Risk Assessment Methods for Biological and Chemical Hazards in Food

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Cover Half Title Title Page Copyright Page Table of Contents Preface Editor Contributors Section I: General Aspects Chapter 1 Food Risk Assessment Framework: Foundations and Concepts 1.1 Introduction 1.2 Hazard versus risk 1.3 Risk Assessment and Its Role in Risk Analysis 1.4 Risk Assessment Framework 1.4.1 Microbial Risk Assessment Concepts 1.4.2 Chemical Risk Assessment Concepts 1.5 Deterministic versus Stochastic Risk Assessment 1.6 Uncertainty and Variability in Risk Assessments 1.7 Limitations and Challenges of Risk Assessment in Foods 1.8 Current Developments and Future Perspectives References Chapter 2 Risk Ranking: Moving towards a Risk-Based Inspection and Surveillance System 2.1 The Need for a Risk-Based Food Inspection and Surveillance System 2.2 Identifying Food Safety Risks: Risk Ranking 2.3 Risk Ranking Framework 2.3.1 Defining the Scope 2.3.2 Screening 2.3.3 Approach 2.3.4 Risk Variables and Metrics 2.3.5 Risk Ranking Model 2.3.6 Data Collection and Evaluation 2.3.7 Risk Ranking Results 2.4 Risk Ranking Tools 2.4.1 Decision Trees 2.4.2 Decision/Risk Matrices 2.4.3 Spreadsheet Calculators 2.4.4 Scoring Systems and Multicriteria Decision Analysis 2.4.5 Web-Based Tools 2.5 CASE STUDY: Development of a Risk-Based Inspection System 2.6 Conclusions References Chapter 3 Risk Metrics: Quantifying the Impact of Adverse Health Effects 3.1 Introduction: From Reactive to Risk-Based Food Safety Systems 3.2 Burden of Illness 3.2.1 Bottom-Up versus Top-Down Approaches 3.2.2 Outcome Trees 3.2.3 From Burden of Illness to Burden of Disease 3.3 Health Impact Metrics 3.4 Economic Impact Metrics 3.4.1 Costs Associated with Foodborne Disease 3.4.2 Methods Used to Estimate Costs 3.4.2.1 Cost-of-Illness 3.4.2.2 Willingness to Pay 3.5 Risk Ranking 3.6 Risk-Benefit Assessment 3.7 Conclusions Acronyms References Chapter 4 Risk–Benefit Assessment of Foods 4.1 Introduction 4.1.1 The Need for Risk–Benefit Assessment 4.1.2 The Risk–Benefit Assessment Approach and the Role of the Risk–Benefit Question 4.2 Risk–Benefit Assessment in Food Safety and Nutrition 4.3 Qualitative and Quantitative Risk–Benefit Assessment Approaches 4.3.1 Identification of the Overall Health Impact: The Tiered Approach 4.3.2 Quantification of the Overall Health Impact 4.4 Risk–Benefit Assessment at Different Levels of Aggregation 4.4.1 Risk–Benefit Assessment for Food Components, Foods and Diets 4.4.2 Food Component Risk–Benefit Assessment 4.4.3 Food Risk–Benefit Assessment 4.4.4 Diet Risk–Benefit Assessment 4.5 Future Perspectives and Challenges of Risk–Benefit Assessment of Foods Acknowledgement References Chapter 5 Application of Quantitative Risk Assessment Methods for Food Quality 5.1 Introduction 5.2 Concepts of Risk Assessment for Microbial Spoilage 5.3 Assessment of Microbiological Spoilage of Food and Beverages: Chemical and Microbiological Changes 5.4 Stages of Risk Assessment for Microbial Spoilage 5.5 Modeling Approaches for Spoilage Risk Assessment 5.5.1 Qualitative and Quantitative Exposure Assessment 5.5.1.1 Model Development 5.5.2 Model Types and Availability 5.5.3 Application of Predictive Microbiology within