Evaluation and Modeling Thermal Inactivation Kinetics of Clostridium difficile in Ground Beef as Influenced by Fat Content and Strain.

Transcription

1 Evaluation and Modeling Thermal Inactivation Kinetics of Clostridium difficile in Ground Beef as Influenced by Fat Content and Strain By Jay Arora Department of Food Science and Agricultural Chemistry Macdonald Campus, McGill University Montreal, Canada September 2014 A thesis submitted to McGill University in partial fulfillments of the requirements for the degree of Master of Science Jay Arora, 2014

2 Suggested short title: THERMAL INACTIVATION OF CLOSTRIDIUM DIFFICILE IN GROUND BEEF

3 ABSTRACT Food has been considered as a possible source of hyper-virulent and epidemic strains of C. difficile. This thesis research was aimed at evaluation of thermal destruction kinetics of C. difficile spores in ground beef as influenced by fat content (lean, 15% and ground 30%) and strain type (ATCC and ATCC 43597). Thermal inactivation studies were carried out in the temperature range C. The kinetic parameters, D and z values, were determined using the first order log-linear model. D values ranged from 3.4 min at 82 C to 2.5 h at 74 C. The associated z values ranged from 5.4 to 5.6 C. Fat content of ground beef was found to reduce the D values of spores and D values of the strain ATCC were higher than that of strain ATCC (p<0.05). Thermal destruction behavior of C. difficile spores was also evaluated using alternative models proposed in literature (Weibull model, Weibullian loglogistic model, log-quadratic model and the three parameter log-logistic model). A statistical framework integrating residual analysis was used to test the goodness-of-fit in addition to information criterion to test the predictive ability. The predictive ability of the conventional first order model was found to be better than the Weibull and log-quadratic models. However, both first order and Weibull models exhibited a bias in fitting the data. The three parameter log-logistic model was a good fit and demonstrated the best predictive ability. The threeparameter log-logistic model was a better choice when a curvilinear behavior of thermal destruction was evident. The impact of performance and selection of model on food safety was discussed. Evaluated kinetic data can be used to design cooking measures for inactivating the food borne hyper-virulent and epidemic strains of C. difficile.

4 RÉSUMÉ La nourriture a été identifiée comme une source possible de souche hyper-virulente et épidémique de C. difficile. Cette recherche avait pour but d évaluer la cinétique de destruction thermique de spores de C. difficile dans le bœuf haché selon la teneur en matières grasses (maigre, 15% et 30%) et la souche (ATCC et ATCC 43597). Des études d inactivation thermique furent réalisées à une température variante de 74 à 82 C. Les paramètres cinétiques, les valeurs D et z ont été déterminés en utilisant le modèle log-linéaire de premier ordre. Les valeurs D ont varié de 3,4 min (82 C) à 2,5 h (74 C); les valeurs z associées ont varié de 5,4 à 5,6 C. La teneur en matières grasses du bœuf haché a été identifiée comme source de réduction des valeurs D des spores : les valeurs D de la souche ATCC ont par ailleurs été plus élevées que celles de la souche ATCC (p < 0,05). Le comportement de destruction thermique de spores de C. difficile a aussi été évalué en utilisant des modèles alternatifs proposés dans la littérature (modèle Weibull, modèle loglogistique Wellbullian, modèle log-quadratique et modèle log-logistique à trois paramètres). Un cadre statistique intégrant une analyse résiduelle a été utilisé pour tester la validité de l ajustement en plus d un critère d information pouvant tester la capacité prédictive. La capacité prédictive du premier modèle conventionnel a révélé être meilleur que les modèles Weibull et log-quadratique. Cependant, autant les modèles de premier ordre et de Weibull ont laissé paraitre des biais dans l ajustement des données. Le modèle log-logistique à trois paramètres a démontré un bon ajustement et est apparu comme étant le modèle ayant la meilleure capacité prédictive. Le modèle log-logistique à trois paramètres s est révélé être un meilleur choix lorsqu un comportement de destruction thermique curvilinéaire était évident. L impact de performance et de sélection d un modèle sur la sécurité alimentaire a été discuté. Les données cinétiques évaluées peuvent être utilisées pour élaborer des recommandations de cuisson pour inactiver les souches hyper-virulentes et épidémiques de C. difficile d origine alimentaire dans la nourriture. II

5 CONTRIBUTIONS OF AUTHORS Several presentations were made based on the research presented in this thesis and are being prepared for publication. The authors involved in the thesis and their contributions to the various articles are as follows: Jay Arora is the M.Sc. candidate who designed and conducted all the experiments in consultation with the supervisors. He performed data collection and analysis. Additionally, he prepared drafts of all the manuscripts for scientific publications. Dr. H. S. Ramaswamy is the thesis supervisor, under whose guidance the research was conducted. He assisted the candidate in designing and conducting the experiments as well as correcting, proofreading, reviewing and processing manuscripts for the publications. Dr. John W. Austin supervised the research conducted at the Clostridium Research Laboratory at the Bureau of Microbial Hazards. He guided the candidate in designing and conducting the experiments in addition to correcting, proofreading, reviewing and processing manuscripts for the publications. Ms. Denise Oudit and Mr. Jeff Bussey also helped the candidate design, plan and conduct the experiments. III

6 LIST OF PUBLICATIONS AND PRESENTATIONS Part of this thesis has been prepared as manuscripts for publications in refereed scientific journals: Jay Arora, Hosahalli Ramaswamy, Jeff Bussey, Denise Oudit and John Austin. Effect of fat on the heat resistance of Clostridium difficile in ground beef (in preparation). Jay Arora and Hosahalli Ramaswamy. Alternate models for understanding the thermal destruction behavior of spores of Clostridium difficile (in preparation). Part of this thesis has been presented in scientific conferences: Jay Arora, Hosahalli Ramaswamy, Jeff Bussey, Denise Oudit and John Austin. Thermal Inactivation Kinetics of Spores of Clostridium difficile in Ground Beef. Northeast Agricultural and Biological Engineering Conference 2014, Kemptville, Ontario, Canada. IV

7 ACKNOWLEDGEMENTS I would like to thank my supervisor Dr. Hosahalli Ramaswamy and co-supervisor Dr. John Austin for their invaluable guidance and support. The execution of the entire thesis from planning to experimentation and data analysis is attributable to their experience, expertise, capability and immense patience. They provided me access to world-class research facilities and honed my skills as a capable researcher and colleague in the scientific community. In addition I would like to thank them for financial support. I extend my thanks to colleagues from my research groups at McGill University, Health Canada and University of Saskatchewan. All my colleagues at these two research groups have been extremely supportive and I would like to thank them for the fruitful two years spent as a masters student. I am grateful to Ms. Denise Oudit and Mr. Jeff Bussey. All of them contributed to my professional development and I share the credit for this thesis with them. Finally my friends and family have supported and encouraged me throughout my masters to accomplish my goals and I would like to thank them all. V

8 TABLE OF CONTENTS ABSTRACT... I RÉSUMÉ... II CONTRIBUTIONS OF AUTHORS... III LIST OF PUBLICATIONS AND PRESENTATIONS... IV ACKNOWLEDGEMENTS... V TABLE OF CONTENTS... VI LIST OF TABLES... X LIST OF FIGURES... XIII CHAPTER 1 INTRODUCTION... 1 CHAPTER 2 LITERATURE REVIEW CLOSTRIDIUM DIFFICILE INTRODUCTION DETERMINANTS OF VIRULENCE CLOSTRIDIUM DIFFICILE INFECTIONS (CDI) GENERAL OVERVIEW EPIDEMIOLOGY OF C. DIFFICILE INFECTIONS (CDI) RISK FACTORS HOSPITAL-ACQUIRED C. DIFFICILE INFECTIONS (HA- CDI) COMMUNITY-ASSOCIATED C. DIFFICILE INFECTIONS (CA-CDIS) PRESENCE OF C. DIFFICILE IN FOODS PRESENCE IN MEAT PRODUCTS SECTION SUMMARY PREVENTION STRATEGIES CAUSES OF CONTAMINATION KNOWN FOODBORNE PATHOGENS AND PREVENTION STRATEGIES THERMAL INACTIVATION OF C. DIFFICILE VI

9 2.6 DETERMINATION OF THERMAL INACTIVATION KINETICS ALTERNATIVE APPROACHES TO MODELING THERMAL INACTIVATION KINETICS DATA WEIBULL MODEL WEIBULL LOG-LOGISTIC MODEL LOG-QUADRATIC MODEL THREE PARAMETER LOG-LOGISTIC MODEL PREFACE TO THE CHAPTER CHAPTER 3 EFFECT OF FAT AND INTER-STRAIN VARIATION OF HEAT RESISTANCE OF SPORES OF CLOSTRIDIUM DIFFICILE IN GROUND BEEF ABSTRACT INTRODUCTION METHODS AND MATERIALS C. DIFFICILE SPORE CULTURE PREPARATION SELECTION OF STRAINS FOR THERMAL TREATMENT PREPARATION OF STERILE BEEF MATRIX STERILITY VERIFICATION OF IRRADIATED SAMPLES GROUND BEEF INOCULATION THERMAL TREATMENT ENUMERATION OF SURVIVING SPORES DATA ANALYSIS RESULTS AND DISCUSSION STRAIN SELECTION THERMAL INACTIVATION KINETICS OF SPORES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC EFFECT OF FAT ON THE THERMAL INACTIVATION KINETICS OF SPORES OF C. DIFFICILE INTER-STRAIN COMPARISON OF THERMAL INACTIVATION KINETICS OF SPORES OF C. DIFFICILE CONCLUSION PREFACE TO CHAPTER VII

10 CHAPTER 4 ALTERNATE MODELS FOR UNDERSTANDING THE THERMAL DESTRUCTION BEHAVIOUR OF SPORES OF C. DIFFICILE ABSTRACT INTRODUCTION METHODS AND MATERIALS WEIBULL DISTRIBUTION MODEL WEIBULLIAN LOG-LOGISTIC MODEL LOG-QUADRATIC MODEL THREE PARAMETER LOG-LOGISTIC MODEL EVALUATING MODEL PERFORMANCE AND PERFORMING MODEL COMPARISONS RESULTS AND DISCUSSION WEIBULL MODEL WEIBULLIAN LOG-LOGISTIC MODEL LOG QUADRATIC MODEL THREE PARAMETER LOG-LOGISTIC MODEL CONCLUSIONS CHAPTER 5 GENERAL CONCLUSIONS REFERENCES APPENDIX A RESIDUAL PLOT AND SCALE-LOCATION PLOT FOR FIRST ORDER MODEL APPENDIX B HAZARD PLOT FOR WEIBULL MODEL APPENDIX C RESIDUAL PLOT AND SCALE-LOCATION PLOT FOR WEIBULL MODEL 129 APPENDIX D RESIDUAL PLOT AND SCALE-LOCATION PLOT FOR LOG QUADRATIC MODEL APPENDIX E LOG PLOTS FOR THREE-PARAMETER LOG-LOGSITIC MODEL 131 APPENDIX F RESIDUAL PLOT AND SCALE-LOCATION PLOT FOR THREE PARAMETER LOG-LOGISTIC MODEL VIII

11 APPENDIX G INFORMATION MEASURE STATISTICS FOR THE FIRST ORDER AND ALTERNATIVE MODELS APPENDIX H RESIDUAL PLOT AND SCALE-LOCATION PLOT FOR MODIFIED LOG-LOGISTIC MODEL IX

