Risk Assessment Methods for Biological and Chemical Hazards in Food

Fernando Pérez-Rodríguez, Fernando Perez Rodriguez, Fernando Pérez-Rodríguez, Fernando Perez Rodriguez

• Publisher: CRC Press
• Year: 2020
• Print ISBN:
  9781498762021
• eBook ISBN:
  9780429531859
• Topic: Physical Sciences
• Subtopic: Microbiology

Risk assessment has been extensively developed in several scientific fields, such as environmental science, economics, and civil engineering, among others. In the aftermath of the SPS and GATT agreements on the use of risk analysis framework in food trade, signed in the 1990s, international organisations and governments adopted risk assessment as a science-based process to ensure food safety along the food chain. The food industry can also benefit from the use of this approach for food process optimisation and quality assurance.

Risk Assessment Methods for Biological and Chemical Hazards in Food introduces the reader to quantitative risk assessment methods encompassing general concepts to specific applications to biological and chemical hazards in foods. In the first section, the book presents food risk assessment as methodology and addresses, more specifically, new trends and approaches such as the development of risk rating methods, risk metrics, risk-benefit assessment studies and quality assessment methods.

Section II is dedicated to biological hazards. This section identifies the most relevant biological hazards along the food chain and provides an overview on the types of predictive microbiology models used to describe the microbial response along the food chain. Chapter 12 specifically deals with cross contamination and the quantitative methods that can be applied to describe this relevant microbial process. The development and application of dose-response models (i.e. mathematical function describing the relationship between pathogen dose and health response) are also covered in this section. In Section III, the book translates risk assessment concepts into the area of chemical hazards, defining the process steps to determine chemical risk and describing the uncertainty and variability sources associated with chemicals.

Key Features:

• Presents new trends and approaches in the field of risk assessment in foods
• Risk assessment concepts are illustrated by practical examples in the food sector
• Discusses how quantitative information and models are integrated in a quantitative risk asssment framework
• Provides examples of applications of quantitative chemical risk assessment in risk management

The book, written by renowned experts in their field, is a comprehensive collection of quantitative methods and approaches applied to risk assessment in foods. It can be used as an extensive guide for food safety practitioners and researchers to perform quantitative risk assessment in foods

Section II

Microbial Risk Assessment

9

Quantitative Methods for Microbial Risk Assessment in Foods

Winy Messens, Marios Georgiadis, Caroline Merten, Kostas Koutsoumanis, Matthias Filter, Carolina Plaza-Rodriguez, and Fernando Pérez-Rodríguez

Contents

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

9.1 Introduction

According to the Codex Alimentarius Commission, risk assessment is a scientifically based process consisting of four steps: (i) hazard identification, (ii) hazard characterization, (iii) exposure assessment, and (iv) risk characterization (CAC 1999). As opposed to qualitative microbial risk assessment (MRA), which provides the risk outcome as descriptive categories, quantitative microbial risk assessment (QMRA) provides numerical expressions to quantitatively assess the adverse health effects resulting from exposure to pathogens. QMRA provides a quantitative basis to support decision-making that aims to reduce food safety risks (FAO/WHO 2006) and plays an important role within international food safety policies (Havelaar, Nauta, and Jansen 2004).

Unlike quantitative chemical risk assessment, the methodology used for QMRA must consider that the microbial counts can change (increase or decrease) during the consecutive phases of the food chain. In addition, the characteristics of the various microorganisms addressed and the differences between the (susceptible) population groups make the implementation of QMRA a challenging undertaking (Voysey and Brown 2000, FAO/WHO 2006).

The elements and approaches needed to perform a QMRA span from microbial counts obtained on foods by enumeration methods to predictive tools applying mathematical models to understand the relationship between the occurrence of pathogens in a food matrix (both prevalence and concentration) and their public health risk. The development of QMRA models is the most advanced in terms of resource requirements and complexity, whether using a deterministic approach (in which the variables are represented by single-point estimates) or a stochastic approach (in which probability distributions are used to describe variables). The latter is generally considered to be the most suitable, as it provides confidence intervals (CIs) of the risk assessment output and therefore, in general terms, gives more realistic estimates. However, it is often complex and difficult to generate (FAO/WHO 2006). This complexity is reflected in the number and diversity of risk assessment software tools currently available. It should be clear that the outcome of such an exercise is dependent on the inputs and functional forms of the models used, which often originate from assumptions that need to be carefully considered and justified.

The generation process of QMRA models is founded on background knowledge (Haberbeck et al. 2018), which consists of data and models that can be retrieved from different sources. Sources include scientific studies (published and/or unpublished), monitoring and surveillance data, laboratory diagnostic data and data from disease outbreak investigations, data from food consumption surveys, national and international risk assessments, and expert opinion (FAO/WHO 2006, Haberbeck et al. 2018). New types of information can be incorporated into QMRA modelling. For example, the omics-based mechanistic inputs may fill key knowledge gaps by providing full genome coverage as well as providing new perspectives on strain diversity and physiological uncertainty (Brul et al. 2012). The emerging field of metagenomics offers the potential to fully characterize the genome content of microorganisms in a sample and therefore is likely to have a great influence on how QMRA will be carried out in the future (Brul et al. 2012).

Importantly, the characterization of uncertainty and variability can increase the understanding of the outputs of the risk assessment, informing decision-makers about the reliability of the obtained results and guiding them in the process of decision-making (Nauta 2000). The degree of credibility of the output of a QMRA model depends largely on the quality and quantity of the data used as well as the appropriateness of the model structure and assumptions made. Careful consideration of the entire modelling process, including the validity of the assumptions and related uncertainties, is, therefore, very important.

Transparency and consistency should play a major role throughout the QMRA process, which should be fully documented and systematically described, including a representation of the strengths and limitations of the model (data quality, assumptions, model structure, variability and uncertainty, and other important attributes of the assessment). This will contribute to the application and re-use of the available knowledge (data and models). Several additional resources have been proposed to enhance transparency and to facilitate knowledge exchange (and therefore the entire QMRA process), such as the establishment of harmonized data formats, the development of consistent rules for knowledge annotation, and the creation of open access food safety knowledge repositories (Plaza-Rodríguez et al. 2018, Haberbeck et al. 2018).

9.2 Quantitative Resources for Risk Assessment

As mentioned before, QMRA consists of four steps. The hazard identification step, the food matrix, and the consumers considered (population groups) determine the risk assessment question to be addressed by the QMRA. The hazard characterization step considers the pathogenicity of the microorganism and usually includes a dose/response (DR) relationship model. The exposure assessment step estimates the dose ingested and usually considers the prevalence and concentration of the pathogens in the food at the moment of consumption, the consumption frequency, and the serving size. Predictive microbiology ...

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Contents
• Preface
• Editor
• Contributors
• SECTION I General Aspects
• SECTION II Microbial Risk Assessment
• SECTION III Chemical Risk Assessment
• Index