Fares, Almabrouk (2007) Quantitative risk assessment model of human salmonellosis linked to the consumption of Camembert cheese made from raw milk. PhD thesis Epidémiologie, ENVA,UEAR, Unité d’epidémiologie et d’analyse des risques, AgroParistech 2007AGPT0051 p.250.

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Abstract

Salmonellae are one of the most important causes of foodborne illness associated with

raw dairy products. The assessment of the real risk associated with the consumption of these

products is needed and the most appropriate method to achieve this goal is the risk analysis

process which links pathogens in food to the public health problem. The main aim of this

thesis is to quantitatively assess the risk of human salmonellosis linked to the consumption of

Camembert cheese made from raw milk. A data gap that is routinely identified in risk

assessment is the lack of quantitative data on pathogens contaminated food. Therefore, as a

first objective of this thesis, we developed a rapid, sensitive and reliable method for the

quantification of Salmonella in artificially contaminated milk samples. The method combined

the principles of most-probable-number (MPN) method with a real-time PCR assay. With this

developed assay (MPN-real-time PCR) low levels of Salmonella (1-5 CFU/mL) in milk could

be enumerated after 8 h of non-selective enrichment in buffered peptone water. All estimated

MPN counts corresponded well to the estimated contamination level of Salmonella inoculated

into milk samples. In order to evaluate the utility of this developed quantification assay, our

second objective was to apply it to naturally contaminated bulk tank milk samples collected

from dairy farms located in western France. Eight (2.68%) of 299 bulk tank milk samples

were found positive, with estimated MPN values ranging from 3.7 to 79.2 MPN/mL of milk.

Despite the PCR inhibitors that were apparently present in some raw bulk tank milk samples,

the application of the MPN-real-time PCR assay for quantifying Salmonella in raw milk

proved to be rapid, easy-to-perform and highly sensitive. In the assessment of potential risks

associated with Salmonella in raw milk and raw milk products it was necessary to examine

the ability of Salmonella to grow in milk. Therefore, we presented in this thesis as a third

v

objective, primary and secondary models describing mathematically the growth of two

Salmonella strains (S. Typhimurium and S. Montevideo) in milk under constant temperatures

during different incubation periods. The primary logistic-with-delay model was used to

describe Salmonella growth as a function of time. The specific growth rates of S.

Typhimurium and S. Montevideo varied according to serotype and temperature. The

maximum growth rates were then modeled as function of temperature using the secondary

cardinal Rosso model. The reported cardinal estimates obtained with S. Typhimurium and S.

Montevideo were: Tmin 3.02, 3.40; Topt 38.44, 38.55 and Tmax 44.51, 46.97°C, respectively. At

the optimum growth temperature (Topt) the maximum growth rates were 1.36 and 1.39 log10

CFU/h-1 for S. Typhimurium and S. Montevideo respectively. Both the primary and secondary

models fitted growth data well with a high-pseudo R2 (0.97-99). Finally, a quantitative risk

assessment of human salmonellosis linked to the consumption of Camembert cheese made

from raw milk is presented. Different distributions were assumed for the parameters of the

model and a Monte Carlo simulation was used to model the process and to quantify the risk

associated with the consumption of 25 g serving of cheese. The 99th percentile of Salmonella

cell numbers in servings of 25 g of cheese was 5 cells at the time of consumption,

corresponding to 0.2 cells of Salmonella per gram. The risk of salmonellosis per 25 g serving

varied from 0 to 1.2 × 10-7 with a median of 7.4 × 10-8. For 100 million servings of 25g, the

expected number of cases of salmonellosis predicted by the model is in average of 7.4. When

the prevalence was reduced in the model by a factor of 10, the number of cases per 100

million servings was reduced to less than 1 case. Despite the limitations and the data gap, we

demonstrated the benefit of risk assessment not only as a risk evaluation tool but also as a

helping device in the decision-making and the risk management.