12 LIST OF TABLES TABLE 2. 2 RECOMMENDED 'SAFE INTERNAL COOKING TEMPERATURES' FOR MEATS TABLE 3.1 THE D VALUES (X ± S), R 2 AND THE 95% CONFIDENCE INTERVAL FOR THE D VALUES FOR SPORES OF C. DIFFICILE ATCC THERMALLY TREATED IN LEAN AND REGULAR GROUND BEEF TABLE 3.2 THE D VALUES (X ± S), R 2 AND THE 95% CONFIDENCE INTERVAL FOR THE D VALUES FOR SPORES OF C. DIFFICILE ATCC THERMALLY TREATED IN LEAN AND REGULAR GROUND BEEF TABLE 3.3 THE Z VALUES ( C) AND 95% CONFIDENCE INTERVAL OF Z ( C) FOR THERMAL INACTIVATION OF SPORES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF TABLE 3.4 P VALUES FOR THE STUDENT'S T TEST TO DETERMINE STATISTICAL SIGNIFICANCE OF THE DIFFERENCE TABLE 3.5 THE DIFFERENCE PARAMETER (Δ) AND THE PERCENTAGE RELATIVE DIFFERENCE PARAMETER (Φ) FOR THE EFFECT OF FAT ON SPORES OF C. DIFFICILE ATCC TABLE 3.6 THE DIFFERENCE PARAMETER (Δ) AND THE PERCENTAGE RELATIVE DIFFERENCE PARAMETER (Φ) FOR THE EFFECT OF FAT ON SPORES OF C. DIFFICILE ATCC TABLE 3.7 P VALUES FOR THE STUDENT'S T TEST TO DETERMINE STATISTICAL SIGNIFICANCE OF THE DIFFERENCE BETWEEN Z VALUES FOR LEAN AND REGULAR GROUND BEEF AS THE HEATING MEDIUM TABLE 3.8 WATER ACTIVITY MEASUREMENTS FOR LEAN AND REGULAR GROUND BEEF TABLE 3.9 AMOUNT OF FREE FATTY ACID PER 100 GRAMS OF REGULAR GROUND BEEF AND MMOL OF FREE FATTY ACIDS PER GRAM OF REGULAR GROUND BEEF TABLE 3.10 AMOUNT OF FREE FATTY ACID PER 100 GRAMS OF LEAN GROUND BEEF AND MMOL OF FREE FATTY ACIDS PER GRAM OF LEAN GROUND BEEF TABLE 3.11 FATTY ACID DISTRIBUTION IN REGULAR GROUND BEEF WITH PERCENTAGE OF FATTY ACID CONTENT FOR EACH CATEGORY AND PERCENTAGE OF TOTAL FATTY ACID CONTENT TABLE 3.12 FATTY ACID DISTRIBUTION IN LEAN GROUND BEEF WITH PERCENTAGE OF FATTY ACID CONTENT FOR EACH CATEGORY AND PERCENTAGE OF TOTAL FATTY ACID CONTENT 56 TABLE 3.13 P VALUES FOR THE STUDENT'S T TEST TO DETERMINE STATISTICAL SIGNIFICANCE OF THE DIFFERENCE X

13 TABLE 3.14 THE DIFFERENCE PARAMETER (Δ) AND THE PERCENTAGE RELATIVE DIFFERENCE PARAMETER (Φ) FOR THE DIFFERENCE IN D VALUES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC IN LEAN GROUND BEEF TABLE 3.15 THE DIFFERENCE PARAMETER (Δ) AND THE PERCENTAGE RELATIVE DIFFERENCE PARAMETER (Φ) FOR DIFFERENCE IN D VALUES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC IN REGULAR GROUND BEEF TABLE 3.16 P VALUES FOR THE STUDENT'S T TEST TO DETERMINE STATISTICAL SIGNIFICANCE OF THE DIFFERENCE BETWEEN Z VALUES FOR C. DIFFICILE ATCC AND C. DIFFICILE ATCC TABLE 4.1 WEIBULL MODEL PARAMETERS Α AND Β, R 2 AND THE RELIABLE LIFE (T R ) OF SPORES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF TABLE 4.2 R 2 VALUES FOR LINEAR, EXPONENTIAL, SECOND ORDER AND THIRD ORDER QUADRATIC MODELS OF THE TEMPERATURE DEPENDENCE OF THE SHAPE FACTOR 'BETA' OF WEIBULL MODELS TABLE 4.3 R 2 VALUES AND THE PARAMETERS FOR THE LOG LINEAR MODELING OF THE TEMPERATURE DEPENDENCE OF THE SCALE PARAMETER (Α) AND THE RELIABLE LIFE (T R )81 TABLE 4.4 RELIABLE LIVES (T R ), MEAN D VALUES (D ), DIFFERENCE PARAMETER (Δ) AND PERCENTAGE RELATIVE DIFFERENCE PARAMETER (Φ) TABLE 4.4 HEAT RESISTANCE DISTRIBUTION PARAMETERS OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC TABLE 4.5 PEARSON CORRELATION COEFFICIENTS OF MODE OF INACTIVATION TIMES AND TEMPERATURE (R TCM-T ), THE MEAN OF INACTIVATION TIMES AND TEMPERATURE (R TC-T ) AND THE COEFFICIENT OF SKEWNESS AND TEMPERATURE (R Ν-T ) TABLE 4.6 PARAMETERS FOR THE WEIBULLIAN LINEAR LOG LOGISTIC MODEL OF INACTIVATION OF SPORES OF C. DIFFICILE ATCC TABLE 4.7 PARAMETERS FOR THE WEIBULLIAN LINEAR LOG LOGISTIC MODEL OF INACTIVATION OF SPORES OF C. DIFFICILE ATCC TABLE 4.8 PARAMETERS FOR THE LOG QUADRATIC MODEL OF INACTIVATION OF SPORES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC TABLE 4.9 PARAMETERS FOR THREE PARAMETER LOG LOGISTIC MODELS FOR SPORES OF C. DIFFICILE ATCC AND C. DIFFICILE ATCC TABLE 4.10 PARAMETERS FOR THE LINEAR MODELS OF THE TEMPERATURE DEPENDENCE OF PARAMETER XMID XI

14 TABLE G.1 AKAIKE INFORMATION CRITERION (AIC), BAYESIAN INFORMATION CRITERION (BIC) AND MALLOW S C P FOR THE FIRST ORDER, WEIBULL, LOG-QUADRATIC AND LOG- LOGISTIC MODEL, WHICH WERE USED TO MODEL THERMAL DESTRUCTION OF C. DIFFICILE IN GROUND BEEF XII

15 LIST OF FIGURES FIGURE 2.1 A SUMMARY OF THE SOURCES OF EXPOSURE, RISK FACTORS AND MECHANISM OF ACQUISITION OF C. DIFFICILE INFECTIONS (CDI) FIGURE 2.2: EFFECT OF HEAT ON INHIBITION OF AGED SPORES OF C. DIFFICILE IN THREE FOOD MATRICES NAMELY 30% FAT GROUND BEEF, 0% FAT BEEF GRAVY AND 3% FAT LEAN GROUND BEEF. (SOURCE: RODRIGUEZ-PALACIOS AND LEJEUNE, 2011) FIGURE 2.3 A TYPICAL SURVIVOR CURVE FIGURE 2.4 A TYPICAL THERMAL RESISTANCE CURVE FIGURE 3.1 (A) HEAT SEALED NASCO WHIRL-PAK BAG WITH 1 GRAM OF INOCULATED GROUND BEEF (B) ASSEMBLY OF INOCULATED BEEF SAMPLES AND THERMOCOUPLES PRIOR TO PERFORMING THE THERMAL STUDY FIGURE 3.2 UN-INOCULATED GROUND BEEF WITH A COPPER CONSTANTAN THERMOCOUPLE AT THE CENTER TO ESTIMATE THE COME-UP TIME AND WATER BATH TEMPERATURE FIGURE 3.3 SURVIVOR CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III,IV), USING THE FIRST ORDER KINETICS MODEL FIGURE 3.4 THERMAL RESISTANCE CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III,IV), USING THE FIRST ORDER KINETICS MODEL FIGURE 3.5 GRAPHICAL COMPARISONS OF Z VALUES FOR C. DIFFICILE ATCC IN LEAN (1) AND REGULAR GROUND BEEF (2) AND Z VALUES FOR C. DIFFICILE ATCC IN LEAN (3) AND REGULAR GROUND BEEF (4) FIGURE 4.1 SURVIVOR CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III,IV), USING THE WEIBULL MODEL FIGURE 4.2 PLOTS OF THE SHAPE PARAMETER Β VERSUS TEMPERATURE ( C), FOR THE WEIBULL MODEL OF THERMAL DESTRUCTION OF C. DIFFICILE ATCC SPORES IN LEAN AND REGULAR GROUND BEEF (I, II) AND C. DIFFICILE ATCC SPORES IN LEAN AND REGULAR GROUND BEEF (III, IV) FIGURE 4.3 PLOTS OF THE NATURAL LOGARITHM OF THE SCALE PARAMETER Α VERSUS TEMPERATURE ( C), FOR THE WEIBULL MODEL OF THE INACTIVATION CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III, IV) XIII

16 FIGURE 4.4 PLOTS OF THE NATURAL LOGARITHM OF THE RELIABLE LIFE (T R ) VERSUS TEMPERATURE ( C) FOR WEIBULL MODELS OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III, IV) FIGURE 4.5 PLOTS OF THE RELIABLE LIFE (T R ) VERSUS D VALUES FROM THE FIRST ORDER MODEL FOR SPORES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III, IV) FIGURE 4.6 SURVIVOR CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III,IV), USING THE LOG QUADRATIC MODEL FIGURE 4.7 SURVIVOR CURVES OF C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I, II) C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (III,IV), USING THE THREE PARAMETER LOG LOGISTIC MODEL FIGURE 4.9 PLOTS FOR THE TEMPERATURE DEPENDENCE OF PARAMETER XMID FOR C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF (I,II) AND C. DIFFICILE ATCC IN LEAN AND REGULAR GROUND BEEF FIGURE A.1 RESIDUAL PLOT FOR THE FIRST ORDER MODEL FIGURE A.2 SCALE-LOCATION PLOT FOR THE FIRST ORDER MODEL FIGURE B.1 HAZARD PLOT FOR THE THERMAL INACTIVATION OF SPORES OF C. DIFFICILE ATCC WITH LEAN GROUND BEEF AS THE HEATING MEDIUM FIGURE C.1 RESIDUAL PLOT FOR THE WEIBULL MODEL FIGURE C.2 SCALE-LOCATION PLOT FOR THE WEIBULL MODEL FIGURE D.1 RESIDUAL PLOT FOR THE LOG QUADRATIC MODEL FIGURE D.2 SCALE-LOCATION PLOT FOR THE LOG QUADRATIC MODEL FIGURE E.1 A PLOT OF SURVIVOR POPULATION VERSUS THE HEATING TIME ON A LOG 10 SCALE FOR THE THERMAL DESTRUCTION OF SPORES OF C. DIFFICILE ATCC WITH LEAN GROUND BEEF AS THE HEATING MEDIUM FIGURE F.1 RESIDUAL PLOT FOR THE LOG-LOGISTIC MODEL FIGURE F.2 SCALE LOCATION PLOT FOR LOG-LOGISTIC MODEL FIGURE H.1 RESIDUAL PLOT FOR THE LOG-LOGISTIC MODEL FIGURE H.2 SCALE LOCATION PLOT FOR LOG-LOGISTIC MODEL XIV

17 CHAPTER 1 INTRODUCTION Clostridium difficile is a gram-positive spore-forming anaerobe, which is highly prevalent in soil, water and the environment (Saif and Brazier, 1996). It is a major cause of antibiotic associated diarrhea and pseudomembranous colitis in humans. Owing to the severe outbreaks of C. difficile infections (CDIs) in healthcare facilities, this bacterium transformed to an infamous superbug from being an obscure bacterium (Brazier, 2012). The rate of C. difficile infections has increased over the past decade and nearly one third of these are community associated (Lessa, 2013, McFee and Abdelsayed, 2009). The changing epidemiology and escalation in C. difficile Infections (CDI) cases has been attributed to a hyper-virulent strain of C. difficile (Denève et al., 2009). Hyper-virulent strains have been found in foods such as vegetables (Metcalf et al., 2010b), retail meats (Curry et al., 2012, Harvey et al., 2011a, Harvey et al., 2011b, Rodriguez-Palacios et al., 2009, Rodriguez et al., 2014, Weese, 2009) and fish and seafood (Metcalf et al., 2011). The widespread presence of hyper-virulent strain ribotype 027 may be assignable to its high sporulation ability (Akerlund et al., 2008, Weese, 2009). The epidemic strains of ribotype 027 exhibit higher sporulation than non-epidemic strains; the spores of epidemic strains are more resistant to disinfectants (Fawley et al., 2007, Wilcox and Fawley, 2000). Consequently, prevalence of hyper-virulent strains of C. difficile in food has been assignable to one or more of the following (Gould and Limbago, 2010, Weese, 2009): high sporulation of hyper-virulent strains, poor hygiene, contaminated processing equipment and environmental sources. Moreover, contamination of meat with spores of C. difficile may also result from carcass contamination of pigs and cattle (Rodriguez et al., 2013, Rodriguez et al., 2014). Strains of C. difficile have been isolated from food products sampled from diverse geographical locations with detection rates ranging between 0 and 45% in retail meats. However, recent studies have reported low levels of contamination of meat products (Marsh, 2013). Importantly, the results of these studies cannot be compared directly due to the differences in experimental setups and design, food commodities studied and cross contamination in laboratory environments (Marsh, 2013). Although strains isolated from food were same as those isolated from food animals suggesting clonal dissemination of C. difficile (Rodriguez et al., 2013, Rodriguez et al., 2014), the clonal dissemination is contradicted by reports of polyclonality of C. difficile isolates (Curry et al., 2012, Marsh, 2013). Research suggests that food may be a possible source of hyper-virulent strains of C. difficile. However, owing to the lack of direct evidence in the research on the foodborne 1

