o a previously published two - parameter exponential decay model (Scollon, et al ( 2016 ) ; Wang and Ryser ( 2016 ) ) , estimated Salmonella populations were 4. 6 , 5. 0 , and 4.9 log CFU/pear for the firm, medium, and soft pear trials after slicing one inoculated pear , resp ectively . Similar transfer ( P > 0.05) decay rates of 0.04, 0.03, and 0.0 3 , were observed for the firm, medium, and soft pear tr ials , respectively, with a RMSE less than 0.75 log CFU/pear , indicating a relatively good fit (Figure 7 ) . Meanwhile, Listeria esti mate s were 4.4, 5. 0 , and 4. 9 log CFU/pear for the firm, medium, and soft pear tr ia ls after slicing one inoculated pear. The transfer decay rates of 0.02, 0.0 4 , and - 0.03, observed for the firm, medium, and soft pear tr ia ls, respectively, were statistically similar with a RMSE less than 0.58 log CFU/pear , indicating a relatively good fit (Figure 8 ) . Figure 3 - 1 : Salmonella distribution (mean ± SE) on dif ferent components of a stationary slicer before and after slicing 15 uninoculated firm, medium and soft pear. Columns with asterisks are significantly different ( P 0.05) from the corresponding component. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Firm pear Medium pear Soft pear Log CFU/ component Pusher (initial) Blade (initial) Pusher (final) Blade (final) * * * * * 69 Figure 3 - 3 : Sequential Salmonella transfer during slicing of pears 0 0.2 0.4 0.6 0.8 1 1.2 1.4 firm medium soft Log CFU/slicer A A A 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log CFU/cm 2 Pear number Firm Medium Soft Figure 3 - 2 : Reduction in Salmonella population s on the before and after slicing 15 uninoculated firm, medium , and soft pears. 70 Figure 3 - 4 : Listeria distr ibution (mean ± SE) on different component s of a stationary slicer before and after slicing 15 uninoculated firm, medium , and soft pear s . Figure 3 - 5 : Re duction of Listeria population s on the slicer (mean ± SE) before and after slicing 15 uninoculated firm, medium and soft pears . 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Firm pear Medium pear Soft pear Log CFU/ Component Pusher (initial) Blade (initial) Pusher (final) Blade (final) * * * * * 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 firm medium soft Log reduction CFU on slicer A A A 71 Figure 3 - 6 : Listeria transfer during slicing of pears 0 1 2 3 4 5 6 7 0 2 4 6 8 10 12 14 16 Log CFU/pear Pear number Firm Medium Soft 72 Figure 3 - 7 : Predicted Salmonella transfer from on e inoculate pear ( f irm, medium, and soft) to 15 uninoculated sample. y predicted is the line of prediction; y observed is the observed line for 3 trails; Confidence intervals is the confidence intervals for the line of prediction. 0 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/pear Sample number Firm pears 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/pear Sample number Medium pears 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/pear Sample number Soft pears y predicted Confidence in tervals y observed for 3 trails 73 Figure 3 - 8 : Predicted Listeria transfer from one inoculate pear (Firm, medium, and soft) to 15 uninoculated sample. y predicted is the line of prediction; y observed is the observed line for 3 trails; Confidence intervals is the co nfidence intervals for the line of prediction. 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LogCFU/pear Sample number Firm pear 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/pear Sample number Medium pears 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/pear Sample number Soft pear y predicted Confidence intervals y observed for 3 trails 74 3.3.2 Quantify Listeria transfer during slicing of different fresh cut produce : For all produc ts sliced, both the pusher and blades yielded significantly ( P 0.05 ) lower Listeria populations after slicing one inoculated product followed by 15 uninoculated product s as compared to Listeria populations recovered after slicing one inoculated product (Fig ure 9 ). However, the reduction in Listeria population varied across products sliced , with significantly ( P 5 ) less reduction for tomatoes (0.7 9 ± 0. 