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1 Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere without the permission of the Author. i

2 Probability-of-growth modelling to optimize the use of hurdle technology to achieve microbiological stability of high moisture processed cheese A thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North, New Zealand Elham Khanipour 2014 ii

3 Abstract This thesis reports a study of the application of hurdle technology to high moisture, low acid ambient shelf-stable hot packed processed cheese analogue (PCA). Hurdle technology makes use of a combination of mild stress factors. A combination of these stress factors can be more effective in inhibiting or inactivating the growth of microorganism than individual stress factors. The current study focused on the application of hurdle technology to inhibit the growth of the food pathogen, Clostridium botulinum (C. botulinum). This micro-organism poses a hazard for consumers and is capable of growth in low acid food (ph> 4.5). As there are difficulties in working with C. botulinum in laboratory trails, Clostridium sporogenes (C. sporogenes) was used as an analogue of C. botulinum. C. sporogenes is very similar to C. botulinum in growth characteristics but is not dangerous. The effectiveness of selected preservatives on the growth of the target micro-organism was expressed as the probability of growth and was modelled as function of the concentrations of the selected preservatives in nutrient broth. Nutrient broth was initially used as it can be easily and accurately adjusted and controlled in terms of composition, and allows more rapid growth than is observed in PCA. A combination of salt (sodium chloride), sorbic acid (in the form of potassium sorbate), nisin and lysozyme was selected as stress factor. The inhibitory effect of these preservatives was then observed in the high-moisture nutrient broth at ph 7 (the optimum condition for spore of C. sporogenes to germinate) at 37ºC for eight weeks. It was found that lysozyme did not have a significant inhibitory effect on C. sporogenes in combination with salt, potassium sorbate and nisin. Therefore, the inhibitory effect of salt, sorbic acid and nisin at two different phs (5.5 and 7) were subsequently evaluated in the nutrient broth at 37ºC for eight weeks. The probability of growth of C. sporogenes was modelled as a function of the concentrations of these selected preservatives at each ph. The results demonstrated that a combination of salt, nisin and potassium sorbate at relatively low concentrations can be used to inhibit growth. The inhibitory effects of the preservatives were ph dependent and their inhibitory effect is higher at ph 5.5. The developed models were validated iii

4 using a fresh data set. Finally, the applicability of the developed model was checked in high moisture PCA. The results showed that the developed broth model underestimated the probability of growth in the PCA. Therefore, a specific probability of growth model was developed for the PCA using the PCA instead of nutrient broth as the growth medium. This model accurately predicted the probability of growth of C. sporogenes in the PCA for given combinations of preservative concentrations. The model developed for PCA allows the relative levels of preservatives to be easily quantified without the need for time consuming and expensive experimental work. The model would have limitations in the case of strongly varying formulations, since minor changes in processed cheese formulation or its production, could significantly alter its ability to support toxin production. Therefore, the model is applicable only to PCAs that have formulations similar to that used in this study. The general approach described in this thesis could be applied in the development of other high moisture, low acid foods. iv

5 Acknowledgments This research has been possible with the collaboration of amazing people I have met along the way. After four years of PhD adventure, I am glad to have the opportunity to express my gratitude to all of you. My deepest sense of gratitude is to my main supervisors, Professor Steve Flint and Dr. Owen McCarthy, for all their experience and knowledge they have shared with me throughout the entire period of my doctoral study. I thank them for their conscientious guidance, advice and their intensive review of different documents, including this thesis, during my PhD. It was a rewarding experience to work under their supervision. Their comments were always constructive and positive, guiding me in completing this thesis with a thorough understanding. I would like to thank my co-supervisors Professor Matt Golding and Dr. Jon Palmer for all their support and suggestions during my doctoral study. I am deeply grateful to the Institute of Food Nutrition and Human Health (IFNHH) and Dr. Owen McCarthy for supporting financially this research, some of my training and my PhD scholarship. I am also thankful to Professor Mark Tamplin for giving me the opportunity to spend some time at the Food Safety Centre at the University of Tasmania. Special thanks to Assoc. Professor Tom Ross for his valuable scientific input and advice during the paper publications. I also acknowledge Emeritus Professor David Ratkowsky and Professor Tom McMeekin for their insightful statistical discussions and wise suggestions during my time at University of Tasmania. Many thanks also to Professor Hugh Morton, Massey University and Duncan Hedderley, Plant and Food Research for helping me with experimental design and statistical analysis. I am indeed thankful to Ann-Marie Jackson for being an excellent lab manager, providing training, ordering chemicals at the right time and for technical help. I am sincerely grateful to Julia Good for helping me anytime in Micro-suite Lab. I am thankful to all the staff in the IFNHH for their help during this period of my study. Thanks to all my office mates especially Sandra and Yap Sia for their support and company. I express my deep sense of gratitude to Faegheh, Lara, Christine, Heather, v