18 potential of CDI, further research is essential. Nevertheless the information regarding preventing human exposure to these strains is much needed. The knowledge regarding behavior of microorganisms and determining their presence in food is prerequisite to devising strategies to prevent human exposure (Hoorfar et al., 2011). This author discussed the incorporation of interventions at several stages throughout the food chain to limit or avert the transmission of pathogens of concern. Notably, thermal destruction of pathogens is the lattermost strategy that can be employed to prevent human exposure. Adequate cooking of food in compliance with the recommended safe internal cooking temperature can render food safe by destroying foodborne pathogens. Although the recommended safe internal cooking temperatures offer adequate lethality to destroy pathogens such as E.coli O157:H7, Salmonella spp. and Listeria monocytogenes, heating to the recommended cooking temperature of 71 C for 2 h caused only a single logarithmic reduction in the counts of C. difficile spores (Rodriguez-Palacios et al., 2010). Further, another study compared the thermal destruction of a cocktail of 4 PCR ribotypes of C. difficile in lean 3 % fat ground beef, regular 30 % fat ground beef and 0% fat beef gravy (Rodriguez-Palacios and Lejeune, 2011). A 6 log 10 reduction was reported following 2 min of heating at 96 C and 5 to 6 log 10 reduction following 15 min heating at 85 C. The D values found were highly variable, decreasing the reliability of the data. The reduced thermal resistances of spores in the presence of free fatty acids in heating medium has been reported (Lekogo et al., 2013, Lekogo et al., 2010, Tremoulet et al., 2002). This suggests that the fat content of the heating medium affects the thermal resistance of spores. Despite the fact that the thermal destruction of spores of C. difficile was compared in lean and regular ground beef (Rodriguez-Palacios and Lejeune, 2011), a clear effect of fat on these spores was not discernable. Thus, the effect of fat content on the thermal destruction of spores of C. difficile needs to be determined. Also, the variation of the thermal resistances among the strains needs to be determined. Two parameters, namely: the decimal reduction time D (min) and the thermal sensitivity indicator z ( C) need to be determined to characterize the thermal destruction kinetics of spores of C. difficile based on the first order log-linear model. Characterizing the thermal destruction behavior with the first order model may not be enough for understanding the thermal destruction behavior of C. difficile spores (Anderson et al., 1996, Corradini and Peleg, 2007a, Corradini and Peleg, 2007b, Peleg and Cole, 1998). Analysis of 120 survivor curves of microorganisms by van Boekel (2002) indicated less than 5% were log-linear. Thus, log-linearity has been proposed to be an exception rather than a 2

19 rule (Corradini and Peleg, 2007b, Peleg and Cole, 1998). The vitalistic models such as the Weibull model have been found to be better in characterizing the thermal destruction behavior in comparison to the first order model (Panagou et al., 2007). On the contrary, the performance of the Weibull model has been reported to be no better than the first order model (Hassan and Ramaswamy, 2011). Thus, thermal destruction behavior of C. difficile needs characterization using alternative models in addition to the log-linear model. This will help evaluate the performance of several modeling approaches for characterizing the thermal destruction behavior of C. difficile spores as an exemplar, in addition to enhancing the understanding of effects of model performance and selection on food safety. The Weibull model is a vitalistic model, which has been extensively used for analysis non-linear survival curves (Peleg and Cole, 1998, van Boekel, 2002). Also, the log-quadratic model performs adequately in characterizing non-linear thermal destruction curves (Stone et al., 2009 ). Although the vitalistic and empirical approaches have been employed widely, the hybrid modeling approach has not been extensively used. An example of the hybrid modeling approach is the three-parameter log-logistic model, which is based on the non-linear mixed effects (NLME) model (Pinheiro and Bates, 2000b). The NLME model combines the vitalistic, mechanistic and empirical modeling approaches. For objectively comparing the model performances, a statistical framework can be used to determine the goodness-of-fit and the predictive ability. The residual plot analysis combined with information criterion statistics can together constitute this statistical framework. The following general objectives were formulated for this study: (i) (ii) (iii) (iv) Determination of thermal inactivation kinetics of toxigenic strains of C. difficile. Ascertaining the effect of fat content of ground beef on the thermal destruction behavior and establishing the variation of thermal inactivation kinetics among these strains. Characterize the thermal destruction behavior of spores of C. difficile using non-log linear modeling approaches. Evaluating performance of models by using a statistical framework in addition to comparing the log-linear and non log-linear models. 3

20 CHAPTER 2 LITERATURE REVIEW 2.1 Clostridium difficile Introduction Clostridium difficile is an gram-positive spore-forming anaerobic bacillus, with cells 3 to 5 μm in length. Sporulation is generally observed in agar cultures, which are in stationary or decline phase past 72 h of incubation (Brazier and Borriello, 2000). C. difficile does not sporulate in most C. difficile selective media. C. difficile is widely present in the environment (Saif and Brazier, 1996). It was discovered in 1935 from the stools of newborns. In 1978, it was recognized as being responsible for antibiotic associated diarrhea and pseudomembranous colitis(kelly et al., 1994). The recent past has observed an increase in C. difficile associated diarrhea (CDAD) cases (Roberts and Mullany, 2009). This has been attributed to emergence of hyper-virulent strains of C. difficile (Lessa et al., 2012, O'Connor et al., 2009, Roberts and Mullany, 2009). In the following sections, the virulence determinants and strain typing techniques used have been summarized Determinants of virulence A review by Dawson et al. (2009) summarizes the known and suspected virulence determinants in C. difficile. C. difficile produces two known toxins A and B. The toxin A (TcdA) and toxin B (TcdB) are related UDP glucosylating toxins. The pathogenicity locus (PaLoc), which is 19.6 kb, encodes the toxins A and B; an accessory gene, tcde; a sigma factor, tcdr; and a negative regulator, tcdc. The negative regulator is also called the antisigma factor. Some strains of C. difficile have deletions in this PaLoc such as the A-B- strains have both the toxin genes missing and the A-B+ strains have a missing tcda gene. Further, 6% of the strains of C. difficile are known to produce a binary toxin called CDT. This is an actin-specific ADP-ribosylating toxin and is encoded by cdta/cdtb (Denève et al., 2009). The presence of binary toxin has been reported in hyper-virulent strains. In strains possessing the binary toxin in addition to TcdA and TcdB, it is believed that the binary toxin acts in alongside the toxins A and B in pathogenesis and disease induction. However, pathogenic strains with truncated TcdA, TcdB and binary toxin have raised questions regarding existence of unidentified factors crucial to the pathogenesis, transmission, survival, colonization, virulence and emergence of epidemic strains of C. difficile. The spores of this pathogen can survive outside the colon. Studies have reported persistence of its spores on floors for up to 5 months (Kim et al., 1981, Mulligan et al., 1979). 4

21 C. difficile spores can persist on surfaces for long times and are resistant to commonly used disinfectants, which varies among strains (Fawley et al., 2007). Further, the epidemic strains of C. difficile have been found to have higher rates of sporulation in comparison to the nonepidemic strains (Fawley et al., 2007, Wilcox and Fawley, 2000). According to Akerlund et al. (2008), the epidemic strain of the ribotype 027/ PFGE type NAP1 demonstrated greater sporulation than the non-epidemic strains. Thus, possible association of virulence factors to increased sporulation has been suggested. Owing to the abovementioned factors, it has been suggested that the choice of cleaning agent may have an effect on the persistence of spores of C. difficile in the environment. Flagella and proteases, which enable adherence and penetration of the mucus layer, have also been suggested to be important determinants of virulence (Denève et al., 2009). 2.2 Clostridium difficile Infections (CDI) General overview The species name difficile is attributed to the difficulty in the isolation of this organism. The word difficile means hard to work with, stubborn or difficult. C. difficile is an enteric pathogen. Studies suggest that this anaerobe is prevalent in water, soil and the environment (Saif and Brazier, 1996). Owing to its highly resistant spores, it exists in healthcare facilities and the environment (Chapman, 2009, Keske and Letizia, 2010, Saif and Brazier, 1996). Toxigenic strains of this microorganism have been reported to cause diarrhea and colitis. C. difficile-associated diarrhea (CDAD) is caused as a result of the production of toxins A and B leading to the manifestation of symptoms, whose severity is a function of intensity of the infection. Symptoms of CDI range as listed in the increasing order of their severity: 1. Mild to moderate diarrhea 2. C. difficile colitis (without pseudomembranes) 3. Pseudomembranous colitis (PMC) 4. Acute abdomen with sepsis syndrome The intensity of CDI is dependent on the strain responsible for the infection. These infections have been found to recur in patients. The transmission of this organism is mainly by fecal-oral route (Chapman, 2009). Its presence in the environment can be an indicator of fecal contamination. A review by Lessa et al. (2012) discussed that in the recent years, the knowledge regarding the pathogenesis, epidemiology, diagnosis, and clinical management of 5

22 CDI has grown. The past has observed a rapid change in the epidemiology of CDI alongside development of new diagnostic methods for its detection Epidemiology of C. difficile Infections (CDI) C. difficile infections (CDIs) have been a major concern leading to patient isolation, ward closures and in extreme cases hospital closures (Dawson et al., 2009). There has been an increase in CDI cases and mortality across U.S.A, Canada and Europe (Lessa et al., 2012). Also, C. difficile has replaced mecithillin-resistant Staphylococcus aureus as the most common healthcare-associated infection. This study noted that although there was a more than two fold increase in CDI cases over 2000 to 2008, these probably leveled off in However, this data did not include cases diagnosed and treated in outpatient settings. CDI is a notifiable disease in Canada. According to Gravel et al. (2009), in Canada, the number of HA CDI cases for adult patients was 4.6 cases per 1000 admissions or 65 per 100,000 patient-days. In 2006, hospital cases were reported in addition to 6500 deaths in the U.K. In the U.S.A, according to the Centers for Disease Control and Prevention (2012), approximately deaths annually can be attributed to CDI. Also, more than cases are reported annually. Further, hospital stays due to CDI have tripled in the past decade and cost a minimum of $1 billion annually in extra healthcare costs. The incidence of hospital acquired CDI has decreased in several countries (Enoch and Aliyu, 2012). The public reporting of CDI decreased the incidence in hospitals in Canada and UK (Daneman et al., 2012) Risk factors The risk factors for CDAD are admission to a long-term health care facility or a hospital, admission to intensive care unit (ICU), administration of proton pump inhibitors and/or histamine 2 receptor antagonist (H2RA) therapy, use of antibiotics, prior renal failure, non-surgical gastrointestinal procedures (like nasogastric tube, stool softeners, anti-ulcer medications and enemas), inferior immunological response and surgeries (Barbut and Petit, 2001, Dawson et al., 2009, Keske and Letizia, 2010, McFee and Abdelsayed, 2009). The stool carriage of hospitalized patients can be as high as 35% (Chapman, 2009). Such a high level of carriage is attributed to continuous daily exposure to C. difficile spores due to highly contaminated hospital environments (Norén, 2009). The patients could be exposed through contaminated medical instruments or other surfaces, hands of health care professionals and contact with infected patients. 6

23 The continuous shedding of C. difficile spores by asymptomatic carriers may be responsible for their environmental presence (Norén, 2009). The likelihood of becoming a carrier is directly dependent on the length of the hospital stay. The probability of contracting C. difficile associated diarrhea (CDAD) is higher for carriers. This is because administration of antibiotics to a carrier may eradicate the beneficial commensal flora, leaving behind antibiotic resistant toxigenic C. difficile strains. These antibiotic resistant toxigenic strains repopulate the gut eventually leading to CDAD. A high relapse has been observed for CDI owing to the re-infection or reactivation of infection. Recurrent diarrhea following treatment of CDI has observed an increase and is due to persistent alterations of the intestinal flora in addition to ineffective immune response against C. difficile toxins (Johnson, 2009). There has been a rise in community associated CDI (Dawson et al., 2009, Kuijper et al., 2006). Owing to the continued use of antibiotics and other drugs, in addition to a rise in the number of immune-compromised and elderly, the situation of CDI cannot be expected to improve (Dawson et al., 2009) Hospital-acquired C. difficile Infections (HA- CDI) C. difficile strains belonging to ribotype 027 were responsible for severe outbreaks in northeastern states of the U.S.A, eastern Canada and United Kingdom (Leclair et al., 2010). Hyper-virulent strains belonging to this ribotype have also caused outbreaks in hospitals in England, Netherlands, Belgium, France, Germany, Finland, Norway and Sweden. A high prevalence of ribotype 027 was reported in Quebec in addition to measurable presence in Western Canada (MacCannell et al., 2006, Miller et al., 2010). C. difficile strains typed NAP1/027 were responsible for all the outbreaks in Ontario (Pillai et al., 2010). This ribotype was responsible for greater incidence, severity and mortality(leclair et al., 2010) (Miller et al., 2010). A comparison of mortality and ribotype 027 presence for all the Canadian provinces revealed the highest rates of mortality in Quebec, which may be due to the highest presence of this ribotype in this province (MacCannell et al., 2006). In addition, ribotype 078 s presence was also reported in Canadian hospitals. The epidemiology of CDI in Canada is changing, which is evident due to a decrease in the presence of ribotype 027 in Quebec, accompanied by an increase in its presence in British Columbia, Ontario and Atlantic provinces (MacCannell et al., 2006). In U.S.A, according to Centers for Disease Control and Prevention (2012), although between 2000 to 2007 there was a 400% increase in deaths due to CDAD, hospitals that followed infection control recommendations showed a 20% decline in infection rates within 2 years. In U.K., 7