Table of content

PUBLICATION RIGHTS - iii

(ABSTRACT) - iv

DEDICATION - ix

ACKNOWLEDGMENTS - x

TABLE OF CONTENTS - xi

LIST OF TABLES - xiv

LIST OF FIGURES - xv

LIST OF ABBREVIATIONS - xvii

Chapter 1: Introduction - 1

Background / Problem Statement - 2

Thesis objectives - 14

Personal objectives - 14

Research objectives - 14

Thesis outline - 15

References - 17

Chapter 2: Literature Review - 22

Salmonella general characteristics - 23

Detection, isolation, and quantification of Salmonella in food - 24

Salmonella surveillance and monitoring programs - 33

French Surveillance systems - 34

Selected international surveillance systems in public health and food safety programs: - 37

Implication of milk and milk products in Salmonella outbreaks - 38

Growth of Salmonella in dairy products - 46

Risk assessment and Salmonella - 51

References - 57

Chapter 3: Combination of Most-Probable-Number Method with LightCycler real-time PCR assay

(MPN-real-time PCR) for Rapid Quantification of Artificially Contaminated Salmonella in Milk Samples

- 76

Abstract - 77

1. Introduction - 79

2. Materials and methods - 81

2.2. Specificity of the real-time PCR assay - 81

2.4. Artificial contamination of milk - 82

2.5. DNA extraction procedures - 83

2.6. SYBR Green real- time PCR assay - 84

3. Results - 85

3.1. Optimization of real-time PCR assay - 85

xii

3.2. Specificity of real-time PCR primers - 85

3.3. Detection limits in pure cultures - 86

3.4. Detection of Salmonella from artificially contaminated milk samples - 86

3.5. Confirmation of real-time PCR products by DNA melting temperature analysis - 87

3.6. Enumeration of Salmonella in artificially contaminated milk samples - 87

4. Discussion - 88

References - 93

Chaptre 4: Application of MPN-real-time PCR Assay for Quantification of Salmonella in Bulk Tank Milk

samples - 107

Abstract - 108

1. Introduction - 110

2. Materials and methods - 112

2.1. Dairy herds - 112

2.2. Detection of Salmonella by LightCycler real-time PCR - 113

2.3. Enumeration of Salmonella by MPN-real-time PCR - 113

2.4. DNA extraction - 114

2.5. LightCycler real-time PCR assay - 114

2.6. Isolation of positive colonies from raw milk samples - 115

3. Results - 116

4. Discussion - 117

References - 121

Chapter 5: Growth of Salmonella in Artificially Contaminated Milk Samples Stored at Different Times

and Temperatures - 126

Abstract - 127

1. Introduction - 129

2. Materials and methods - 130

2.1. Bacterial Strains - 130

2.2. Inoculum preparation - 130

2.3. Sample preparation, and inoculation - 130

2.4. Incubation temperatures, sampling time and bacterial enumeration - 131

2.5. Primary Model - 132

2.6. Secondary Model - 132

2.7. Primary Model Fitting - 133

2.8. Secondary Model Fitting - 133

3. Results and discussion - 134

3.1. Primary modelling curve fitting - 134

3.2. Secondary Model (Cardinal Temperatures) - 136

References - 147

Chapter 6: Quantitative risk assessment of human salmonellosis linked to the consumption of Camembert

cheese made from raw milk - 150

Abstract - 151

1. Introduction - 153

2. Materials and methods - 154

2.1 Hazard identification - 154

2.2. Exposure assessment - 155

2.2.1. Collection of data on raw milk contaminated by Salmonella - 155

2.3. Cheese processing - 158

2.4. Growth model - 159

2.4.1. Growth of Salmonella during cheese ripening to consumption - 160

xiii

2.5. Number of Salmonella in cheese - 162

2.6. Control programs of Salmonella at farms - 162

2.7. Dose-response model - 163

2.7.1. Probability of illness - 164

2.8. Risk characterization - 164

3. Results - 167

3.1. Milk contamination - 167

3.2. Cheese contamination - 168

3.3. Risk of salmonellosis - 170

4. Discussion - 171

References - 175

Chapter 8: General Discussion and Conclusion - 179

Detection and quantification of Salmonella in milk - 180

Predictive modelling of Salmonella growth in milk - 185

Risk assessment model - 187

References - 189

Appendix - 227

Vita - 250

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