Evaluation Of Sensitivity Analysis Methods For Application To Microbial Food Safety Process Risk Models

Keywords: Variability, Sensitivity Analysis, Microbial Food Safety, Risk Assessment, Uncertainty.

With the emergence of quantitative risk assessment models in the field of food safety, there has also been a growing recognition of the need for sensitivity analysis of such models. Key questions that must be addressed in performing sensitivity analysis with food safety risk models include the following: What are the key criteria for sensitivity analysis methods applied to food safety risk assessment?; What sensitivity analysis methods are most promising for application to food safety and risk assessment?; and What are the key needs for implementation and demonstration of such methods? Microbial food safety process risk (MFSPR) models are examples in which the process of sensitivity analysis is challenged by typical characteristics of those models such as non-linearity, thresholds, interactions, use of both continuous and categorical inputs, and two-dimensional simulation of variability and uncertainty. The three main purposes of this dissertation are to: (1) evaluate sensitivity analysis methods; (2) present examples of how sensitivity analysis can be applied to MFSPR models and how the results can be presented and interpreted; and (3) provide guidance to risk analysis practitioners regarding the application of sensitivity analysis, particularly with regard to MFSPR models as well as other risk models with similar characteristics. This study included a review of existing methods, and a detailed series of quantitative case studies of multiple sensitivity analysis methods applied to an E. coli O157:H7 food safety process risk model in ground beef. Methods evaluated include Pearson and Spearman correlation analyses, sample and rank regression analyses, sample and rank stepwise regression analyses, analysis of variance, and classification and regression trees. The guidance is intended to assist exposure and risk modeling practitioners to make effective use of sensitivity analysis methods and the insights they provide. The guidance provides insight regarding: (1) why an.

With the emergence of large food safety risk models, there has also been a growing recognition of the need for sensitivity analysis of such models. Key questions that must be addressed in performing sensitivity analysis with food safety risk models include the following: What are the key criteria for sensitivity analysis methods applied to food safety risk assessment?; What sensitivity analysis methods are most promising for application to food safety and risk assessment?; and What are the key needs for implementation and demonstration of such methods? Food safety risk assessment models are challenging because they typically include: (1) nonlinearities; (2) thresholds; (3) continuous, discrete, and categorical inputs; and (4) two-dimensional simulation of variability and uncertainty. In June 2001, NCSU hosted a workshop on sensitivity analysis. Recommendations were made regarding a guideline document to assist practitioners in selecting, applying, interpreting, and reporting the results of sensitivity analysis. The workshop also supported the need for case studies with existing food safety risk models to demonstrate to practitioners how sensitivity analysis methods can be used and to evaluate various specific methods. The two main purposes of this report are to: (1) evaluate sensitivity analysis methods; and (2) present an example of how sensitivity analysis can be applied to food safety risk assessment models and how the results can be presented and interpreted. This study included a review of existing methods, and a detailed series of quantitative case studies of multiple sensitivity analysis methods applied to Listeria monocytogenes model. Methods evaluated include local sensitivity analysis (e.g., nominal range sensitivity, differential sensitivity), global sensitivity analysis methods (e.g., linear regression, analysis of variance, and regression trees), and the graphical method of scatter plot. The report present example results and insights from the applicati.

Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food safety risk assessment models are presented in this paper. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods and one graphical method, are identified. Application of these methods was also illustrated with the examples from various fields. These methods were compared on the basis of their applicability to different types of models, computational issues such as initial data requirement, time requirement, and complexity of their application, representation of the sensitivity, and the specific uses of these methods. No one method is clearly best for food safety risk models. In general, the use of two or more methods may be needed to increase confidence on the rank ordering of key inputs. To identify specific issues with respect to the application to a typical food safety risk model, the sensitivity analysis methods were applied to the risk assessment model of the public health impact of vibrio Parahaemolyticus (the Vp model). The Vp model was modified so that proper sensitivity analysis can be done on independent inputs. The results of the sensitivity analyses were interpreted and discussed in detail. The rank ordering of key inputs was reasonably similar for most of the methods. For example, five of the seven methods ranked water temperature, the number of oysters per meal, and a new input IUR in the top three. Time on water and an input IG were identified as the least important inputs by six methods.

Keywords: food safety, risk assessment, sensitivity analysis methods, complex models, critical control points.

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.

The guidelines aim to provide a practical framework and approach for undertaking exposure assessment of microbiological hazards (bacteria, fungi, viruses, protozoa and microbial toxins) in foods in the context of a risk assessment or as a stand-alone process.

This investigation reviewed and evaluated methodologies used for microbial risk assessment with respect to their applicability for reclaimed water applications. The investigation was comprised of five primary components: a comprehensive database of articles, reports and books describing microbial risk assessment methodologies was established and reviewed. Risk assessment techniques and models were identified for estimating the public health risk from exposure to microorganisms via reclaimed water applications. Two models were identified for further evaluation: a static (individual based) and a dynamic (population based). In the third component, the two models were evaluated to differentiate between the conditions under which models predict similar and substantially different estimations of risk. Through numerical simulation, exposure/pathogen combinations were identified when it may be appropriate to use the less complex, static model. Case study risk assessment scenarios demonstrated the model selection process for three realistic, yet hypothetical reclaimed water scenarios.The fourth component presents a constraint analysis for existing reuse regulations. The constraint analysis is carried out by documenting the existing reuse regulations. The constraint analysis is carried out by documenting the existing regs in three states for landscape irrigation and uses that comparison as a starting point to identify how microbial risk assessment may be useful within the context of existing and potential future water reuse regulations. The investigation concludes by identifying criteria for a computer interface that would allow regulatory and/or municipal agencies/utilities to take advantage of the analysis discussed in the report. This publication can also be purchased and downloaded via Pay Per View on Water Intelligence Online - click on the Pay Per View icon below

It is in the risk characterization step that the results of the risk assessment are presented.

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