Exposure Assessment 5.6 Variability, Uncertainty, and Sensitivity for Spoilage Risk Assessment 5.7 Examples of Risk Assessment for Food Spoilage 5.8 Concluding Remarks References Chapter 6 Estimating Concentration Distributions: The Effect of Measurement Limits with Small Data 6.1 Introduction: Modeling the Observation 6.2 Parameter Estimation from Censored Data of Known Positive Concentrations 6.2.1 Bayesian Computation 6.3 Bayesian Estimation for True Zeros with Reported Microbial Colony Forming Units per Gram 6.4 Bayesian Estimation for True Zeros with Reported Microbial Plate Counts 6.5 Concluding Remarks Appendix 6: Visual Exploring Using R and OpenBUGS A.6.1 Censored Data and Likelihood Contour Plots A.6.2 Bayesian Model with Censored Data A.6.3 Bayesian Model with Censored Data and True Zeros A.6.4 Bayesian Model with Plate Count Data References Chapter 7 Understanding Uncertainty and Variability in Risk Assessment 7.1 Introduction 7.2 Some Classical Definitions 7.2.1 Variability 7.2.2 Uncertainty 7.3 Understanding Uncertainty and Variability 7.3.1 A Proposal to Better Understand and Consider Variability and Uncertainty 7.3.2 Why Is There a Lack of Understanding? 7.3.3 Things Get Worse: When Uncertainty and Variability Are Exchangeable 7.4 Variability and Uncertainty in Practice 7.4.1 Conduct an Uncertainty Analysis 7.4.2 Considering Uncertainty and Variability Separately 7.4.2.1 Characterize the Variability/Uncertainty from the Data 7.4.2.2 Use Monte-Carlo Simulations to Integrate Variability 7.4.2.3 Characterize the Uncertainty 7.4.3 Prioritize the Different Sources of Uncertainty 7.4.4 Communicating Uncertainties and Their Impact on the Outcome 7.5 Discussion References Chapter 8 Application of Sensitivity Analysis Methods in Quantitative Risk Assessment 8.1 Introduction 8.2 Description of Methods and Approaches for Sensitivity Analysis 8.2.1 Mathematical Methods 8.2.1.1 Nominal Range Sensitivity Analysis (NRSA) 8.2.1.2 Break-Even Analysis (BEA) 8.2.2 Statistical Methods 8.2.2.1 Model Independent Methods 8.2.2.2 Model Dependent Methods 8.2.3 Graphical Methods References Section II: Microbial Risk Assessment Chapter 9 Quantitative Methods for Microbial Risk Assessment in Foods 9.1 Introduction 9.2 Quantitative Resources for Risk Assessment 9.2.1 Human Data 9.2.1.1 Data on Human Cases 9.2.1.2 Data on Foodborne Outbreaks 9.2.2 Animal/Food Data 9.2.2.1 EFSA Monitoring Data 9.2.2.2 EU-Wide Baseline Survey Data 9.2.2.3 EU Rapid Alert System for Food and Feed Data 9.2.2.4 Other Resources, e.g. Data/Models from Scientific Literature 9.2.3 Consumption Data 9.2.3.1 EFSA Consumption Data 9.2.3.2 Other Consumption Data 9.2.3.3 Consumer Behaviour 9.2.4 Dose–Response Data 9.3 Risk Modelling Process and Model Integration 9.3.1 From Data to Risk: Data Treatment 9.3.2 Deterministic Models vs. Stochastic Models 9.3.3 Prevalence and Concentration 9.3.4 Models and Modelling Approaches 9.3.5 Model Integration: Population Risk versus Individual Risk 9.3.6 Modelling and Simulation Tools 9.4 Risk Assessment Output Interpretation: Importance of Uncertainty Analysis 9.4.1 Elements in Uncertainty Analysis 9.4.2 Interpretation of Uncertainty Analysis in MRA 9.5 Knowledge Exchange to Improve Microbial Risk Assessment 9.5.1 