The Application of Quantitative Risk Assessment to Microbial Food Safety

1998 ◽  
Vol 61 (5) ◽  
pp. 640-648 ◽  
Author(s):  
DAVID JOHN VOSE

Quantitative risk assessment (QRA) is rapidly accumulating recognition as the most practical method for assessing the risks associated with microbial contamination of foodstuffs. These risk analyses are most commonly developed in commercial Computer spreadsheet applications, combined with Monte Carlo simulation add-ins that enable probability distributions to be inserted into a spreadsheet. If a suitable model structure can be defined and all of the variables within that model reasonably quantified, a QRA will demonstrate the sensitivity of the severity of the risk to each stage in the risk-assessment model. It can therefore provide guidance for the selection of appropriate risk-reduction measures and a quantitative assessment of the benefits and costs of these proposed measures. However, very few reports explaining QRA models have been submitted for publication in this area. There is, therefore, little guidance available to those who intend to embark on a full microbial QRA. This paper looks at a number of modeling techniques that can help produce more realistic and accurate Monte Carlo simulation models. The use and limitations of several distributions important to microbial risk assessment are explained. Some simple techniques specific to Monte Carlo simulation modelling of microbial risks using spreadsheets are also offered which will help the analyst more realistically reflect the uncertain nature of the scenarios being modeled. simulation, food safety

Risk Analysis ◽  
2004 ◽  
Vol 24 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Régis Pouillot ◽  
Pascal Beaudeau ◽  
Jean‐Baptiste Denis ◽  
Francis Derouin ◽  

2013 ◽  
Vol 19 (3) ◽  
pp. 521-527 ◽  
Author(s):  
Song YANG ◽  
Shuqin WU ◽  
Ningqiu LI ◽  
Cunbin SHI ◽  
Guocheng DENG ◽  
...  

2021 ◽  
Vol 7 ◽  
pp. 1954-1961
Author(s):  
Andrea Colantoni ◽  
Mauro Villarini ◽  
Danilo Monarca ◽  
Maurizio Carlini ◽  
Enrico Maria Mosconi ◽  
...  

2003 ◽  
Vol 66 (10) ◽  
pp. 1900-1910 ◽  
Author(s):  
VALERIE J. DAVIDSON ◽  
JOANNE RYKS

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (−4 log CFU/ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (−3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (−2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.


2016 ◽  
Vol 23 (3) ◽  
pp. 97-105
Author(s):  
Deyu He ◽  
Niaoqing Hu ◽  
Lei Hu ◽  
Ling Chen ◽  
YiPing Guo ◽  
...  

Abstract Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.


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