Application of Food MicroModel predictive software in the development of Hazard Analysis Critical Control Point (HACCP) systems

Panisello, Pedro Javier; Quantick, Peter C.

doi:10.1006/fmic.1998.0195

Cited by 21 publications

(4 citation statements)

References 33 publications

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“…Nevertheless, there are reports that a pH between 4.4 and 4.7 is a caution zone where growth could occur (Panisello & Quantick, 1998), as it was observed in the mamey pulp with 250 mg/kg of ascorbic acid, pH 4.5 and 10 C after 300 h of storage (Table 3). Therefore, the growth and no-growth threshold of the bacterium in mamey pulp is placed at a pH equal to or greater than 4.5 and 10 C. This agrees with the studies by Koutsoumanis and Sofos (2005) on the effect of inoculum size to determine the growth limits of L. monocytogenes in a laboratory medium, where from a concentration greater than 3 logarithmic cycles the bacteria was able to grow at 10 C, pH !…”

Section: Hurdles In the Mamey Pulpmentioning

confidence: 82%

“…The safe and adequate pH to control Listeria is less than 4.5 (Panisello & Quantick, 1998). In this context, Pérez-Díaz et al (2008) demonstrated that vegetable puree supplemented with 0.06% (wt/vol) ascorbic acid or benzoic acid plus mild acidification with citric acid to pH 4.2 effectively prevented growth of L. monocytogenes under refrigerated conditions (4 C) for 3 weeks.…”

Section: Hurdles In the Mamey Pulpmentioning

confidence: 98%

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Effect of pH and storage conditions on Listeria monocytogenes growth inoculated into sapote mamey (Pouteria sapota (Jacq) H.E. Moore & Stearn) pulp

et al. 2012

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Section: Hurdles In the Mamey Pulpmentioning

confidence: 82%

Section: Hurdles In the Mamey Pulpmentioning

confidence: 98%

Effect of pH and storage conditions on Listeria monocytogenes growth inoculated into sapote mamey (Pouteria sapota (Jacq) H.E. Moore & Stearn) pulp

et al. 2012

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“…These include the USDA's Pathogen Modeling Program, (71) which is available without charge from http://www.arserrc.gov/mfs/ pathogen.htm. A similar suite of software (Food MicroModel, sponsored by the UK government) was developed by a consortium of industry and government researchers (72,73) (see also http://www.lfra.co.uk/ micromodel/index.html). Seafood Spoilage Predictor was developed to predict shelf-life of seafood at constant and under fluctuating temperature storage conditions.…”

Section: Sources Of Modelsmentioning

confidence: 99%

Modeling Microbial Growth Within Food Safety Risk Assessments

Ross

McMeekin²

2003

Risk Analysis

110

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Risk estimates for food-borne infection will usually depend heavily on numbers of microorganisms present on the food at the time of consumption. As these data are seldom available directly, attention has turned to predictive microbiology as a means of inferring exposure at consumption. Codex guidelines recommend that microbiological risk assessment should explicitly consider the dynamics of microbiological growth, survival, and death in foods. This article describes predictive models and resources for modeling microbial growth in foods, and their utility and limitations in food safety risk assessment. We also aim to identify tools, data, and knowledge sources, and to provide an understanding of the microbial ecology of foods so that users can recognize model limits, avoid modeling unrealistic scenarios, and thus be able to appreciate the levels of confidence they can have in the outputs of predictive microbiology models. The microbial ecology of foods is complex. Developing reliable risk assessments involving microbial growth in foods will require the skills of both microbial ecologists and mathematical modelers. Simplifying assumptions will need to be made, but because of the potential for apparently small errors in growth rate to translate into very large errors in the estimate of risk, the validity of those assumptions should be carefully assessed. Quantitative estimates of absolute microbial risk within narrow confidence intervals do not yet appear to be possible. Nevertheless, the expression of microbial ecology knowledge in "predictive microbiology" models does allow decision support using the tools of risk assessment.

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“…Models that predict growth of HBP are valuable tools for food safety. For example, a common practical application of predictive models for growth of HBP is in Hazard Analysis and Critical Control Point (HACCP) programs (Baker, ; Elliott, ; Panisello & Quantick, ). In HACCP, predictive models are used to verify that a Critical Control Point (CCP) is under control.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

Oscar

2018

Int J of Food Sci Tech

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Summary A study was undertaken to model growth of Salmonella on tomatoes for developing and validating a predictive model for use in risk assessment. Cylindrical portions (0.14 g) of Roma tomato pulp were inoculated with a low dose (0.89 log MPN) of Salmonella Newport. The inoculated tomato portions were incubated for 0–8 h at 16–40 °C in 2 °C increments to obtain most probable number (MPN) data for model development and validation. A multiple‐layer feedforward neural network model with two hidden layers of two nodes each was developed. The proportion of residuals in an acceptable prediction zone (pAPZ) from −1 (fail‐safe) to 0.5 log (fail‐dangerous) was 0.93 (194/209) for dependent data and 0.96 (86/90) for independent data for interpolation. A pAPZ ≥0.7 indicated that the model provided acceptable predictions. Thus, the model was successfully validated. It was also validated for extrapolation to seven other Salmonella serotypes.

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Application of Food MicroModel predictive software in the development of Hazard Analysis Critical Control Point (HACCP) systems

Cited by 21 publications

References 33 publications

Effect of pH and storage conditions on Listeria monocytogenes growth inoculated into sapote mamey (Pouteria sapota (Jacq) H.E. Moore & Stearn) pulp

Effect of pH and storage conditions on Listeria monocytogenes growth inoculated into sapote mamey (Pouteria sapota (Jacq) H.E. Moore & Stearn) pulp

Modeling Microbial Growth Within Food Safety Risk Assessments

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

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