Risk Assessment Methods for Biological and Chemical Hazards in Food 2020024661, 2020024662, 9781498762021, 9780429083525

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Risk Assessment Methods for Biological and Chemical Hazards in Food
 2020024661, 2020024662, 9781498762021, 9780429083525

Table of contents :
Half Title
Title Page
Copyright Page
Table of Contents
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
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
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 Cost-of-Illness Willingness to Pay
3.5 Risk Ranking
3.6 Risk-Benefit Assessment
3.7 Conclusions
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
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 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
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
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 Characterize the Variability/Uncertainty from the Data Use Monte-Carlo Simulations to Integrate Variability 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
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 Nominal Range Sensitivity Analysis (NRSA) Break-Even Analysis (BEA)
8.2.2 Statistical Methods Model Independent Methods Model Dependent Methods
8.2.3 Graphical Methods
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 Data on Human Cases Data on Foodborne Outbreaks
9.2.2 Animal/Food Data EFSA Monitoring Data EU-Wide Baseline Survey Data EU Rapid Alert System for Food and Feed Data Other Resources, e.g. Data/Models from Scientific Literature
9.2.3 Consumption Data EFSA Consumption Data Other Consumption Data 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
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
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 Gompertz and Logistic Models Baranyi and Roberts Model Buchanan Three-Phase Linear Model
11.3.2 Interaction Models Jameson Effect Model Lotka–Volterra Model
11.3.3 Phoenix Phenomenon Model
11.3.4 Inactivation Models Bigelow Model Weibull Model 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 Experimental Design Data Collection Data Processing
11.8.2 Model Fitting and Goodness-of-Fit Indexes Model Fitting Goodness-of-Fit Indexes
11.8.3 Model Validation
11.9 Conclusions
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)
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
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
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 How to Group Substances in a Mixture 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
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
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 Chronic Exposure Assessment Models Acute Exposure Assessment Models
17.4.2 Case Studies Quantitative Assessment of Risk Derived from Dietary Intake of OTA Quantitative Assessment of Risk Derived from Dietary Exposure to Deoxynivalenol Quantitative Assessment of Risk Derived from Dietary Intake of T2-HT2 (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) Preharvest Postharvest 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) Preharvest Postharvest 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

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