24 owing to the attention by media, legal and systemic measures, C. difficile infections acquired in health care facilities have observed a decline (Brazier, 2012) Community-associated C. difficile Infections (CA-CDIs) A huge burden of hospital-acquired CDI on the healthcare facilities still prevails, along with an increase in CDI in the community (Lessa, 2013). These include healthy pregnant women, children and individuals with minimal or no healthcare exposure in the past. According to Lessa (2013), the changes in the epidemiology of C. difficile resulted in the initiation of a surveillance program for CDI by the Centers for Disease Control and Prevention (CDC) in 2009, through the Emerging Infections Program (EIP). The surveillance of 9 states in the U.S.A in 2010 resulted in the detection of 10,342 cases of CDI. Out of these, 3269 (32%, nearly 1/3 rd ) cases were community acquired. The median age of CA-CDI cases was 52 years and 61% of them were female. Also, 27% of the identified CA-CDI cases were hospitalized within 7 days after positive C. difficile stool collection. Further, the recurrence rate was found to be 9%. The fraction of CA-CDI cases in this study was higher than previously reported in the U.S.A and Canada. Further, out of the 588 CA-CDI samples submitted to the CDC, the most common pulsed field electrophoresis (PFGE) types were NAP1 (23.5%) and NAP11 (11.4%). However, fractions as large as 31.1% of these samples were unidentified. Antibiotic exposure may be an important risk factor for CA-CDI (Chitnis et al., 2013, Deshpande et al., 2013). Chitnis et al. (2013) reported that outpatient health care exposure or inpatient health care exposure without an overnight stay was a major cause of CA-CDI. The causation of C. difficile infections was found to be contaminated environmental surfaces in these outpatient settings besides administration of antibiotics. Although antibiotic exposure has been reported to be an important risk factor for CA-CDI, this risk differs with various classes of antibiotics (Deshpande et al., 2013). Deshpande et al. (2013) reported the risk to be highest following administration of lincosamides like clindamycin, followed by fluoroquinolones and cephalosporins, However, tetracycline was not linked with a greater risk. Up to 36% of the CA-CDI cases could not be attributed to antibiotic exposure and the cause was unknown. Also, it is uncertain whether or not this fraction of cases is on a rise. Further, frequent use of proton pump inhibitors (PPIs) was also suggested to increase the risk of contracting community associated C. difficile infections (CA-CDIs). However, the 8

25 mechanism by which PPI increase the susceptibility to CDI is not fully understood (Lessa, 2013). Also, contact with children younger than 2 years of age and contact with household members with CDI may also increase the risk of contracting CA-CDI. (Lessa, 2013). The Figure 2.1 summarizes the sources of exposure, risk factors and mechanism of acquisition of C. difficile infections. 9

26 Figure 2.1 A summary of the sources of exposure, risk factors and mechanism of acquisition of C. difficile infections (CDI) 10

27 2.3 Presence of C. difficile in foods Owing to the isolation of epidemic strains of C. difficile from retail meats (de Boer et al., 2011, Harvey et al., 2011b, Metcalf et al., 2011, Metcalf et al., 2010a, Metcalf et al., 2010b, Rodriguez-Palacios et al., 2009, Rodriguez-Palacios et al., 2007), seafood and fish (Metcalf et al., 2011) and vegetables (Metcalf et al., 2010b), the foodborne potential of CDI is under research. Epidemic strains of C. difficile indistinguishable have been isolated from farm animals and have known to cause disease (Keel et al., 2007). Thus, concerns of a zoonotic potential have been raised. Although several factors have been implicated to be causative of CA-CDI, further research is needed to fully understand key causes and establish an epidemiological correlation (Lessa, 2013). This section summarizes the results of the studies that tested the presence of spores of C. difficile in food Presence in meat products Studies that reported presence of hyper-virulent strains of C. difficile in food In Tuscon, Arizona, U.S.A, C. difficile was detected in 37 (42%) of 88 meat samples that were obtained by convenience sampling of 88 cooked and uncooked meat products (Songer et al., 2009). Looking at the prevalence commodity wise, 50% of ground beef (12/26), 14% of summer sausages (1/7), 43% of ground pork (3/7), 63% of braunschweiger (10/16), 30% chorizo (3/10), 23% pork sausages (3/13), and 44% ground turkey (4/9) tested positive. Further, ribotype 078/toxinotype V strains were found to be the most common, accounting for 73% of the isolates. The remaining 27% were strains of ribotype 027/toxinotype III. Also, an overall prevalence of 6.1 % for C. difficile was reported by a Canadian study, which used broad national sampling infrastructure to obtain retail meat samples from three provinces over an 8-month period (Rodriguez-Palacios et al., 2009). C. difficile was isolated from 6.7% of 149 ground beef samples and 4.6% of 65 veal chop samples. Of these isolates, 77% were toxigenic and had been associated with CDI in humans in Ontario (Weese, 2009). Moreover, 31% of the toxigenic isolates were NAP1/Toxinotype III strains, 23% were ribotype 077/NAP2 and 15% were ribotype 014/NAP4. Another interesting finding was the higher prevalence of C. difficile in winter, for which the reason was not clear. Weese et al. (2009) discussed that hyper-virulent strains of C. difficile have been detected in meat products; however, the spore load of C. difficile in these products was low. The spore load ranged from 20 to 240 spores per gram for beef samples and 20 to 60 spores per gram for pork samples. C. difficile was detected in 12% of 115 ground beef and 12% of 11

28 115 ground pork samples. Of these, 12 (86%) of 14 beef isolates and 10 (71%) of 14 pork isolates were identified as ribotype 078. Further, 1 (7.1%) pork and 1 (7.1%) beef isolate was identified as ribotype 027/ NAP1. Additionally, C. difficile was found in 1.8% of ground pork and pork chops, with ribotype 027/NAP1 accounting for 43% of the isolates. In 2010, C. difficile was detected in 7 (1.8%) out of 393 retail pork samples, which were collected from four provinces in Canada (Metcalf et al., 2010a). Out of the 7 isolates, 3 were epidemic ribotype 027 and toxinotype III. In the same year, C. difficile was also detected in 26 (12.8%) out of 203 samples of chicken (10 (9.0%) out of 111 thighs, 12 (18%) out of 72 wings and 3 (15%) out of 20 legs), which were collected from retail stores across Ontario using a standardized method (Weese et al., 2010a). All isolates were found to be ribotype 078. C. difficile was detected in 5 (4.5%) of the 111 vegetable samples obtained from 11 retail outlets in Guelph, Ontario, Canada (Metcalf et al., 2010b). Two distinct ribotypes and toxinotypes were identified: 3 of the 5 isolates were ribotype O78/NAP 7/ toxinotype V; the remaining was also associated with CDI in humans. In 2011, C. difficile was detected in 5 (4.8%) of the 119 seafood and fish samples, from five Canadian provinces (Metcalf et al., 2011). Of these 5 isolates, 4 were toxinotype V/ribotype 078/NAP7. Also, C. difficile was present in 23 (9.5%) out of 243 meat samples, from sausage manufacturing plants and retail meats in Texas, U.S.A (Harvey et al., 2011b). Out of the 23 isolates, 22 tested positive for the presence of TcdA, TcdB and CDT and were toxinotype V. When typed by PFGE, these were NAP-7 or NAP-7 variants. However, the antimicrobial resistance of these isolates was reported to be less in comparison to other meat, animal and human isolates. In 2012, C. difficile was detected in 2 (~2%) of the 102 ground meat and sausage samples from three grocers in Pittsburg, PA, U.S.A (Curry et al., 2012). All the isolates were ribotype 078, which encompassed 6 genotypes. Recently, C. difficile was detected in 4.7% of 107 pork and 2.3% of 133 beef samples (Rodriguez et al., 2014). Of these isolates, 50% were ribotype 014 and 25% were ribotype 078. Furthermore, the meat isolates were indistinguishable from human isolates from Belgium. Moreover, C. difficile was also detected in 13 (2%) of the 660 samples (6 (9%) out of 67 buffalo meat, 3 (3.3%) out of 92 goat meat, 1 (1.7%) out of 121 beef (young cattle), 1 (0.94%) of 106 cow (adult dairy cattle) meat and 1 (0.9%) of 150 sheep meat) collected from 49 butcheries across two Iranian provinces (Rahimi et al., 2014). Out of the 13 strains, 7 were positive for all three toxins and were ribotype 078; 4 were positive for toxins A and B; one was positive only for toxin B. 12

29 Thus, these studies suggested the foodborne potential of CA CDI by detecting hypervirulent strains of C. difficile in food, which were indistinguishable from clinical CDI isolates Studies that linked food with CA CDI cases In 2007, C. difficile was detected in 12 (20%) out of 60 retail ground meat samples (21% of 53 ground beef and 14% of 7 ground veal samples) (Rodriguez-Palacios et al., 2007). In this study, 8 (67%) of the 11 toxigenic isolates detected belonged to toxinotype III. These strains encoded for toxins A, B and the binary toxin CDT in addition to an 18 bp deletion in the tcdc region. Although, it was designated as NAP1 by Pulsed Field Gel Electrophoresis (PFGE) typing, its ribotype pattern did not match 027. In 2010, C. difficile was detected in 3 (3%) of the 100 retail beef samples tested in Austria (Jobstl et al., 2010). Out of the 3 isolates, 2 were ribotype AI-57, negative for toxin genes and sensitive to all antibiotics. However, the third isolate was toxigenic (A + B + ) and frequently observed in CDI patients in Austria. In a French study, 2 (1.9%) of the 105 ground beef samples tested positive, all of which were vacuum packed (Bouttier et al., 2010). All the isolates were identified to be toxinotype 0 and ribotype 012. It pointed out that the ribotype 012 was among the ten regularly isolated ribotypes from patients with CDI. Further, C. difficile was detected in 6.3% of 48 meat samples (2 (8.3%) of 24 ground beef, 1 (4.2%) of 24 ground pork samples) tested in Winnipeg, Manitoba, Canada (Visser et al., 2012). All the strains from retail meats had been previously isolated from human patients with CDI in this province Other studies Although C. difficile was detected 8 (1.6%) out of 500 samples tested in the Netherlands, one isolate from chicken meat sample belonged to PCR ribotype 001, previously reported in humans (de Boer et al., 2011). C. difficile was absent in the 1755 retail meats (617 ground beef, 614 ground turkey, 259 chicken breasts, 265 pork chops) tested in the U.S.A (Limbago et al., 2012). Furthermore, C. difficile was detected 93 (12.8 %) out of 723 ground meat samples and most of these isolates were toxigenic, however, none of the isolates belonged to the hyper-virulent ribotypes 078 or 027 (Sugeng, 2012). 13