Current Limitations of Knowledge Exchange 9.5.2 Current Status of Knowledge Exchange 9.5.3 Novel Initiatives to Improve Knowledge Exchange Disclaimer Notes References Chapter 10 Hazard Identification: Microbial Risks along the Food Chain 10.1 Overview and Approaches to Hazard Identification 10.1.1 Hazard Identification Incorporating a Quantitative Approach: Listeria monocytogenes in Ready-to-Eat Foods 10.1.2 Qualitative Approach to Hazard Identification: Microbial Risks for Primary Producers of Leafy Vegetables 10.2 Microorganisms in Foods 10.2.1 General Pathogen Characteristics 10.2.2 Sources of Microorganisms 10.2.3 Exposure Routes to Microorganisms 10.2.4 Survival and Growth of Microorganisms in Foods 10.2.5 Food Production and Beyond 10.3 Human Adverse Health Outcomes 10.4 The Population Burden of Disease 10.4.1 Public Health Surveillance 10.4.2 Estimating the Burden of Foodborne Disease 10.4.3 Attributing Illnesses to Foods 10.5 Conclusions References Chapter 11 Predictive Microbiology Tools for Exposure Assessment 11.1 Predictive Microbiology for Quantitative Microbiological Risk Assessment 11.2 Predictive Microbiology Model Types 11.3 Primary Models: Growth, Interaction and Inactivation Models 11.3.1 Growth Models 11.3.1.1 Gompertz and Logistic Models 11.3.1.2 Baranyi and Roberts Model 11.3.1.3 Buchanan Three-Phase Linear Model 11.3.2 Interaction Models 11.3.2.1 Jameson Effect Model 11.3.2.2 Lotka–Volterra Model 11.3.3 Phoenix Phenomenon Model 11.3.4 Inactivation Models 11.3.4.1 Bigelow Model 11.3.4.2 Weibull Model 11.3.4.3 Shoulder/Tail Model (Geeraerd Model) 11.4 Secondary Models 11.4.1 Ratkowsky or Square Root Model 11.4.2 Arrhenius-Type Model 11.4.3 Polynomial or Response Surface Models 11.4.4 Bigelow Model 11.5 Transfer Models 11.6 Mixing, Partitioning and Others 11.6.1 Mixing 11.6.2 Partitioning 11.6.3 Removal 11.7 Growth Probability Models (Growth/No Growth) 11.8 Model Generation Process 11.8.1 Data Generation: Experimental Design, Data Acquisition and Data Process 11.8.1.1 Experimental Design 11.8.1.2 Data Collection 11.8.1.3 Data Processing 11.8.2 Model Fitting and Goodness-of-Fit Indexes 11.8.2.1 Model Fitting 11.8.2.2 Goodness-of-Fit Indexes 11.8.3 Model Validation 11.9 Conclusions References Chapter 12 Modelling Cross-Contamination in Food Processing 12.1 Introduction: Transfer and Cross-Contamination 12.2 Cross-Contamination as a Non-Log-Linear Process: Implications for Cross-Contamination Models and Risk Assessment 12.3 Towards a Generic Mechanistic Model for Cross-Contamination in Food Processing 12.3.1 Models for Cross-Contamination during Industrial Broiler Processing 12.3.2 Models for Cross-Contamination during Grinding and Slicing 12.4 Overview of Models in Large-Scale Food Processing 12.5 Cause and Implications of the Tailing Phenomenon 12.6 Evaluation of the Performance of Cross-Contamination Models 12.7 Summary and Outlook Appendix 12.1 Comparison of the Models Used by Sheen and Hwang (2010), Nauta et al (2005) and Møller et al (2012) References Chapter 13 Expert Systems Applied to Microbial Food Safety 13.1 Introduction 13.2 Software Presentation 13.3 User Perspective 13.4 Challenges and Opportunities References Chapter 14 Dose–Response Models for Microbial Risk Assessment 14.1 