1 l og CFU/slicer) and potatoes (0. 83 ± 0. 3 l og CFU/slicer) as compared to zucchini ( 1.9 ± 0. 1 l og CFU/slicer) ( Fig ure 10 ). Inoculation level for product after dip - inoculating in ~ 6 log cfu/ml, initial transfer from inoculated product ( ~5.5 Log CFU/cm 2 ) to slicer, the percentage of the Listeria population transferred from one inoculated to 15 uninoculated samples, and the percent recovery of Listeria is summarized in table 1. P ercent transfer during sequential sl icing of one inoculated produc t to 15 uninoculated samples was affected by product type. Transfer was significantly ( P ) higher for c antaloupe (41.1 ± 14.4%) and tomato (41.1 ± 16.1 %) compared to potatoes (2.3 ± 0.8%), onion s (2.2± 0.4%), radish es (1 .4 ± 0.9%) and pears (0.49 ± 0.2%). Listeria was not detectable during slicing of carrots . Although affected by slicer sampling method, t he percent recovery for all product varied greatly with significant higher recovery for tomato ( 2002.41 ± 607 %). as opposed to radish ( 16.2 ± 11.4 %). For each of the nine produ cts assessed for Listeria transfer, a previously described two - parameter exponential decay model was fitted to the Listeria populations obtained during the slicing of 15 uninoculated samples . M odel parameters and RMSE are shown in Table 2 , along t he line of prediction, the observed line for 3 trails and, the confidence intervals for the line of 75 prediction are shown in (Fig ure 11 ) . The decay rate (parameter B) ranged from 0.008±0.002 for cucumbers to 0.09±0.4 for radishes. The multiple comparisons for the decay rate across all products are ( summarized in Table 3 ) shows that decay rate s are significantly different between products ( P ). The RMSE ranged from 0.25 for gray zucchini to 0.68 log CFU/ produce for onion , indicating a relatively good fit . The physicochemical measurements including water content, cutting force , pH, surface, soluble sol ids content, surface hydrophobicity , surface roughness , are summarized in Table 4 . When these physiological characteristics including the products were fitted into a generalized linear model to describe their impact on the decay rate during slicing, the mo del was heavily dependent on the product type with a statistical significant ( P ) . However, pH, cutting force , and surface hydrophobicity had were more statically relevant as oppose to the other physicochemical characteristics. The predicted decay rates for all the products is summarized in table 2. The individual component of the regression model is shown in figure 12. 76 Figure 3 - 9 : Listeria distribution (mean ± SE) on different component s of a stationary slicer before and a fter slicing 15 uninoculated prod uct samples . Figure 3 - 10 : Reduction of Listeria population s on the slicer (mean ± SE) before and after slicing 15 un inoculated produc t samples . Means with different letters for produce are significantly different ( P 0 1 2 3 4 5 6 Log CFU/ component Pusher (initial) Blade (initial) Pusher (final) Blade (final) 0 0.5 1 1.5 2 2.5 Log reduction CFU on slicer AB AB A B B B AB AB AB AB AB 77 Table 3 - 1 : Inoculation level for product after dip - inoculating in ~ 6 log cfu/ml , initial transfer f rom inoculated product ( ~ 7 .5 Log CFU/ product ) to slicer, t he percentage of the Listeria population transferred from one inoculated to 15 uninoculated samples , and the percent recovery of Listeria Product Inoculation level (Log CFU/ product ) Initial transfe r (%) % T ransfer R ecovery (%) Apple 7.7 ±0 .1 b cd 1.3 ± 0.6 a 14.4 ± 14.0 b 0.9 ± 0. 