30 2.3.2 Section summary C. difficile has been isolated frequently in food products from diverse geographical areas and the detection rates range from 0 to 45% in retail meats (Marsh, 2013). The differences in the detection rates may be ascribable to differences in culture techniques, sampling strategies, food types examined, methods for processing food samples and cross contamination in laboratory environment (Marsh, 2013). Strains of C. difficile indistinguishable from human CDI patients have been detected in food (Visser et al., 2012). In some cases, these isolates were found to be hyper-virulent strains (Metcalf et al., 2011, Metcalf et al., 2010a, Metcalf et al., 2010b, Rahimi et al., 2014, Rodriguez-Palacios et al., 2009, Weese et al., 2010b). Thus, a possibility of foodborne transmission of CDI has been raised. The strategies to prevent exposure of humans to CDI-associated strains of C. difficile are summarized in the following section. 2.4 Prevention strategies Causes of contamination It is important to identify potential causes of contamination before formulating the prevention strategies. The high prevalence of ribotype 027 in foods may be due to its high sporulation (Akerlund et al., 2008, Weese, 2009). Epidemic strains of C. difficile sporulate more and are less sensitive to disinfectants than non-epidemic strains (Fawley et al., 2007, Wilcox and Fawley, 2000). Thus, high sporulation and ineffective disinfectants may be responsible for the presence of these epidemic strains in food (Weese, 2009). Other causes of contamination may be exposure to contaminated imports, manure, soil and infected or carrier humans (Metcalf et al., 2010b), carcass contamination (Rodriguez et al., 2014), poor hygiene, contaminated processing equipment or the environmental sources (Curry et al., 2012, Limbago et al., 2012, Weese, 2009) Known foodborne pathogens and prevention strategies Foods of animal origin have been a major source of illness and interventions for known foodborne pathogens are in place to prevent exposure and subsequent outbreaks of infections (Hoorfar et al., 2011). These pathogens are disseminated by animal reservoirs through feces into the soil and grass. This can lead to infected herds; contaminate farm produce, water and the environment. Further, enteric pathogens of food animals may contaminate the carcass during processing and carry over to the consumer. 14

31 According to Hoorfar et al. (2011), in order to prevent exposure to pathogenic microorganisms in foods, determining the presence and understanding the behavior of the target microorganism is a prerequisite. Following this, adding interventions at various stages can help reduce or prevent transmission along the food chain. Also, in spite of the introduction of new scientific information, novel detection methods and interventions in the food chain, measures must be incorporated throughout the food chain to ensure food safety. For known foodborne and zoonotic pathogens, biosecurity, farm management and biocontainment strategies can reduce exposure of cattle to pathogens. Also, it is known that onfarm HACCP has been successful for E. coli 0157:H7 and Listeria monocytogenes. However, with the current information, fabricating intervention and implementing broad schemes throughout the food chain does not seem feasible. Also, economic feasibility for such interventions limits their use. We know that thermal inactivation of the pathogens is the last line of defense in the food chain. Adequate cooking of foods prior to consumption is a simple way to ensure food safety. Foods cooked to ensure complete inactivation of pathogens are regarded as safe for consumption. Safe Internal Cooking Temperatures have been recommended by Health Canada (2012) for meat products like beef, pork, chicken and many others. Safe Internal Cooking Temperature is the temperature that must be achieved and maintained in the center of the meat in order to ensure sufficient inactivation of pathogens of concern. Table 2.1 summarizes the Safe Internal Cooking Temperature for meat products. Based on studies performed on known food pathogens (e.g. E. coli O157:H7, Salmonella spp., Listeria monocytogenes) 71 C is the recommended cooking temperature for ground meat and meat mixtures (ground beef, pork, lamb and veal, burgers, sausages, meatballs, meatloafs, etc.) and pork. The required lethality to sufficiently inactivate these pathogens is achieved instantly at this temperature (United States Food Safety and Inspection Service, 1999). However, some studies have demonstrated the survival of C. difficile spores after heating at 71 C for 2 h (Rodriguez-Palacios et al., 2010). Thus, to prevent exposure to C. difficile spores by heating, we need to know its thermal inactivation kinetics. The subsequent section elaborates on the literature available regarding the thermal resistance of spores of C. difficile. 15

32 Table 2. 2 Recommended 'Safe Internal Cooking Temperatures' for meats Safe Internal Meat type Specifications Cooking Temperature Beef, veal, lamb (whole pieces, cuts) Medium-rare 63 C Beef, veal, lamb (whole pieces, cuts) Medium 71 C Beef, veal, lamb (whole pieces, cuts) Well done 77 C Pork (whole pieces, cuts) 71 C Ground meat and meat mixtures Beef, veal, lamb, pork 71 C Ground meat and meat mixtures Chicken, turkey 74 C Poultry Pieces 74 C Poultry Whole 85 C (Source: Health Canada (2012)) 16

33 2.5 Thermal Inactivation of C. difficile This section summarizes the studies that have been carried out to determine the thermal resistance of spores of C. difficile. The D 100 C for C. difficile spores ranged from 2.5 to 33.5 min, compared to D 100 C of 6 to 17.6 min for spores of C. perfringens (Nakamura et al., 1985). Spores of C. difficile strains representing distinct meat- and bovine- ribotypes reduced by 2 log 10 after heating at 71 C for 120 min (Rodriguez-Palacios et al., 2010). Further, they reported 10 % spore survival when samples heated at 71 C for 30 min were reheated at 85 C for 10 min. However, a total inactivation was observed, when samples were heated at 71 C for 30 min followed by re-heating at 85 C for 20 min. A major limitation of this study is the determination of inactivation kinetics was performed in a buffer medium, necessitating the determination in beef, pork, chicken and other food products. Another study determined the effects of heating at four different temperatures: 63, 71, 85, and 96 C (Rodriguez-Palacios and Lejeune, 2011). The heating media (3% ground beef, 30% ground beef and beef gravy (0% fat)) were inoculated with a 1:1:1:1 mixture of C. difficile PCR ribotypes 027, 078, 077 and ATCC 9689 and thermal studies were conducted. They revealed that spore aging affects thermal resistance of spores at 63 C. A temperature of 63 C reduced the spore concentration by 1 log 10 over 30 min, if the spores were fresh. However, for aged spores, heat treatment for 30 min at this temperature increased the spore count by 30%. This increase was due to the reactivation of superdormant spores due to sublethal heat treatment. Furthermore, a 5 to 6 log 10 reduction in spore concentration was observed after 15 min of heating at 85 C irrespective of the spore age. Also, heating at 96 C for 1 to 2 min led to a 6 log 10 reduction. Additionally, as can be seen from the Figure 2.2, the percentage of fat in ground beef can possibly affect the D values of spores of C. difficile, however, large standard deviation associated with the D values renders the effect uncertain. Cooking food above 85 C was suggested to ensure adequate destruction of spores of C. difficile in food. The D values by Nakamura et al. (1985) are much higher than recently reported for C. difficile. This may be attributable to the heat shock step, which was employed prior to performing the thermal studies. In this step, spores were heating at 80 C for 10 min prior to subjecting to a heat treatment at 90 or 100 C. A sub-lethal heat shock may select more resistant members of the spore population. To cite an instance, the D 100 C for spores of 9 out of 10 strains of C. perfringens increased significantly (p < 0.05) following a sub-lethal heat shock at 75 C for 20 min (Juneja et al., 2003). Further, heat shock can induced higher 17

34 resistance in partly activated spores of B. stearothermophilus ATCC 7953 at 100 C than dormant spores (Beaman et al., 1988). The resistance of Salmonella Thompson at 54 and 60 C increased if cells were subjected to a heat treatment at 48 C for 30 min prior to performing the thermal study (Mackey and Derrick, 1987). This effect was observed for all the heating media (tryptic soy broth, liquid whole egg, 10% and 40% reconstituted milk and minced beef). For Listeria monocytogenes, heat treatment at 48 C for 120 min prior to performing thermal studies increased the D 64 C by 2.4 folds, however, there was no significant increase observed when cells were heated at 48 C for 30 or 60 min (Farber and Brown, 1990). Another possible reason for high resistance reported by Nakamura et al. (1985) in comparison to Rodriguez-Palacios and Lejeune (2011) may be assignable to different strains used in the two studies. For instance, for C. perfringens, significant variations in the heat resistances of strains have been reported. Thus, there exists uncertainty regarding the thermal resistance of this microorganism. With regard to the Bigelow s first order model, more precise estimates of the D values in the range of 71 to 85 C are needed. Furthermore, the temperature dependence of thermal resistance also needs to be determined. Thus, the following section elaborates on determination of thermal inactivation kinetics of microorganisms. 18

35 Figure 2.2: Effect of heat on inhibition of aged spores of C. difficile in three food matrices namely 30% fat ground beef, 0% fat beef gravy and 3% fat lean ground beef. (Source: Rodriguez-Palacios and Lejeune, 2011) 19

36 2.6 Determination of Thermal Inactivation Kinetics In order to develop a thermal process, sufficient information regarding the target microorganism, the microbiological history and post-treatment storage conditions of food product, and the thermal inactivation kinetics of the target are needed (Ramaswamy and Marcotte, 2006). In this research, targets are spores of Clostridium difficile. Since spores are more thermo-tolerant in comparison to vegetative cells, destruction of spores would suggest total inactivation of vegetative cells. Thermal inactivation kinetics of microorganisms and their spores has been reported to follow a first order reaction, i.e. a logarithmic order of death. D value is a key parameter of thermal destruction kinetics, and represents the microbial destruction rate. D value that results in one decimal reduction in the surviving microbial population. Alternatively, it may be stated as the time required for 90% reduction in the microbial population. From Figure 2.3, it is the time interval within which the survival curve crosses a single logarithmic cycle. Figure 2.3 A typical survivor curve The decimal reduction time can be expressed by equation 2.1. D = t 2 t 1 log 10 a log 10 b 2.1 where, a and b are the survivor microbial populations at times t 1 and t 2, respectively. 20

37 The temperature dependence of D value is expressed by the z values. It is also known as the temperature sensitivity indicator. It is obtained from the thermal resistance curve, which is a plot of the logarithm (base 10) of the D values versus the thermal treatment temperatures. Figure 2.4 gives a typical thermal resistance curve. Figure 2.4 A typical thermal resistance curve The z value is defined as the difference in the thermal treatment temperature required for increasing or decreasing the D value by tenfold. Graphically, it is the difference in the heating temperature that causes a unit logarithmic increase or decrease in the D value. It can be expressed by equation 2.2. z = T 2 T 1 log 10 D 1 log 10 D where, D 1 and D 2 are D values at the temperatures T 1 and T 2, respectively. 2.7 Alternative approaches to modeling thermal inactivation kinetics data According to van Boekel (2002), microbial survival curves are a reflection of the cumulative distribution of the death times of the microbial population. Thus, the first order log-linear model may be a special case instead of being a rule. There are two assumptions that are intrinsic to the log-linear inactivation kinetics. These are as follows: 21

38 i. The probability of inactivation of spores or cells is independent of the time of exposure to a treatment. ii. The inactivation resistance of all spores or cells in the population is the same. According to the mechanistic hypothesis, the shape of the survival curve is determined by the reaction between the organism and the reagent (Withell, 1942). When the survival curve is a straight line, the death rate is considered to be similar to a monomolecular chemical reaction. However, the monomolecular chemical reaction theory or the quantum theory of cell destruction may not be well supported by experimental evidence and knowing that variation is a fundamental law of living matter, the acceptance of this theory is questionable (Withell, 1942). The deviations from the log-linear model may be explained by the vitalistic theory (Corradini and Peleg, 2007a, Corradini and Peleg, 2007b, Peleg and Cole, 1998) According to this theory, the inactivation times or the resistances of spores in a population are not identical and follow a distribution. The distribution of survival times is extremely skewed for most of the microorganisms, however, if the survival times are plotted as logarithms instead of absolute numbers, all the time-survivor curves yield a normal distribution (Withell, 1942). Among the different spores or cells of a microorganism of a pure culture, difference in the degrees of resistance is permanent. Henceforth, it is also termed as the theory of variable permanent resistance. This may be employed for sigmoidal or upward concave curves. According to Pinheiro and Bates (2000b), the non-linear regression models can also be established on a mechanistic framework, with model parameters possessing a physical interpretation. The semi-mechanistic non-linear regression models are derived empirically but integrate the theoretical framework underlying the data such as asymptotes and monotonicity. Another type of model is a polynomial model, which is linear in its parameters (Pinheiro and Bates, 2000a). By increasing the order of a polynomial model, the model tends to give a closer approximation of the true regression function. Although the predicted model is true within the observed range of the data, it is based solely on the observed association of the results and the covariates. Also, it does not take into account the theoretical framework of the underlying mechanism responsible for the data. Non-linear models, in comparison to the linear models like polynomial models, use fewer parameters and are therefore parsimonious (Pinheiro and Bates, 2000a). Also, they can be used to make more accurate predictions beyond the observed range of the data. For modeling non-log linear inactivation kinetics, there are a variety of models that may be employed for better description. These are: Weibull (Peleg and Cole, 1998, Seminario 22