Introduction 14.2 Exponential Model 14.3 Beta-Poisson Model 14.4 Fitting Challenge Trials Data to Exponential and Beta-Poisson Models 14.5 Dose-Response Model from Human Outbreak Data 14.6 Dose–Response Model Combining Surveillance Epidemiological Data and Exposure Data 14.7 Conclusions References Section III: Chemical Risk Assessment Chapter 15 Quantitative Chemical Risk Assessment Methods 15.1 Introduction 15.2 Chemical Risk Assessment 15.2.1 Hazard Identification 15.2.2 Hazard Characterization 15.2.3 Exposure Assessment 15.2.4 Risk Characterization 15.3 Risk Assessment Methods 15.3.1 Conceptual Model of Risk Assessment 15.3.2 Deterministic Approach 15.3.3 Probabilistic Approach 15.3.4 Tiered Approach 15.3.5 Uncertainty Analysis 15.4 Current and Future Challenges in Chemical Risk Assessment 15.4.1 Mixture Risk Assessment 15.4.1.1 How to Group Substances in a Mixture 15.4.1.2 How to Assess the Risk of a Chemical Mixture 15.4.2 Aggregate Exposure and Biomonitoring Data 15.4.3 Integrating Toxicokinetic Models in Chemical Risk Assessment 15.5 Epidemiology and Chemical Risk Assessment 15.6 Conclusion Acronyms References Chapter 16 Uncertainty Analysis in Chemical Risk Assessment 16.1 Introduction 16.2 Definition of Uncertainty 16.3 Classification of Uncertainty Sources 16.4 Uncertainty Analysis Approach 16.4.1 Preliminary Step: Planning the Uncertainty Analysis 16.4.2 Step 1: Identification and Description of Uncertainty Sources 16.4.3 Step 2: Individual Assessment of Uncertainties 16.4.4 Step 3: Assessing the Impact of the Combined Uncertainties 16.4.5 Step 4: Prioritization of Sources of Uncertainty 16.4.6 Step 5: Communication of the Results of the Uncertainty Analysis 16.5 Conclusion References Chapter 17 Examples of Quantitative Mycotoxin Risk Assessments: Use and Application in Risk Management 17.1 Introduction 17.2 Mycotoxins 17.3 Risk Assessment 17.3.1 Dietary Exposure Assessment of Mycotoxins 17.4 Quantitative Exposure Assessment of Mycotoxins through Dietary Exposure Modelling 17.4.1 Acute vs. Chronic Dietary Exposure Assessments 17.4.1.1 Chronic Exposure Assessment Models 17.4.1.2 Acute Exposure Assessment Models 17.4.2 Case Studies 17.4.2.1 Quantitative Assessment of Risk Derived from Dietary Intake of OTA 17.4.2.2 Quantitative Assessment of Risk Derived from Dietary Exposure to Deoxynivalenol 17.4.2.3 Quantitative Assessment of Risk Derived from Dietary Intake of T2-HT2 17.4.2.4 (Integrative) Quantitative Assessment of Carcinogenic Risk Derived from Dietary Intake of Aflatoxin B1 17.5 Quantitative Exposure Assessment of Mycotoxins Based on Food Chain Data 17.5.1 Case Study 1: Simulation of Consumer Exposure to Deoxynivalenol According to Wheat Crop Management and Grain Segregation (Le Bail et al. 2005) 17.5.1.1 Preharvest 17.5.1.2 Postharvest 17.5.1.3 Processing 17.5.2 Case Study 2: Evaluation of Strategies for Reducing Patulin Contamination of Apple Juice Using a Farm-to-Fork Risk Assessment Model (Baert et al., 2012) 17.5.2.1 Preharvest 17.5.2.2 Postharvest 17.5.2.3 Processing 17.5.3 Case Study 3: A Stochastic Simulation Model for the Quantitative Assessment of the Concentration of Mycotoxins in Milk and the Related Human Exposure (Signorini et al. 2012) 17.6 Concluding Remarks References Index