1 cd Cantaloupe 7.9 ±0.05 b c 0.16±0.06 a 41.1 ± 14.4 a 0.2 ± 0.1 d Cucumber 7.6 ±0.1 bcd 0.2 ± 0.03 a 5.2 ± 0.6 b 11.9 ± 2.8 abc d Gr a y zucchini 7.7 ±0.1 bcd 0.2 ± 0.1 a 6.1 ± 2.7 b 17.3 ± 2.8 ab Oni on 7.5 ±0.1 d 1.2 ± 0.6 a 2.25 ± 0.4 b 13.3 ± 6.8 ab c d Pear 7.9 ±0.06 b 0.03 ±0. 01 a 0.4 ± 0.2 b 20.3 ± 8.5 a Potato 8.4 ±0.1 a 0.1 ± 0.06 a 2.3 ± 0.8 b 1 5.8 ± 4.2 ab c Radish 7.5 ±0. 08 d 12.5 ± 3.2 b 1.4 ± 0.9 b 0.8 ± 0.1 c d Sweet potatoes 8.3 ±0.1 a 0.2 ± 0.1 a 1.6 ± 0.4 b 13.4 ± 5.1 abc d T omato 7.6 ±0. 1 cd 1.8 ± 1.0 a 41.0 ± 16.1 a 4.0 ± 2.0 bcd Zucchini 7.6 ±0.0 5 bcd 0.3 ± 0.1 a 4.0 ± 2.4 b 25.1 ± 10.2 a 78 Table 3 - 2 : Transfer model parameters (A and B) and predicted decay rate during transfer of Listeria from inoculated produce to the slicer during sequential slicing (n = 3) PRODUCE A ± SE (LOG CFU/PRODUC E B ± SE RMSE (LOG CFU/PRODU CE) PREDICTED DECAY RATE B RADISH 3.1 ± 0. 1 0.09 ± 0.01 0.41 0.09 TOMATO 5.5 ± 0.1 0.0 4 ± 0.00 2 0.3 3 0.0 3 CANTALOUPE 2.9 ± 0.14 0.04 ± 0.0 06 0.39 0.0 3 ONION 4.7 ± 0.2 0.0 3 ± 0.0 06 0.6 8 0.0 3 SWEET POTATO 5.5 ± 0.1 0.0 3 ± 0.00 3 0. 40 0.0 3 APPLE 4.1 ± 0. 2 0. 0 2 ± 0. 0 1 0. 56 0.0 5 PEAR 4.4 ± 0. 2 0. 0 2 ± 0. 0 06 0. 5 7 0. 03 POTATOES 5.8 ± 0.1 0. 0 2 ± 0. 00 2 0. 3 4 0.02 ZUCCHINI 5.1 ± 0.2 0.0 1 ± 0.00 5 0.6 5 0.0 1 GRAY ZUCCHINI 5.6 ± 0.08 0.0 1 ± 0.00 1 0.25 0.02 CUCUMB ER 5.1 ± 0.1 0.0 08 ± 0.00 2 0.3 2 0.0 06 Figure 3 - 11 Predicted L. monocytogenes transfer from one inoculate (Radish, Onion, Cantaloupe, Apple, Cucumber, Pear, Tomato , Potato, Zucchini, Gray zucchini, and sweet potato) to 15 uninoculated sample. y predicted is the line of prediction; y observed is the observed line for 3 trails; Confidence intervals is the confidence intervals for the line of prediction. 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Radish Sample number Radish y predicted Confidence intervals y observed for 3 trails 79 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LogCFU/Onion Sample number Onion y predicted Confidence intervals y observed for 3 trials 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Cantaloupe Sample number Cantaloupe 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Apple Sample number Apple Figure 3 - 80 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Cucumber Sample number Cucumber y predicted Confidence intervals y observed for 3 trials 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LogCFU/Pear Sample number Pear 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Tomato Sample number Tomato Figure 3 - 81 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Potato Sample number Potato 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log/CFU/Zucchini Sample number Zucchin i y predicted C onfidence intervals y observed for 3 trials Figure 3 - 82 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Gray zucchini Sample number Gray zucchini y predicted Confidence intervals y observed for 3 trials 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Log CFU/Sweet potato Sample number Sweet potato Figure 3 - 83 Table 3 - 3 : Multiple comparison summary for the decay rate parameter (B) t value df P - value adjusted P - value G ray zucchini vs T omatoes 13.4 88 5.3E - 23 1.9E - 21 * T omatoes vs C ucumber 11.3 88 7.5E - 19 2.7E - 17 * R adish vs C ucumber 8.0 88 3.8E - 12 1.3E - 10 * G ray zucchini vs R adish 7.9 88 5.5E - 12 2.0E - 10 * R adish vs Z ucchini 7.1 88 2.3E - 10 8.5E - 09 * T omatoes vs P otatoes 7.0 88 3.4E - 10 1.2E - 08 * R adish vs P otatoes 6.8 88 9.0E - 10 3.2E - 08 * S weet potato vs C ucumber 6.1 88 2.7E - 08 9.8E - 07 * R adish vs P ear 6.0 88 4.2E - 08 1.5E - 06 * S weet potato vs R adish 5.7 88 1.2E - 07 4.6E - 06 * T omatoes vs Z ucchini 5.5 88 2.7E - 07 9.8E - 06 * C antaloupe vs C ucumber 5.2 88 1.1E - 06 4.2E - 05 * R adish