39 et al., 2011, van Boekel, 2002), Peleg kinetics (Seminario et al., 2011), log-quadratic (Stone et al., 2009 ), log-normal (Withell, 1942), log-logistic (Anderson et al., 1996, Cole et al., 1993) and bi-phasic. The following sections summarize the Weibull model, the Weibullian log-logistic model, the log quadratic model and the three parametric log logistic models Weibull model Several semi-logarithmic survival curves initially considered linear are slightly curved (Peleg and Cole, 1998). The curvature of the survivor curves can affect the calculations of the thermal death time and the magnitude of this effect depends largely upon whether the curve is concave upwards or downwards and the method of calculation of decimal reduction time. artifacts. Initially, the curvilinearity was attributed to mixed populations or experimental However, according to an alternative explanation the survival curves may be considered as a cumulative form of temporal distribution of microbial death or damage (van Boekel, 2002). Thus, each spore is inactivated at a different time due to an underlying spectrum of heat resistances or inactivation times, the distribution of which ultimately decides the shape of the survival curve. Thus, this concept does not regard these curves as inactivation kinetics of different orders rather regards them as a statistical distribution of inactivation times t c. Several distributions are possible, with the basic requirement being simplicity. This is called the principle of parsimony; also known as Ockham s razor, which suggests that for adequate representation of data, one should strive for smallest number of parameters. The Weibull model is a simple yet flexible model widely used in reliability engineering and thermo-bacteriology for analyzing survival data (van Boekel, 2002). In reliability engineering, survival data analysis may be viewed as the time of exposure to stress after which the microorganism or spores fail to grow. This model does not take into account the mechanisms of inactivation and spore dormancy, but considers two possible states: active spores and inactivated spores. The cumulative function for the Weibull model is given by equation 2.3 and 2.4. S(t) = e [( t α )β ] 2.3 log S(t) = (t α )β where, S(t) is the survivor function given by equation 2.5. It is defined as the ratio of the surviving spores at time t (N t ) to the initial number of spores (N 0 ) at time t=

40 S(t) = N t N Solving the equation 2.5 for Weibull model, we get equation 2.6. ln( ln ( N t N 0 ) = β ln t β ln α 2.6 The plot of ln[-ln(s(t))] versus ln(t) is termed as the hazard plot. The slope of the plot yields β and the intercept yielded -β lnα. The reliable life is defined as the 90% percentile of the failure time distribution (van Boekel, 2002). The concept of reliable life has been used in reliability engineering and is analogous to the D value. It is given by equation t R = α ( ln 0.1) β = α (2.303) β 2.7 For d decimal reductions, the general form may be expressed as equation 2.8. t d = α ( ln(10 d 1 ) β ) Weibull log-logistic model This model is an experimental derivative from observed isothermal survival curves of spores and microorganisms (Corradini and Peleg, 2007b). In this model, the data is fitted to the modified Weibull model given by the equation 2.9 (Seminario et al., 2011): log 10 N t N 0 = β t n 2.9 Here, β is a temperature dependent parameter. It is defined by a linear or a nonlinear log logistic function. The log-logistic function characterizing the temperature dependence of β is given by the equations 2.10 and or β T = ln(1 + e k(t T c ) ) 2.10 β T = ln(1 + e k(t T c ) ) m 2.11 Here, T c is the beginning of the temperature range at which lethality intensifies; k is a constant representing the slope of the curve of β versus temperature at T>T c. The thermal resistances of microorganisms can be compared, if the parameters k and T c are known (Corradini and Peleg, 2007b). Further, an explanation of the secondary log logistic model of β is: for T T C ; b(t) 0 and for T T C ; b(t) = k (T T C ). This interpretation of the model is in accordance with the conception of a qualitative difference between the influence of low and high temperatures. A higher T c and a lower k indicate higher thermal resistance. 24

41 Also, the linear form offers an acceptable characterization of the temperature dependence, thus the use of non-linear form is not necessary (Corradini and Peleg, 2007a) Log-quadratic model This model is an approximation of the mechanistic biphasic model and the vitalistic Weibull model, and integrates the vitalistic and the mechanistic hypotheses (Stone et al., 2009 ). In this model, to obtain an acceptable fit of the survival curves, the parametric constraints for the biphasic and Weibull models are overlooked. Also, this model can be used to fit curves with upward or downward concavity, however is unsuited for extrapolation beyond the experimental observational range (Stone et al., 2009 ). The survival data is fitted to the quadratic equation as given by equation log y i = α Ri + β Ti t i + γ Ti t 2 i + ε i 2.12 Here, y i is the i-th measurement of the spore concentration, R i is the replicate of the number of measurement, T i is the temperature for the measurement i, and t i is the time interval for the measurement i, ε i is the error due to random variation in the determination of the survivors Three parameter log-logistic model The three-parameter log-logistic model is a non-linear mixed effects model, which uses a self-starting nls logistic model. According to (Pinheiro and Bates, 2000a), mixed effects models incorporate both the fixed effects and the random effects. Fixed effects are the parameters that are associated with the entire population and the experimental variables are well categorized, where each category is a level. However, random effects account for samples drawn at random from a population, samples being individual experiments. Mixed effects models can be used to describe relationships between response variables and covariates in cases where the data is classified based on one or more considerations. Thus, in such cases the experimental variable is a subject, which in this case, is a spore or a microorganism. In case of spores, the fixed effects will take into consideration the thermal resistance as a factor. However, since the thermal resistance of the spore population has been proposed to be a distribution, the spore population withdrawn after exposure to lethality is a sample representative of the total spore preparation. Non-linear regression models are used due to their interpretability, parsimony and validity beyond the observed range of data (Pinheiro and Bates, 2000b). Thus, the non-linear 25

42 mixed effect (NLME) model presents a hybrid modeling approach that can be viewed as a mixture of the mechanistic, vitalistic and empirical modeling approaches. A self-starting nonlinear regression model is composed of two components: the first is an auxiliary function component that determines the starting estimates and the second determines the non-linear regression function itself. The three parameter log-logistic model, which employs a selfstarting function SSlogis in R programming, is given by the equation 2.13 y = asym 1 + e (xmid x scal ) 2.13 where, asym is the asymptotic height; xmid is the inflection point or the log 10 (t) in which the log 10 (N) reaches 0.5*asym and scal is the scale parameter. Here, N is the survivor population at time t. 26

43 PREFACE TO THE CHAPTER 3 C. difficile Infections (CDI) have increased over the past decade and one third of these infections are community associated (CA CDI). Although the exact source of these infections is still under not clearly understood, food has been considered to be a possible source. Previous research demonstrates that the strains of C. difficile isolated from food are indistinguishable from strains associated with CDI in humans. If food is a possible source of hyper-virulent strains of C. difficile, it is important to develop a thermal treatment that prevents their survival. Existing literature on thermal inactivation of C. difficile is quite variable with some demonstrating that cooking to an internal temperature of 71 C for 2 h decreased the C. difficile spores concentration by tenfold. In addition, fat content of the heating medium can influence thermal resistance of spores; hence, evaluating the effect of fat content of ground beef on thermal resistance of C. difficile spores is a necessity. Further, variation of thermal resistance of spores or vegetative cells among different strains is well documented. Thus, this research study is aimed at evaluating the thermal inactivation kinetics C. difficile as influenced by fat content of ground beef and strains of C. difficile. A part of this research was communicated as an oral presentation at the Northeast Agricultural and Biological Engineering Conference (NABEC), 2014 and was awarded the third place. This research was conducted at the Clostridium Research Laboratory, Bureau of Microbial Hazards at the Food Directorate, Health Canada in Ottawa. The candidate carried out the experimental work and data analysis under the supervision of Dr. Hosahalli Ramaswamy and Dr. John Austin. 27

44 CHAPTER 3 EFFECT OF FAT AND INTER-STRAIN VARIATION OF HEAT RESISTANCE OF SPORES OF CLOSTRIDIUM DIFFICILE IN GROUND BEEF 3.1 Abstract Food has been considered as a source of hyper-virulent and epidemic strains of C. difficile. In this study, the thermal destruction kinetics of spores of C. difficile was evaluated and characterized using the first order log-linear model using ground beef of two fat contents and for two strains (ATCC and ATCC 17857). The temperature range used was 74 to 82 C. The D values ranged from 3.4 min at 82 C to 146 min at 74 C. The z values ranged from 5.4 to 5.6 C. The heat resistance of C. difficile spores was significantly lower for regular ground beef (p<0.05) and in addition, there were differences (p<0.05) between the D values of the two strains. To achieve a 4D reduction of spores of C. difficile, the calculated heating times varied between 20 min at 82 C and 10 h at 74 C. Ultimately, this study provides important information to address development of thermal treatment to inactivate C. difficile in food. 28

45 3.2 Introduction Clostridium difficile, a spore-forming pathogen, is a major cause of antibioticassociated diarrhea and pseudomembranous colitis in humans. C. difficile infections (CDI) have increased in the recent years, and one third of these are community associated (CA CDI) (Lessa, 2013, McFee and Abdelsayed, 2009). This increase was due to the emergence of epidemic strains of C. difficile (Denève et al., 2009). Although the exact source of CA CDIs is still under not well understood, epidemic strains of C. difficile have been detected in vegetables (Metcalf et al., 2010b), fish and seafood (Metcalf et al., 2011), and retail meats (Curry et al., 2012, Harvey et al., 2011a, Harvey et al., 2011b, Rodriguez-Palacios et al., 2009, Rodriguez et al., 2014, Weese, 2009). Although the results of these epidemiological investigations that determined the prevalence of epidemic strains of C. difficile in food were varied, there appears to be increasing possibility of food being a source of C. difficile. Heat treatment has been traditionally used to prevent outbreaks due to bacterial pathogens in foods. Rodriguez-Palacios et al. (2010) in their thermal inactivation studies reported a single decimal reduction in C. difficile spores after heating at the recommended cooking temperature of 71 C even after for 2 h. Also, heating at 85 C for 15 min resulted in 5 to 6 log 10 reduction and heating at 96 C for 2 min resulted in 6 log 10 reduction in spore survivors (Rodriguez-Palacios and Lejeune, 2011). Therefore, they reported that heating food to temperatures above 85 C would be necessary to prevent ingestion of active spores of C. difficile. However, detailed information on thermal destruction kinetics of C. difficile spores in the temperature range C is still lacking. As summarized in the Journal of Food Science Supplement (Kinetics of Microbial Inactivation for Alternative Processing Technologies, IFT 2000), the thermal inactivation kinetics of vegetative cells of E. coli O157:H7, Salmonella spp., Listeria monocytogenes, Camplylobacter jejuni and spores of Clostrdium perfringens, Bacillus cereus and Clostridium botulinum is strongly dependent on the composition and nature of the heating medium. For example: for Bacillus cereus spores, the D 95 C varied from 6.7 to 10.1 min depending on the heating medium. Furthermore, studies have reported the sensitizing effect of monoglycerides and fatty acids on spores of bacteria. Tremoulet et al. (2002) observed the sensitizing effect of free fatty acids of palmitic acid (C16:0), palmitoleic acid (C16:1), stearic (C18:0), oleic acid (C18:1) and linoleic acid (C18:2) on spores of G. stearothermophlius. This was also found for spores of Bacillus cereus NTCC and Clostridium sporogenes Pasteur 79.3 (Lekogo et al., 2010). 29

46 Although fat has been implicated to influence the thermal destruction of bacteria, Rodriguez-Palacios and Lejeune (2011) found the effect of fat content on the thermal inactivation of C. difficile spores to be unclear. The nutrition facts table for lean ground beef and regular ground beef obtained from the National Nutrient Database for Standard Reference, a database developed by the Agricultural Research Service of the U. S. Department of Agriculture, shows that both types of ground beef (lean 15% fat and regular 30% fat) had significant amounts of 16:0 and 18:0 saturated fatty acids (SFA) contributing to the total SFA content, while 18:1 monounsaturated fatty acids (MUFA) contributed to the total MUFA content and 18:2 polyunsaturated fatty acids (PUFA) mostly contributed the total PUFA content. Thus, the effect of fat content in ground beef on the thermal resistance of bacteria needs further examination. Since strain-to-strain variation on heat resistance is also important, this was also included as one of the objectives. The objectives of this study were therefore to: (i) determine the thermal inactivation kinetics of toxigenic strains of C. difficile; (ii) evaluate the effect of fat content (two levels) of ground beef on the thermal inactivation kinetics, and to (iii) observe the variation of thermal inactivation kinetics among two strains of C. difficile. 3.3 Methods and Materials C. difficile Spore Culture Preparation The freeze-dried cultures from American Type Culture Collection (ATCC) were revived on McClung-Toabe 1.5% agar (Difco, Tucker, GA) with 5% egg yolk and 0.5% yeast extract (MTEYE) and were allowed to grow at 35 C for 48 h. One pure colony from the plate was transferred to each of three MTEYE petri-plates by streaking over the entire surface of the plates. The plates were then incubated at 35 C for 96 to 120 h. The plates were then flooded with 5 ml 0.067M Sorensen s phosphate buffer (ph 7.0) and C. difficile colonies were scraped from the surface of the plate. The wash step was repeated to transfer any remaining colonies and collected in a sterile falcon tube, which was then centrifuged at rpm for 10 min. The pellet was removed and re-suspended in 0.067M Sorensen s phosphate buffer (ph 7.0) and the vegetative cells were killed by, heat shock at 60 C for 20 min. The suspension was centrifuged at rpm for 10 min and the pellet was collected. Then, the pellet was suspended in 50% ethanol for 1 h at room temperature after which the spore suspension was centrifuged at again at rpm for 10 min. The pellet was then washed with 20 ml of sterile cold distilled water and centrifuged at 7000 rpm for 10 min. This step was repeated three times. The washed spores were then suspended in 5 ml 0.067M 30