6 Ximenita, and Fleur for their encouragement and inspiration, always helping me to keep thinking positive even in a discouraging phase of my study. Heartfelt thanks to Anna Wildey for her enormous support and proof assistance. I am extremely grateful to the pillar of my life, my parents, for their constant motivation and endless guidance. I am deeply thankful to my sister, Elahe, for her invaluable emotional support and encouragement throughout my PhD study. The last but not the least, eternal gratitude goes to my husband, Sina, as the thesis would not have been completed without his constant support and his sacrifice. vi

7 Table of contents Abstract... v Acknowledgments... vii List of figures... xv List of tables... xvii List of publications... xix Chapter One. Introduction Introduction Importance of the study Research hypotheses and aims Thesis outline... 4 Chapter Two. Literature Review Introduction Principles of hurdle technology Effect of interactions hurdles Production of hot packed processed cheese Ingredients that affect processed cheese safety Salt ph and weak acids Nisin Emulsifiers Fat Flavour enhancers and other food additives Effect of Maillard reaction products Combined effects of cheese ingredients Microbiological considerations Microbial challenge testing Predictive microbiology Probability models Techniques used for establishing probability models vii

8 Factors influencing probability of growth modelling Summary Chapter Three. Materials and methods Introduction Inoculum preparation Spore staining Spore harvesting Spore enumeration Preparation and inoculation of growth media Nutrient broth Processed cheese analogue Incubation of inoculated samples Growth evaluation Nutrient broth Processed cheese analogue Other analyses Moisture content Water activity ph Experimental design and statistical analyses Chapter Four. Exploration of the relative preservative effects of salt, sorbic acid, nisin and lysozyme on the growth of Clostridium sporogenes in nutrient broth Introduction Materials and methods Experimental design Growth medium preparation and inoculation Growth evaluation Model development Model validation Results Model validation Discussion Conclusion viii

9 Chapter Five. Modelling the combined effects of salt, sorbic acid and nisin on the probability of growth of Clostridium sporogenes in nutrient broth Introduction Materials and methods Experimental design Preparation and inoculation of nutrient broth Growth evaluation Development of probability of growth models Assessment of model performance Model validation Checking growth at room temperature (25ºC) Results Development of probability of growth models Model A1 (ph 5.5) and Model A2 (ph 7): sorbic acid concentration expressed as potassium sorbate concentration (% w/w) Model B1 (ph 5.5) and Model B2 (ph 7): sorbic acid concentration expressed as undissociated sorbic acid concentration (mol L-1) Model C ( data for ph 5.5 and ph 7 continued): sorbic acid concentration expressed as the concentration of undissociated sorbic acid (mol L-1) Validation of models A1 and A Checking probability of growth at room temperature (25ºC) Exploration of the relative preservative effects of salt, nisin and sorbic acid using models A1 and A The inhibitory effect of salt The inhibitory effect of sorbic acid The inhibitory effect of nisin Discussion Conclusion Chapter Six. Applicability in processed cheese analogue of the developed broth probability of growth model for Clostridium sporogenes Introduction Materials and methods Experimental approach Preparation of samples ix

10 Measurements made during storage Results Discussion Conclusion Chapter Seven. Modelling the combined effects of salt, sorbic acid and nisin on the probability of growth of Clostridium sporogenes in high moisture processed cheese analogue Introduction Materials and methods Experimental design Preparation of PCA samples Measurements made during sample storage The development of the probability of growth model Assessment of model performance Validation of the models Results Development of the probability of growth models Model A: preservative concentrations based on total mass of PCA Continued on next page Model B: preservatives concentrations based on moisture content of the PCA Model validation Exploration of the relative preservative effects of salt, nisin and sorbic acid using Model A The inhibitory effects of potassium sorbate and nisin as influenced by salt concentrtion The inhibitory effects of salt and nisin as influenced by sorbic acid (potassium sorabte) The inhibitory effects of potassium sorbate and salt as influenced by the nisin concentration Discussion Conclusion Chapter Eight. General discussion and conclusion Introduction Exploration of the relative preservative effects of salt, sorbic acid, nisin and lysozyme on the growth of C. sporogenes in nutrient broth x