vs O nion 5.1 88 1.6E - 06 5.7E - 05 * G ray zucchini vs C a ntaloupe 5.0 88 1.9E - 06 7.0E - 05 * R adish vs A pple 4.9 88 3.5E - 06 0.0001 * R adish vs T omatoes 4.9 88 4.2E - 06 0.0001 * G ray zucchini vs P otatoes 4.4 88 2.3E - 05 0.0008 * P otatoes vs C ucumber 4.2 88 5.4E - 05 0.001 * C antaloupe vs R adish 4.2 88 6.3E - 05 0.002 * 84 C antaloupe vs Z ucchini 3.9 88 0.0001 0.005 * Cucumber vs O nion 3.4 88 0.0007 0.02 * S weet potato vs Z ucchini 3.4 88 0.0009 0.03 * C antaloupe vs P otatoes 3.3 88 0.001 0.04 * T omatoes vs P ear 3.1 88 0.002 > 0.05 S weet potato vs T omatoes 2.7 88 0.006 > 0.05 S weet potato vs P otatoes 2.7 88 0.006 > 0.05 Z ucchini vs O nion 2.5 88 0.01 > 0.05 C antaloupe vs P ear 2.4 88 0.01 > 0.05 T omatoes vs A pple 1.9 88 > 0.05 > 0.05 P ear vs C ucumber 1.8 88 > 0.05 > 0.05 P otatoes vs Z ucchini 1.8 88 > 0.05 > 0 .05 C antaloupe vs A pple 1.8 88 > 0.05 > 0.05 G ray zucchini vs P ear 1.6 88 > 0.05 > 0.05 S weet potato vs C antaloupe 1.6 88 > 0.05 > 0.05 T omatoes vs O nion 1.5 88 > 0.05 > 0.05 P otatoes vs O nion 1.5 88 > 0.05 > 0.05 S weet potato vs Pe ar 1. 4 88 > 0.05 > 0.05 C antaloupe vs O nion 1.2 88 > 0.05 > 0.05 P ear vs Z ucchini 1.2 88 > 0.05 > 0.05 P ear vs O nion 1.1 88 > 0.05 > 0.05 A pple vs C ucumber 1.1 88 > 0.05 > 0.05 G ray zucchini vs A pple 0.9 88 > 0.05 > 0.05 Table 3 - 3 85 S weet potato vs A ppl e 0.9 88 > 0.05 > 0.05 G ray zucchini vs C ucumber 0.8 88 > 0.05 > 0.05 A pple vs Z ucchini 0.8 88 > 0.05 > 0.05 A pple vs O nion 0.8 88 > 0.05 > 0.05 Cucumber vs Z ucchini 0.3 88 > 0.05 > 0.05 C antaloupe vs T omatoes 0.1 88 > 0.05 > 0.05 P ear vs P otatoes 0 88 > 0.05 > 0.05 S weet potato vs O nion 0 88 > 0.05 > 0.05 A pple vs P otato 0 88 > 0.05 > 0.05 A pple vs P ear 0 88 > 0.05 > 0.05 G ray zucchini vs Z ucchini 0 88 > 0.05 > 0.05 G ray zucchini vs O nion 0 88 > 0.05 > 0.05 Table 3 - 3 86 Table 3 - 4 : Physicochemical characteristic s of produce Produce Water content (%) C utting force (N) pH soluble solids content (SSC) o Brix Surface hydropho bicity o Surface roughness ( Ra ) Apples 82.8±1.1 cd 17.3±1.0 b 4.5±0.2 c 15.0±0.3 a 93.4±1.4 b 75±6.4 b Cantaloupe 89.5±1.0 b 3.2±0.0 e 6.5±0.1 a 10.0±0.3 c <5.0 e 167.1±13.7 ab Cucumbers 95.6±1.0 a 17.3±1.0 b 5.8±0.1 ab 4.0 ±0.3 f 93.0±2.6 b 367.6±155.4 a Gray zucchini 95.2±0.1 a 11.7±0.1 cd 6.1±0.05 a 5.8±0.7 e 63.0±11.5 c 236.3±41.2 ab Onions 85.4±0.6 bc 13.1±0.2 bc 5.8±0.2 b 8.0±0.3 d 98.1±2.0 ab 100.2±24.0 b Pears 85.1±0.2 cd 15.2±0.6 bc 4.5± 0.1 c 12.5±0.2 b 112.1±7.4 a 122.1±9.1 ab Potatoes 85.4±0.2 de 1 3.1±1.1 bc 6.2±0.0 ab 5.0±0.0 ef 51.3±1.4 cd 145.1± 36.1 ab Radishes 94.8±0.1 a 16.2±0.9 b 6.1±0.1 ab 4.0±0.0 f 37.1±0.9 d 161.4±30.6 ab Sweet potatoes 80. 3±1.9 e 33.5±3.8 a 5.8±0.1 ab 10.1±0.2 c <5.0 e 112.2±14.1 ab Tomatoes 95.3±0.7 a 7.5±0.8 de 4.7±0.0 c 5.0±0.3 ef 103.0±1.6 ab 108.7±7.3 ab Zucchini 95.4±0.6 a 14.5±0.7 bc 6.2±0.3 ab 4.0±0.3 f 93.1±6.9 b 153.6±14.2 ab Car rots 88.1±0.5 28.8±0.3 6.3±0.2 9 ± 0.5 103 ± 9.5 113.7±2.9 87 Table 3 - 5 : Regression analysis of variance Source DF Sum of Squares Mean Square F Ratio Model 13 0.01686732 0.001297 4.4158 Error 19 0.00558274 0.000294 Prob > F C. Total 32 0.02245006 0.0018* Table 3 - 6 : Effect tests of the regression analysis Source Nparm DF Sum of Squares F Ratio Prob > F P roduct 10 10 0.01474480 5.0182 0.0013* Cutting force 1 1 0.00037030 1.2603 0.2756 pH 1 1 0.00065365 2.2246 0.1522 Surface hydrophobicity 1 1 0.00052127 1.7741 0.1986 88 Figure 3 - 12 : I ndividual component of the multip le regression model 89 3.4 DISCUSSION: Th e findings of this research clearly show that cross - contamination from inoculated to uninoculated produce occurs at different rates during sequential slicing . After slicin g one inoculated followed by 15 uninoculated samples , detectable levels of L. monocytogenes remained on both the pusher plates