47 Sorensen s phosphate buffer (ph 7.0), vortexed to break clumps and the volume was made up to 50 ml. Following this isolation and washing, the spore suspension was dispensed in 1.5 ml tubes and frozen stored at -20 C. The spore suspensions were prepared for several ATCC strains (ATCC 43600, ATCC 43601, ATCC 43602, ATCC 43603, ATCC 17857, ATCC 17858, ATCC 43594, ATCC and ATCC 43597) of C. difficile, which were then screened for thermal resistance as detailed the following section Selection of strains for thermal treatment Several well-characterized ATCC strains were screened for their abilities for sporulation. 100 μl of the spore suspensions were spread onto the BHIS medium and incubated at 37 C for 8 days. Following this, the colonies were washed using 5ml 0.067M Sorensen s phosphate buffer (ph 7.0) and the suspension was collected in a 15 ml tube. After centrifugation at 10,000 rpm for 10 min, the pellet was re-suspended in 50% ethanol. The suspension was again centrifuged at 10,000 rpm for 10 min and the pellet was suspended in 5 ml 0.067M Sorensen s phosphate buffer. 5 μl of the spore suspension was transferred to a slide and observed under a phase contrast microscope. The percentage sporulation was calculated as the ratio of spores to the total bacteria on the slide. The strains with percentage sporulation ranging from 90 to 95 were selected. Out of these strains, the ones that were toxigenic were selected. ATCC strains screened were ATCC 43600, ATCC 43601, ATCC 43602, ATCC 43603, ATCC 17857, ATCC 17858, ATCC 43594, ATCC and ATCC Several food isolates of C. difficile isolated during a study performed at Health Canada (Sugeng, 2012), which examined the prevalence of C. difficile in ground meat were also screened. Ultimately the strains of C. difficile with high sporulation and germination ability in addition to ability to produce toxins were selected Preparation of sterile beef matrix The heating mediums used were lean 15% fat ground beef and regular 30% fat ground beef. Retail ground beef of President Choice brand (PC ) (15% and 30% fat per 100 g ground beef) was purchased from a supermarket in Ottawa, Ontario. The composition of ground beef was obtained from the nutrition facts table of the ground beef product. Ground beef was then packed in plastic bags (10g per bag) and heat-sealed. These meat bags were then frozen at -40 C as previously done by (Juneja et al., 2001) and Velugoti et al. (2011). The protocol was similar to as done by Juneja et al. (2001). However, there were some modifications. The frozen meat bags were irradiated at 25 kgy to eliminate indigenous flora. 31

48 Following this, randomly selected samples were tested to verify the sterility of lean ground meat samples. Meat samples were serially diluted in 0.067M Sorensen s Phosphate buffer (ph 7.0) and then plated on BHIS medium. The growth medium contained the following per 1L: 47 grams of Brain Heart Infusion Agar (Oxoid, Code: CM1136), 5g yeast extract, 10 ml of 10% (w/v) L-cysteine and 10% (w/v) filter sterilized taurocholic acid (Sorg and Dineen, 2009). The petri-plates were incubated at 37 C for 48 h Sterility verification of irradiated samples Meat samples were serially diluted in M Sorensen s phosphate buffer (ph 7.0) and then plated on BHIS medium. The petri-plates were incubated at 37 C for 48 h. The colony forming units were enumerated and reported. Absence of viable colonies on the plates was considered to pass the sterility test Ground beef inoculation Irradiated ground beef sample (10 g) and a micro-centrifuge tube containing 1 ml of spore stock (10 6 spores per ml) of C. difficile were thawed in a refrigerator prior to inoculation. 1 g ground beef was added to a plastic bag (Nasco Whirl-pak, 2 oz., 75 mm x 125 mm x 0.57 mm) along with 100 µl of spores to give 10 5 spores per gram ground beef and hand massaged for a 1-2 min for evenly distributing the spores in the ground beef matrix. Following this, the bags were sealed using a heat sealer. The negative control used was 1 g irradiated ground beef with 100µL of Sorensen s buffer (no innoculum). Figure 3.1 (a) shows the sealed plastic bag (Nasco Whirl-pak, 2 oz., 75 mm x 125 mm x 0.57 mm) with inoculated ground beef sample. To test the integrity of these heat sealed bags with inoculated ground beef, they were then observed under a simple microscope. Following this, the bags were assembled as shown in Figure 3.1 (b). 32

49 a) b) Figure 3.1 (a) Heat sealed Nasco Whirl-pak bag with 1 gram of inoculated ground beef (b) Assembly of inoculated beef samples and thermocouples prior to performing the thermal study Thermal treatment The thermal treatments were carried out in a programmable temperature controlled water bath. The uniformity of temperature distribution within the water bath was monitored using thin wire copper constantan thermocouples attached to a data logger. Prior to the experiments, temperatures were monitored at different locations in the water bath to monitor the temperature uniformity of the bath, which was found to be uniform because of good 33

50 circulation and temperature controller. The sample temperature was monitored with a thermocouple located at the center of un-inoculated ground beef sample bag. Also, the input from this thermocouple was used to estimate the come-up time (CUT) of the meat sample. Figure 3.2 shows the picture of an un-inoculated meat bag with thermocouple positioned at the geometrical center of ground beef sample. This bag was positioned diagonally across the heating element, representing the slowest heating location in the bath and yielding the maximum possible CUT. Further, the bath temperature was measured using another thermocouple located at the center of the water bath. Figure 3.2 Un-inoculated ground beef with a copper constantan thermocouple at the center to estimate the come-up time and water bath temperature Sample bags were immersed in the water bath using an aluminum mesh basket as shown in Figure 3.1(b). The thermal treatments were carried out at set temperatures of 74, 76, 78, 80 and 82 C. These temperatures were selected based on preliminary experiments conducted in the temperature range between 70 and 85 C. At each temperature, five to six holding times (excluding the CUT). Further, test samples removed immediately after immersion in to the water bath and at the end of CUT, helped determine the initial spore concentration in the meat and residual survivor at zero holding time (positive control). After 34

51 thermal treatments, inoculated meat bags were cooled immediately in an ice bath and spores were enumerated within 30 min Enumeration of surviving spores All spore enumerations were made in duplicates, using the spread plate technique. To 1.1 g (1 g ground beef with 0.1 ml spore suspension) of thermally treated sample, 4 ml of M Sorensen s phosphate buffer was added to yield a 1.1:5.1 dilution. The meat slurry was then mixed using a vortex mixer for one minute. The supernatant liquid was then transferred to another tube and was considered as the first sample for further dilution. This was then serially diluted from 10-1 to 10-3, and 100µL aliquots of the dilutions were plated on BHIS medium with 0.1 % taurocholic acid and incubated anaerobically at 37 C for 48 to 72 h. The colony forming units were then enumerated, multiplied by the corresponding dilution factors and reported as survivors of the thermal treatment Data analysis Test data were analyzed to determine the thermal inactivation kinetic parameters based on the following assumptions: (i) The inactivation of spores of C. difficile in ground beef treated any given temperature occurred at random in accordance with the first-order kinetic model (thermal inactivation of spores occurring exponentially as a function of time). (ii) The thermal studies were carried out at uniform temperature in the water bath and in small bags containing 1g of inoculated ground beef the spores were uniformly distributed. (iii) The spores were homogenous with regard to thermal resistance. Based on the assumptions stated above, the D values were estimated from the survivor curves by plotting the log 10 (N/N 0 ) versus time (min) as the negative reciprocal of the slope of the survival curve. Mathematically, the equations used to estimate the D value were represented by equations 3.1 (survivor curve) and 3.2 (two point formula). log 10 N N 0 = t D ; Slope = 1/D 3.1 where, N is the survivor population of spores (CFU/ml) at time t and N 0 is the initial concentration of the spores prior to the heat treatment at holding time zero (t=0 min), a and b represent the surviving microbial populations at times t 1 and t 2, respectively (when used by a two point approach).. D value so obtained is called the decimal reduction time because at any particular temperature, D represents the heating time required for the microbial 35

52 population to reduce by one decimal reduction or 90%. Graphically, it is the time required for one log 10 reduction of the surviving population at a given temperature. The second parameter used to define the thermal inactivation kinetics of spores of C. difficile is the thermal resistance constant, also known as the temperature sensitivity indicator, denoted by z. The z value is the difference in the thermal treatment temperature that causes an increase or decrease in the D value by one decimal. The temperature sensitivity indicator was obtained as the negative reciprocal of the thermal resistance curve, which is a plot of log 10 D versus the heat treatment temperatures. Based on a two-point approach, the z value can be represented as shown in the equation 3.2. z = T 2 T 1 log 10 D 1 log 10 D where, D1 and D2 are D values at the temperatures T1 and T2, respectively. All experiments were performed in duplicates and data analysis was done in Microsoft Excel program. Student s t test was used to determine the significance of differences and 95% confidence intervals were determined for the D values. Further, the difference in the D values was defined in terms of the following two parameters: 1. Difference parameter (Δ) The numerical difference between the means of the two D values - D 1 and D 2. The units of the parameter Δ are min. It can be expressed by equation 3.3. Δ = D 1 D Percentage relative difference parameter (Φ) If the means of the two D values being compared are D 1 and D 2, then Φ is the percentage by which D 1 is greater than D 2. The parameter Φ has no units. It is given by equation 3.4. Φ = (1 D 2 D 1 ) These parameters were used to quantitatively express the difference between the D values. They were used to quantify the effect of fat content of ground beef on the D values and the variation of D values between the strains of C. difficile. 36

53 3.4 Results and Discussion Strain selection C. difficile ATCC and ATCC were selected for their high sporulation potential under experimental conditions and ability to produce toxins. These were characterized as Ribotype 014/Toxinotype 0 and were isolated from humans sources (Delmée et al., 1986, Rupnik et al., 1998). Since thermal studies required a minimum start up spore concentration 10 6 CFU/ml in the spore stock, this mandated the selection of C. difficile strains with high sporulation ability. However, high sporulation on a growth media in the laboratory does not necessarily mean high sporulation rates in the thermal treatment reservoirs. Moreover, hypervirulent strains of C. difficile have higher rates of sporulation, which allows greater dissemination of spores (Akerlund et al., 2008). Thus, testing for high sporulation potential and toxin producing ability would increase the clinical relevance of the study in addition to being a good selection criterion for selecting strains for research Thermal inactivation kinetics of spores of C. difficile ATCC and C. difficile ATCC Figure 3.3 (i) shows the survival curves [plot of the log 10 of the survivor fraction (N/N 0 ) versus the heating time (min)] of C. difficile ATCC spores in lean ground beef for thermal treatment temperatures ranging from 74 C to 82 C and the holding times (excluding come up times) ranging from 2 to 375 min. Figure 3.3 (ii) shows the survival curves of spores of C. difficile ATCC in regular ground beef for abovementioned temperature range, however, with holding times 1.5 to 240 min. Further, Figure 3.3 (iii) shows the survival curves of spores of C. difficile ATCC in lean ground beef, with thermal treatment temperatures ranging from 74 to 82 C and holding times ranging from 2 to 375 min and Figure 3.3 (iv) shows the survival cures of C. difficile ATCC in regular ground beef with abovementioned thermal treatment temperature range, however, the holding times ranged from 1.5 to 240 min. The "Trendline" option in the Microsoft Excel program was employed to fit the linear regression lines to the experimental data. From these curves, the slopes of the linear regression lines in addition to the R 2 values were obtained. Then, D values were calculated as the negative reciprocal of slope of the regression line. Table 3.1 and Table 3.2 give the D values calculated using the Bigelow first-order model, regression coefficients (R 2 ) and the 95% confidence interval for the D values of spores of both the strains of C. difficile in lean and regular ground beef. 37