11 8.3. Development of probability of growth models General recommendations for future study Bibliography Appendix xi

12 List of figures Figure 2.1. Flow diagram showing the stages of manufacturing processed cheese... 9 Figure 3.1. Schematic of the process used to produce spores Figure 3.2. Images of stained spores at the first (a), second (b) and third (c) week of incubation at room temperature Figure 3.3. Sterile filtration for separation of spores from cooked meat media particles Figure 3.4.Washing steps to remove vegetative cell debris from spores. (a) after first washing step; (b) before last washing step (c) after last washing step Figure 3.5. Vorwerk Thermomix TM 21 blender for preparing PCA Figure 3.6. Preparation of PCA Figure 4.1. Probability of growth as a function of salt and potassium sorbate concentrations when nisin and lysozyme were at their median concentrations of 250 ppm Figure 4.2. Probability of growth as a function of potassium sorbate and nisin concentrations when salt and lysozyme were at their median concentrations of 4% and 250 ppm, respectively Figure 4.3. Probability of growth as a function of salt and lysozyme concentrations when nisin and potassium sorbate were at their median concentrations of 250 ppm and 2.25%, respectively Figure 5.1. Comparisons, highlighting the effect of salt concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by Model A 2 (lines) at ph 7. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 5.2. Comparison, highlighting the effect of salt concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by Model A 1 (lines) at ph 5.5. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 5.3. Comparison, highlighting the effect of potassium sorbate concentration, between the observed probability of growth (coloured symbols) and probability of xii

13 growth predicted by Model A 2 (lines) at ph 7. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 5.4. Comparison, highlighting the effect of potassium sorbate concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by Model A 1 (lines) at ph 5.5. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 5.5. Comparison, highlighting the effect of nisin concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by Model A 2 (lines) at ph 7. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 5.6. Comparison, highlighting the effect of nisin concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by Model A 1 (lines) at ph 5.5. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 7.1. Comparison, highlighting the effect of salt concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by model A. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 7.2. Comparison, highlighting the effect of potassium sorbate concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by model A. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = Figure 7.3. Comparison, highlighting the effect of nisin concentration, between the observed probability of growth (coloured symbols) and probability of growth predicted by model A. Red triangle: observed p = 0; blue square: 0.1 observed p 0.9; green circle: observed p = 1. Dashed line: predicted p = 0.1; dotted line: predicted p = 0.5; solid line: predicted p = xiii

14 List of tables Table 2.1. A range of ingredients used to formulate processed cheese analogue (Data from (O Riordan et al., 2011) Table 3.1. Composition of PCA Table 4.1. The factors and the levels in the central composite design Table 4.2. Combination of preservatives used for the model validation Table 4.3. Treatments (preservative combinations) and observed probabilities of growth Table 4.4. Analysis of variance for the probability of growth model showing the effects of independent variables as a linear, quadratic and interaction terms on the response variables Table 4.5. Coefficients and p-value given by regression of the data in Table Table 4.6. Validation of model: comparison between observed and predicted probabilitis of growth Table 5.1. Experimental treatments (combinations of preservative concentrations) Table 5.2. Combinations of preservatives used in testing model predictions at ph 5.5 and Table 5.3. Combinations of different preservatives at both ph 5.5 and ph 7 that showed no growth at 37ºC, and which were tested again at 25ºC Table 5.4. Observed probabilities of growth of C. sporogenes in broth at phs 5.5 and Table 5.5. Coefficients of, and statistics for, models A 1 and A 2, for which sorbic acid concentration is expressed as potassium sorbate concentration (% w/w) Table 5.6. Coefficients of, and statistics for, models B 1 and B 2, for which sorbic acid concentration is expressed as undissociated sorbic acid concentration (mol L -1 ) Table 5.7. Coefficients of, and statistics for, Model C, for which sorbic acid concentration is expressed as mol L -1 of undissociated sorbic acid Table 5.8. Models A 1 and A 2 validation: observed and predicted probabilities at ph 5.5 and Table 5.9. Test of models A 1 and A 2 using literature data Table 6.1. Experimental preservative treatments Table 6.2. Observed and predicted probabilities of growth xiv