and the blades, allowing for further transfer in all product s . However, L. monocytogenes populations remaining on the slicer vari ed significantly across different products. I n t he present study, percent sequential transfer was the highest for cantaloupe compared to other produc ts sliced. These finding are consistent with those of Miranda and Schaffner (2016) wh o showed that more bacteria transferred to watermelon (~ 0.2 to 97%) than to any other food examined regardless of the contact time . Given the significantly higher water content of watermelon (0.99 ± 0.01) compared to the other products tested, these finding s support the impact of water content on bacterial transfer. The recovery percentage (mass balance) of Listeria after slicing 15 uncontaminated products varied greatly. Mass balance calculation or p ercentage recovery is a highly variable process due to the nature of bacterial transfer and it has been observed by other research groups. For instance, Buchholz (2012) reported recovery of E. coli transferred during processing of baby spinach as high as 1 47.2±50.4 % compared to 52.6±43.0 % recovered from romaine lettuce. These variances are attributed to the errors inherent to microbial collection from surfaces and enumeration techniques . B acterial transfer during slicing of foods is a complex process. I n this study, eleven products were evaluated individually for the transfer of Listeria during mechanical slicing. Additionally, t he physicochemical characteristics of these products were also measured. An exponential decay model used to describe the transf er of L isteria during slicing resulted in 90 significantly different decay rates for different products. These observation s are in partial agreement with those of Vorst et al . ( 2006) who evaluated the t ransfer of L . monocytogenes during mechanical slicing of turkey breast, bologna, and s alami . These products , which vary in fat and moisture content , yielded differen t Listeria populations on the slicer blade and other components after repeated slicing. Moreover, t hese observations are consistent with previous cross - contamination studies in which foodborne pathogens w ere shown to readily transfer to or from slicers or mechanical shredders to deli meats (Vorst et al . 2006), lettuce (Buchholz et al. 2012a, 2012b), and celery (Kaminski et al . 2014) during simulated commercial processing ( Herman et al. 2015) . The impact of the physicochemical characteristics of products on decay rates was examined in this research . The multiple linear regression preformed showed that decay rate of products during slicing is highly depended on the produ ct itself with a statistical significate ( P However, the model showed that pH, cutting force , and surface hydrophobicity had the most pronounced effect on the decay rate compared to surface roughness, soluble solid content , and water content . The impact of texture / cutting force on decay ra te have been studied before. Wang and Ryser ( 2016) reported significantly lower transfer decay rates for Salmonella when slicing Rebelski and Bigdena as compared to Torero tomatoes which had significantly ( P tougher texture and lower water content compared to the other two varieties . In this study, the impact of pear texture / cutting force was also investigated. Given their ability to ripen over time, pears were used assess the effect of product fir mness on bacterial transfer. Except for cutting force , all of the remaining (water content, pH, soluble solids cont ent and surface hydrophobicity) remained similar. The slicer used for this study also provided a constant force to minimize 91 variability. Cutt ing force seemed to have no significant effect on transfer as the reduction of bacteria on the slicer and the exponential decay rate were statistically similar ( P > 0.05 ). Although these finding contradict those of Wang and Ryser ( 2016) where cutting force did influence bacterial transfer during slicing of tomatoes, the different tomato varieties that were used varied in water content as well, suggesting that the combination of cutting force and water conte nt can potentially affect bacterial transfer. Few studies have measured and reported the impact physicochemical characteristics of products on bacterial transfer . For instance, Wang et al . (2009) showed a positive linear correlation between average surface roughness ( Ra ) and the adhesion rate of E. coli O157:H7 for Gol den Delicious apples avocadoes (9.58 ± , while surface hydrophobicity for the se same produc ts , respectively . In a meta - an alysis of bacterial transfer data , Mazon ( 2017) found that five studies included surface roughness for es s and with these same studies showing that product roughness affected bacterial transfer. Based on the meta - analysis by Mazon ( 2017 ) that included the impact of pH on bacterial attachment to food products, both the i ntercept and rate parameter decreased with increasing pH . All of the studied mentioned before examined the transfer as a function of one variable (water content, pH, c utting force ...etc.). to our knowledge, this is the first study to analyze bacterial transfer as a function of multiple physicochemical characteristics . Although the model 92 accurately predicted the decay rate for the different product, the predictions were heavily dependent on the type of product analyzed . In summary, based on product type , some fresh produc ts are more prone to cross - contamination than others during slicing . Creating a generalized model to predict decay rates of product based on their in herent characteristic is a challenging task due to the complexity , dynamics , and variables involved in bacterial transfer during slicing . However, t hese findings should lay the foundation for future research and narrow the focus of variables affecting bact eria transfer during slicing to improve our understanding of this phenomena. 93 CHAPTER 4 : 4 Conclusions and Recommendations for Future Work 94 4.1 CONCLUSIONS OF THIS DISSERTATION This dissertation includes th r ee research chapters pertaining to bacterial trans fer during slicing of fresh produce and the effect of physicochemical properties of produce on bacterial transfer . The findings from this research illustrate the overall interactions between physicochemic al characteristics of fresh produce and transfer of Listeria during slicing, and more importantly provide a new approach for modeling Listeria transfer data , which should in turn lead to the development of more effective strategies to minimize cross - contamination. The first research chapter - " C hapter 2: Microbial Cross - Contamination of Cucumber and Zucchini during Slicing as Impacted by Mechanical Slicer Type, Slicing Speed and Water content", demonstrated that slicing direction (vertical vs horizontal) impact ed L isteria transfer. A fter slicing one inocu lated sample followed by fifteen uninoculated samples, Listeria populations on different parts of the stationary slicer decreased significantly ( P 0.05). However, different parts of the rotating slicer were able to retain a greater proportion of the initial Listeria population transferred to the same parts during continued use . Moreover , using floral foam to evaluate the effect of water content on bacterial transfer, a statistically similar decay rate was observed for all moisture content examined . This chapter concluded that both the type of slicer and type of product sliced affected the numbers of Listeria transferred . Chapter 3 focused on t he effect of physicochemical properties of fresh produce and Listeria transfer during mechanical slicing. Using three pear firmness