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Chapter 4 : The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment

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Abstract:

The use of models to quantitatively describe the transmission of pathogens over the food-production chain is increasing in quantitative microbial risk assessment (QMRA). Such models may cover the whole “farm-to-fork” food pathway or only the part that is relevant to the problem. This chapter explains the use of a methodology developed for this purpose: the modular process risk model (MPRM). First, the MPRM methodology is outlined, offering guidelines to perform a food chain QMRA; then, some examples are given to illustrate these guidelines and to show how MPRM has been applied in practical situations. The major advantage of modeling is that it is an instrument that allows an easy comparison of a broad range of alternative scenarios, the main objective of risk assessment. Risk assessment deals with risk, which is a function of probability and severity of an event. As variability and uncertainty can both be represented by probability distributions and the difference between the two is not always obvious, they are easily mixed up. In MPRM food chain risk assessment, the selection of the “best” model depends on the statement of purpose, process knowledge, and data availability.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4

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Quantitative Risk Assessment
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Risk Assessment
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Figures

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Figure 1

Seven steps for conducting QMRA with MPRM.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 2

Each module in MPRM describes the change in prevalence () and number of microorganisms per unit () in a process step. The food chain can be regarded as a line of linked modules.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 3

Partitioning: A large unit, containing cells (particles, spores, CFU, etc.), is split into small units ( = 1 … ) that contain cells. The objective is to describe the distribution of the over the small units, given the values of and .

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 4

Mixing: small units, containing cells (particles, spores, CFUs, etc.) in the units ( = 1 … ), are joined to form a new large unit with cells. The objective is to describe the probability distribution of , given a distribution of the .

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 5

Cross-contamination between a series of units modeled in line (right) is assumed to occur via an environment that can represent air, water, equipment, hands, etc. The transmission rate to the environment is given by , the transmission rate back is given by .

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 6

The effect of cross-contamination in a production line on the variability distribution of log(), the number of cells per food unit. (Left) A normal input distribution of log( ), with μ = 2, σ = 0.7. With model parameter = 0.2 the output distribution strongly shifts to the right for low values but is almost unaltered for high values. (Right) A normal input distribution of , with μ = 5,000, σ = 1,500. With model parameter = 0.5 the output distribution shows a smaller variance and also a decrease in high values.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 7

A schematic representation of the broccoli puree production process. The process starts with raw broccoli heads, which are treated and gathered in 280-kg batches. After ingredients are added, puree packages of 380 g are produced.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 8

The percentages of packages containing critical levels of for five different strain types (avtz415, z4222, avz421, l2104, and cip527) and a selection of five different “typical” consumer refrigerator temperature profiles (European [EU], fixed at 7°C [fixed], following regulations [= mean 5°C with standard deviation 1.9 {Standard}, North European {North} and South European {South}]). Percentages of packages with more than the critical level of 10 CFU/g are given as “> 10^5” bars. Other bars indicate more realistic values, leaving out packages that are likely to be spoiled with cells after reaching the Maximum Population Density (MPD), together with either not passing the Sell By Date (SBD) and “not being spoiled” by spoilage flora, as predicted according to the time-temperature profile ( ). Apparently the psychrotrophic strain avtz415 with the “South” domestic fridge temperature profile (mean, 8.3°C) holds the largest risk.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 9

Food pathway of the risk assessment model. The MPRM exposure model started at the entrance of the processing plant and ended with salads cross-contaminated by fresh chicken breast fillet in a domestic setting.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 10

The relative frequencies of dose classes (exposures) and the percentages of human cases of campylobacteriosis attributable to those classes. Most of the exposures are to low doses of 1 to 10 CFU . Higher doses have more impact than the distribution of exposures suggests.

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 11

The effect of inactivation (1 log reduction), partitioning (to 10 smaller units), and removal on the log of the number of CFUs per unit, given a Normal N(3,1) variability distribution over the units of log .

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Figure 12

The effects of growth (1 log increase), mixing (of 10 units), and cross-contamination on the log of the number of CFUs per unit, given a Normal N(3,1) variability distribution over the units of log .

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
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Tables

Generic image for table
Table 1

The MPRM model structure of the food pathway as a series of basic processes

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
Generic image for table
Table 2

Overview of the models applied for each of stages of the risk assessment model

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4
Generic image for table
Table 3

Basic processes of the MPRM and their qualitative effect on the prevalence (), the total number of organism in the system (i.e., all units evaluated in one simulation run of the model, ), and the unit size

Citation: Nauta M. 2008. The Modular Process Risk Model (MPRM): a Structured Approach to Food Chain Exposure Assessment, p 99-136. In Schaffner D, Doyle M (ed), Microbial Risk Analysis of Foods. ASM Press, Washington, DC. doi: 10.1128/9781555815752.ch4

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