54 To elaborate, for the survival curves of C. difficile ATCC in lean ground beef, the R 2 values ranged from 0.97 to 0.99 and in regular ground beef, the R 2 values ranged from 0.94 to Similarly, for the survival curves of C. difficile ATCC in lean ground beef, the R 2 values ranged from 0.97 to 0.99 and in regular ground beef, the R 2 values ranged from 0.95 to Although the first order model was found to be a good fit for the survivor curves of C. difficile ATCC and C. difficile ATCC in both types of ground beef, the average values of the regression coefficient were slightly higher when lean ground beef was used as the heating medium. The slope of the survival curves increased and the D values decreased with an increment in the thermal treatment temperatures. In other words, higher thermal treatment temperatures resulted in higher rates of destruction as characterized by lower D values. For spores of C. difficile ATCC in lean ground beef, D values ranged between 4.39 to 146 min. However, for the spores of the same strain in regular ground beef, D values ranged between 3.79 to 90.9 min. Additionally, for spores of C. difficile ATCC in lean ground beef, D values ranged between 4.15 to 116 min and in regular ground beef, mean D values ranged from 3.37 to 89.3 min. Figure 3.4 shows the thermal resistance curves of spores of C. difficile ATCC in lean and regular ground beef. Figure 3.4 (iii,iv) shows the thermal resistance curves of spores of C. difficile ATCC in lean and regular ground beef. Again, from the "Trendline" option of the Microsoft excel software was used to fit a linear regression line to the plot of logarithm of D values against thermal treatment temperatures. The z values were calculated as the negative reciprocal of the slope of the regression lines and are summarized in Table 3.3 gives the z values along with the regression coefficient (R 2 ) and the 95% confidence interval for z values of spores of both the strains of C. difficile in lean and regular ground beef as the heating medium. For spores of C. difficile ATCC in lean and regular ground beef, mean z values were found to be 5.17 and 5.58 C, respectively. For spores of C. difficile ATCC in lean and regular ground beef, mean z values were found to be nearly, 5.42 and 5.58 C, respectively. The regression coefficient for all the log-linear models for the variation of D (min) with heating temperature ( C), were greater than 0.99, indicating a good fit. Comparing the thermal resistance of spores of C. difficile ATCC and C. difficile ATCC to previous studies (Rodriguez-Palacios and Lejeune, 2011, Rodriguez- Palacios et al., 2010), the spores of C. difficile strains used in this study were more heat resistant. According to Rodriguez-Palacios et al. (2010), heating at 71 C for 2 h led to a 2-log 38

55 reduction in C. difficile spores in a buffer medium. Also, when these spores were reheated at 85 C for 10 min, 2 out of 20 strains survived. However, when spores were reheated at 85 C for 20 min, complete inactivation was observed. According to United States Food Safety and Inspection Service (1999), beef products must be cooked to 71.1 C, which instantly reduces Salmonella spp. by 6.5-log or 7.0- log. The mean D 60 C for another pathogen, E. coli O157:H7, was reported to be 1.9 min with a z value of 5.5 C. A heat treatment at 70 C for 2 min therefore should be sufficient for a 6-log reduction in E. coli O157:H7 (Juneja et al., 1997). According to our study, C. difficile spores need to be heated at 71 C for up to 5 to 9 h for a single decimal reduction depending on the fat content and strain of C. difficile. For 4 D reduction of spores of C. difficile ATCC in lean ground beef heating times will range from nearly 18 min at 82 C to 9.75 h at 74 C. For regular ground beef, for this reduction heating times ranged from nearly 16 min at 82 C to 6 h at 74 C. For spores of C. difficile ATCC 43597, for the 4 D values in lean ground beef were 17 min at 82 C to 7.75 h at 74 C; and in regular ground they ranged from 14 min at 82 C to 6 h at 74 C. The thermal resistance of spores in beef matrix have been reported to be higher in comparison to buffers (Zhu et al., 2008). The D values for the thermal inactivation of Listeria monocytogenes were found to be highest in ground beef, after comparing thermal resistances in phosphate buffer, meat slurry (20% ground beef, 80% water) and ground beef (80% lean) (Boyle et al., 1990). This may explain a much higher thermal resistance of spores of the two strains of C. difficile in comparison to thermal resistances in buffer medium. 39

56 (i) (ii) (iii) (iv) Figure 3.3 Survivor curves of C. difficile ATCC in lean and regular ground beef (i, ii) C. difficile ATCC in lean and regular ground beef (iii,iv), using the first order kinetics model. 40

57 Table 3.1 The D values (x ± s), R 2 and the 95% confidence interval for the D values for spores of C. difficile ATCC thermally treated in lean and regular ground beef. C. difficile ATCC Lean ground beef Regular ground beef Temperature ( C) 95 % confidence interval 95 % confidence interval D (min) R 2 Lower limit Upper limit D (min) R 2 Lower limit Upper limit (min) (min) (min) (min) ± ± ± ± ± ± ± ± ± ±

58 Table 3.2 The D values (x ± s), R 2 and the 95% confidence interval for the D values for spores of C. difficile ATCC thermally treated in lean and regular ground beef. C. difficile ATCC Lean ground beef Regular ground beef Temperature ( C) 95 % confidence interval 95 % confidence interval D (min) R 2 Lower limit Upper limit D (min) R 2 Lower limit Upper limit (min) (min) (min) (min) ± ± ± ± ± ± ± ± ± ±

59 (i) (ii) (iii) (iv) Figure 3.4 Thermal resistance curves of C. difficile ATCC in lean and regular ground beef (i, ii) C. difficile ATCC in lean and regular ground beef (iii,iv), using the first order kinetics model. 43

60 Table 3.3 The z values ( C) and 95% confidence interval of z ( C) for thermal inactivation of spores of C. difficile ATCC and C. difficile ATCC in lean and regular ground beef. 95% confidence interval Microorganism Type of ground beef z ( C) R 2 Lower limit (min) Upper limit (min) Lean 5.17 ± C. difficile ATCC Regular 5.58 ± Lean 5.42 ± C. difficile ATCC Regular 5.58 ±

61 Further, according to Rodriguez-Palacios and Lejeune (2011), heating at 85 C for 15 min leads to a 5-6 log reduction in spores of C. difficile. However, according to our study, although this temperature lies outside the experimental range, owing to a good fit and extensive use of first order model for predictions, the D 85 C was computed. It was estimated that heating at 85 C for 6 min could reduce spores of C. difficile by 6 logarithmic cycles. The difference in the results may also be due to the use of a cocktail of spores of different strains of C. difficile by Rodriguez-Palacios and Lejeune (2011). The D values for spores of C. sporogenes in extra lean ground beef were found to be 103.8, 41.9 and 16.2 min at 90, 95 and 100 C, respectively (Zhu et al., 2008). Since the fat content of extra lean ground beef is lower than ground beef in our study, the results cannot be compared directly. However, spores of C. difficile would be less heat resistant than that of C. sporogenes in the temperature range of 70 to 100 C. Further, for C. perfringens the D C was 6.6 min in beef gravy, however, at these temperatures spores of both the strains of C. difficile would be instantaneously killed. The D 90 C spores of Clostridium perfringens was 3 to 5 min, whereas for food poisoning strains of C. perfringens, the D 90 C was 15 to 145 min (Roberts, 1968). But, at this temperature, spores of C. difficile will range from 0.11 to 0.13 min. Although the two studies cannot be directly compared, spores of C. difficile ATCC and C. difficile ATCC may be less heat resistant than C. perfringens spores. According to Journal of Food Science Supplement (Kinetics of Microbial Inactivation for Alternative Processing Technologies, IFT 2000), the largest D 110 C of min has been reported for proteolytic C. botulinum type B spores. Most D 110 C values for spore formers range from 1 to 3 min at this temperature, whereas for C. difficile spores would not survive these temperatures. For spores of proteolytic C. botulinum, D 121 C in phosphate buffer is 0.20 min and z value is 10 C (Lund and Peck, 2000). The non-proteolytic strains of C. botulinum are much less resistant than the proteolytic strains (Lund and Peck, 2000). The spores of C. difficile ATCC and C. difficile ATCC are far less resistant than proteolytic C. botulinum. The closest comparison for non-proteolytic strain of C. botulinum was the D 82.2 C of 18 min for meat slurry. A general comparison with other spore formers of Clostridium genus such as C. perfringens, C. sporogenes, proteolytic C. botulinum suggests that the strains of C. difficile used in this study are not of much concern when cooking temperatures used are higher than 95 C. 45

62 In summary, spores of C. difficile were more resistant than the vegetative foodborne pathogens of concern such as E. coli O157:H7, Listeria monocytogenes and Salmonella spp. From the extrapolation of the first order model, C. difficile (ATCC and ATCC 43597) spores exhibited extremely low D values at temperatures of 90 to 121 C and are undoubtedly eliminated during a conventional thermal process based on commercial sterility of low acid products (12 D Clostridium botulinum cook); however, for even for a single decimal reduction of spores of C. difficile in ground beef at 71 C, the heating time required may vary from 286 to 516 min, depending upon the type of ground beef and the strain of C. difficile Effect of fat on the thermal inactivation kinetics of spores of C. difficile Table 3.4 gives the p values for the Student s t test used to test the significance of the effect of fat of ground beef on the D values and the variation of D values between the two strains of C. difficile. Tables 3.5 and 3.6 give the difference parameter ( ) in min and the percentage relative difference parameter (Φ) for the effect of fat content of ground beef on the D values of C. difficile spores. An increase in fat content of ground beef from 15 to 30% decreased the D values significantly (p < 0.05) at all temperatures tested, 74, 76 and 80 C. However, at 78 and 82 C, although an increase in fat content decreased the mean of D values by 19.5 and 13.6 %, respectively, this decrease was not significant (p 0.05). For C. difficile ATCC 43597, an increase in the fat content of ground beef from 15 to 30%, decreased the D value significantly (p < 0.05) at 74 and 78 C, whereas at 76, 80 and 82 C, although the D values decreased by 22.7, 12.7 and 18.8 %, respectively with an increment in fat content of ground beef, this decrease was not statistically significant (p 0.05). The difference in the D values at the two fat levels of ground beef for both the strains was not significant at some temperature. du Prel et al. (2009) explained that statistically nonsignificant difference does not mean that there is no difference; and in this case it does not mean that fat has no effect on the D values at these temperatures. The D values are indicators of the rate of thermal destruction and are obtained from the slopes of survivor curves. Even small differences can have a significant effect on the resulting cumulative destruction. Hence it is advisable not to ignore the variations. Overall, an increase in fat content of ground beef decreased the mean D values of the spores of both the strains of C. difficile, suggesting a 46

63 sensitizing effect, which was statistically significant at 74, 76 and 80 C for C. difficile ATCC 17857, and 74 and 78 C for C. difficile ATCC Table 3.7 gives the p values obtained after employing Student s t test, which was used to test the significance of the difference between z values for lean and regular ground beef, for the two strains of C. difficile. Also, Figure 3.5 presents the z values for C. difficile ATCC and C. difficile ATCC in lean and regular ground beef as a histogram. For spores of C. difficile ATCC 17857, the difference between the z values for lean ground beef and regular ground beef was significant (p<0.05), whereas for spores of C. difficile ATCC 43597, the difference between the z values for lean and regular ground beef was not significant (p>0.05). Thus, an increment in fat content of ground beef from 15 to 30 % significantly increased the z value for C. difficile ATCC 17857, but not for C. difficile ATCC

64 Table 3.4 P values for the Student's t test to determine statistical significance of the difference p value Temperature ( C) 1 versus 2 3 versus = D values of C. difficile ATCC in lean ground beef 2 = D value of C. difficile ATCC in regular ground beef 3 = D values of C. difficile ATCC in lean ground beef 4 = D values of C. difficile ATCC in regular ground beef Table 3.5 The difference parameter (Δ) and the percentage relative difference parameter (Φ) for the effect of fat on spores of C. difficile ATCC C. difficile ATCC Temperature D lean (min) D regular (min) Δ (min) Φ

65 Table 3.6 The difference parameter (Δ) and the percentage relative difference parameter (Φ) for the effect of fat on spores of C. difficile ATCC C. difficile ATCC Temperature D lean (min) D regular (min) Δ (min) Φ Table 3.7 P values for the Student's t test to determine statistical significance of the difference between z values for lean and regular ground beef as the heating medium P value 1 versus versus = z value for C. difficile ATCC in lean ground beef 2 = z value for C. difficile ATCC in regular ground beef 3 = z value for C. difficile ATCC in lean ground beef 4 = z value for C. difficile ATCC in regular ground beef 49

66 Figure 3.5 graphical comparisons of z values for C. difficile ATCC in lean (1) and regular ground beef (2) and z values for C. difficile ATCC in lean (3) and regular ground beef (4) 50