categories - firm (10 - 15 N), medium (6 - 9 N) and soft (< 6 N) to study the impact of firmness on Listeria and Salmonella t ransfer during slicing , similar transfer ( P > 0.05) decay rates were observed for firm, medium, and soft pear s , indicat ing that bacterial transfer is a multifactorial process. W hen a range of fresh produce ( onions, radishes, tomatoes, potatoes, carrots, zu cchini, cantaloupe, apple s , sweet 95 potato es , grey zucchini and cucumber s ) were assessed for Listeria transfer, the different products yielded different transfer decay rates. Further investigation of the physicochemical properties of fresh produce indica ted that decay rates are significant ly ( P 0.05) dependent on product tested . In summary, based on product characteristics, some types of fresh produce are more prone to cross - contamination than others during slicing . These findings should improve our und erstanding of bacterial transfer, help define the order in which different products are sliced and aide in the development of improved predictive models for risk assessment. 4.2 RECOMMENDATIONS FOR FUTURE WORK As shown in this research, bacterial transfer d uring slicing is a very complex process that involves a therefore, more replicat ions are needed to minimize variation in the data collected which will result in better predictions . While the res ults from this study can provide valuable information, in the future, increasing the number of produc ts sliced when collecting experimental data could help improve model predictions. Most bacterial transfer studies have used one product and one microorga nism, which makes it difficult to draw general conclusions on what factors affect bacterial transfer during slicing. It would be extremely beneficial for future research to focus on the effect of extreme differences of physiochemical characteristic on bact erial transfer. This could be achieved by genetically engineer a product to have for instance, low and high water content. One of the main observations from this work is that bacterial transfer during slicing is a multifactorial process . I dentifying and e valuating new factors related to produce , bacteria and the physical process of slicing is vital. For instance, the impact of new produce characteristics such 96 as cell size and cell wall polysaccharides , which contribute to the amount of liquid released from products during slicing, will advance our understanding of the bacterial transfer phenomena. 97 APPENDICES 98 5 APPENDIX A: 6 7 Microbial Cross - Contamination of Cucumber and Zucchini during Slicing as Impacted by Mechanical Slicer Type, Slicing Spee d and Water C ontent 99 Table A - 1 : Mean L. monocytogenes distribution on produce slices from inoculated and uninoculated cucumber and zucchini after slicing with a rotating slicer. Listeria population (log CFU/cm 2 ) Produce slices order Inoculated cucum ber Un - inoculated cucumber Inoculated zucchini Un - inoculated zucchini 1 st 4.7 3.8 5.3 3.0 2 nd 4.4 3.8 4.2 2.7 15 th 4.3 3.7 4.0 2.4 16 th 4.4 3.8 4.1 2.6 29 th 4.2 3.7 4.2 2.6 Last slice 4.7 3.4 5.0 2.4 Table A - 2 : Listeria distribution (mean ± SE) on different components of the rotating slicer before and after slicing 15 uninoculated zucchini and cucumber. Listeria population (log CFU/component) Slicer component Before slicing cu cumber After slicing cucumber Before slicing zucchini After slicing zucchini Slicing plate 4.0 3.3 4.2 3.4 Bottom 3.3 2.2 3.4 4.5 Pusher 3.9 3.0 4.5 3.3 Table A - 3 : Listeria distribution (mean ± SE) on different components of the stationary slicer before and after slicing 15 uninoculated zucchini and cucumber. Listeria population (log CFU/component) Slicer component Before slicing cucumber After slicing cucumbe r Before slicing zucchini After slicing zucchini B lade 4.0 3.1 4.4 3.1 Pusher 5.0 