15 Table 6.3. Moisture content and a w of the PCA samples at time zero and at the end of eight weeks Table 7.1. Experimental treatments Table 7.2. Combinations of preservative used for validation of the models Table 7.3. The observed probability of growth of C. sporogenes with the moisture content and a w of the PCA samples at time zero and the end of the eight week storage period, and the average moisture content. Preservative concentrations are expressed on both a total mass basis and a moisture content basis Table 7.4. Model A: coefficients and relevant statistics Table 7.5. Model B: coefficient and relative statistics Table 7.6. Validation of Model A: comparison between observed and predicted probabilities of growth Table 7.7. Validation of Model B: comparison between observed and predicted probabilities of growth xv

16 List of publications Peer-reviewed papers Khanipour, E., McCarthy, O., Flint, S., Golding, M., Palmer, J. and Tamplin, M. Evaluation of the effects of salt, potassium sorbate, nisin and lysozyme on the probability of growth of Clostridium sporogenes, International Journal of Food Science and Technology, 2014, 49(6), Khanipour, E., McCarthy, O., Flint, S., Golding, M., Palmer, J. Ratkowsky, D. A., Ross, T. and Tamplin, M. (2014). Modelling the combined effects of salt, sorbic acid and nisin on the probability of growth of Clostridium sporogenes in nutrient broth. Submitted for publication in International Journal of Microbiology. Khanipour, E., McCarthy, O., Flint, S., Golding, M. & Palmer, J. and Tamplin, M. (2014). The growth probability model of Clostridium sporogenes as a function of salt, sorbic acid and nisin in processed cheese (In preparation). Conference presentations Khanipour, E., McCarthy, O., Flint, S., Golding, M. & Palmer, J. Production of a cocktail of spores of Clostridium sporogenes for growth trials in new generation foods. New Zealand Microbiology Society (NZMS) Conference, Convention Center, Palmerston North, November 2011 (Poster presentation). Khanipour, E., McCarthy, O., Flint, S., Golding, M. & Palmer, J. The combined effect of salt, nisin, sorbate and lysozyme on the survival of Clostridium sporogenes in broth. New Zealand Microbiology Society (NZMS) Conference, Otago University, Dunedin, November 2012 (Poster presentation). Khanipour, E., McCarthy, O., Flint, S., Golding, M. & Palmer, J. The combined effects of salt, nisin and potassium sorbate at different phs on the survival of Clostridium sporogenes in broth. New Zealand Institute of Food Science and xvi

17 Technology (NZIFST) Conference, Hawkes Bay Opera House, Hastings 2-4 July 2013 (Poster presentation). Khanipour, E., McCarthy, O., Flint, S., Golding, M. & Palmer, J. Modelling the growth/no growth of Clostridium sporogenes spores in broth as a function of salt, nisin and potassium sorbate concentrations at two phs. International Conference on Predictive Modeling in Foods (ICPM) Conference, the Institute Pasteur, Paris, September 2013 (Poster presentation). Khanipour, E., Flint, S., McCarthy, O., Golding, M. & Palmer, J. How does the combination of common preservatives affect the survival of Clostridium sporogenes. Second Food Symposium. Research Presentation, Institute of Food, Nutrition and Human Health, Massey University, Palmerston North, 15 November 2013 (oral presentation). Khanipour, E., Flint, S., McCarthy, O., Golding, M. & Palmer, J. Probability-ofgrowth modelling to optimize the use of hurdle technology to achieve microbiological stability of high moisture food product. University of Tasmania's Postgraduate Research meeting, 25 November 2013, Sandy Bay Campus, Hobart, Tasmania (oral presentation). Khanipour, E., Flint, S., McCarthy, O., Golding, M. & Palmer, J. Evaluation of the effect of salt, potassium sorbate and nisin on the probability of growth of Clostridium sporogenes. University of Tasmania's Food Safety Centre Forum, 2-3 December 2013, Sandy Bay Campus, Hobart, Tasmania (oral presentation). xvii

18 All models are wrong but some are useful George E. P. Box 1979 xviii