4.0 5.1 4.4 100 Table A - 4 : Listeria populations (mean ± SE) on different locations of a zucchini and cucumber slice. Listeria population (log CFU/cm 2 ) Slicer type Skin cucumber Flesh cucumber Skin zucchini Flesh zucchini Rotating 5.55 2.56 6.17 3.25 Stationary 5.53 2.27 6.19 2.52 Table A - 5 : Listeria transfer from an inoculated stationary slicer (~ 7 log CFU/ product) to 15 inoculated zucchini and cucumb er . Listeria population (log CFU/ product ) Cucumber Zucchini Uninoculated product Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 1 5.9 5.1 5.6 4.3 5.4 6.4 2 4.6 5.6 4.5 4.9 5.6 4.8 3 4.6 4.9 4.6 4.5 4.0 5.0 4 4.9 4.6 5.0 3.3 4.3 5.0 5 4.9 4.8 4.6 4.6 4.6 5.2 6 4.7 4.8 4.8 4.8 3.8 5.5 7 4.5 4.9 5.1 3.9 4.2 5.0 8 4.2 4.6 5.2 4.3 3.8 4.6 9 4.9 5.0 4.8 4.3 3.6 4.5 10 4.7 4.5 4.9 4.5 4.5 5.1 11 4.6 4.7 5. 0 3.3 2.9 4.4 12 3.9 4.4 4.9 3.5 4.0 4.3 13 4.4 4.3 5.1 3.3 3.6 5.1 14 4.0 4.3 4.7 4.2 3.2 5.0 15 4.1 4.5 4.7 4.3 3.8 4.9 Table A - 6 : Listeria transfer from an inoculated rotating slicer (~ 7 log CFU/ product) to 15 inoculated zucchini and cucumber . Listeria population (log CFU/ product ) Cucumber Zucchini Uninoculated product Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 101 1 6.0 6.1 5.4 5.6 5.7 5.7 2 6.0 5.2 4.6 5.4 5.6 6.0 3 4.7 4.8 5.0 4.8 5.4 5.7 4 3.7 4.7 4.7 6.1 5.7 5.4 5 3.6 4.4 4.5 5.3 5.0 5.7 6 3.6 3.9 4.1 5.2 4.5 5.6 7 3.3 4.6 4.0 6.0 5.1 5.1 8 3.4 4.1 3.6 5.7 5.7 4.6 9 3.6 3.4 4.9 6.1 5.8 4.7 10 3.3 3.9 4.6 4.9 5.4 4.7 11 3.5 4.6 4.4 4.8 5.4 5.0 12 3.3 4. 5 5.0 4.8 4.8 5.1 13 4.0 4.4 3.5 4.6 4.8 5.0 14 2.8 4.4 4.7 3.9 5.7 4.8 15 2.8 3.5 3.1 5.0 4.9 5.0 Table A - 7 : L. monocytogenes transfer from inoculated to uninoculated zucchini during slicing at high speed and low speed. Listeria population (log CFU/ zucchini ) High speed Low speed Uninoculat ed zucchini Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 1 4.3 5.7 6.4 5.4 5.1 5.8 2 4.9 5.7 4.8 5.1 5.7 5.8 3 4.5 5.6 5.0 4.8 4.8 6.3 4 3.3 5.7 5.0 4.6 5.6 6.1 5 4.6 5.3 5.2 4.3 4.3 6.3 6 4.8 5.4 5.5 4.8 5.0 5.8 7 3.9 5.5 5.0 3.9 5.6 5.1 8 4.3 5.4 4.6 5.2 4.7 6.0 9 4.3 5.5 4.5 4.4 5.0 5.6 10 4.5 5.1 5.1 5.6 5.1 5.6 11 3.3 4.9 4.4 4.3 5.6 5.4 12 3.5 5.3 4.3 4.9 4.7 4.7 13 3.3 5.2 5.1 4.4 5.1 4.6 14 4.2 5.1 5.0 4.4 5.1 4.3 15 4.3 5.3 4.9 3.9 5.0 4.4 Table A - 6 102 Table A - 8 : L. monocytogenes transfer from inoculated to uninoculated cucumber during slicing at high speed and low speed . Listeria population (log CFU/ cucumber ) High speed Low speed Uninoculated cucumber Rep 1 Rep 2 Rep 3 Re p 1 Rep 2 Rep 3 1 6.0 5.2 5.7 5.5 4.9 5.5 2 4.7 5.6 4.6 5.8 5.2 6.1 3 4.7 5.0 4.7 4.8 5.2 5.8 4 5.0 4.6 5.1 4.4 4.7 5.2 5 5.0 4.8 4.6 4.8 4.9 5.4 6 4.8 4.9 4.9 5.4 5.1 6.3 7 4.6 5.0 5.2 5.0 5. 0 6.0 8 4.3 4.7 5.3 5.0 4.9 5.6 9 5.0 5.1 4.9 5.1 5.2 5.4 10 4.8 4.6 5 5.2 4.2 4.2 11 4.7 4.8 5.1 5.1 4.9 4.7 12 4 4.5 5.0 4.8 5.0 4.8 13 4.5 4.4 5.2 5.0 5.0 4.2 14 4.1 4.4 4.8 4.2 5.2 4.5 15 4.2 4.6 4.7 4.7 5.0 4.7 Table A - 9 : Listeria distribution (mean ± SE) on different components of a stationary slicer before and after slicing 15 uninoculated pieces of floral foam at water saturation levels of 97.6 , 96.7, and 95.1 % . Listeria population (log CFU/component) Slicer component Before slicing moisture levels of 97.6% After slicing moisture levels of 97.6% Before slicing moisture levels of 96.7 % After slicing moisture levels of 96.7 % Before slicing saturatio n levels of 95.1 % After slicing moisture levels of 95.1 % Blade 4.0 3.23 4.0 3.13 4.0 3.4 Pusher 5.08 3.45 5.0 3.7 5.0 4.1 Table A - 10 : Sequential transfer during slicing of floral foam at water saturation le vels of 9 7.6 , 96.7, and 95.1 % Listeria population (log CFU/ floral foam ) Percent moisture 97.6% Percent moisture 96.7% Percent moisture 95.1% Uninoculated floral foam Rep 1 Rep 2 R ep 3 Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 1 4.9 5.0 2.7 5.6