Journal Article10.1111/J.1365-2672.1996.TB03539.X
Indices for performance evaluation of predictive models in food microbiology
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TL;DR: Evaluation of predictive models by comparison to published microbial growth rate data may be inappropriate because of limitations in that data, and two complementary measures are proposed as simple indices of the performance of models in predictive food microbiology.
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Abstract: Two complementary measures are proposed as simple indices of the performance of models in predictive food microbiology. The indices assess the level of confidence one can have in the predictions of the model and whether the model displays any bias which could lead to 'fail-dangerous' predictions. The use of the indices is demonstrated using data collated from independent and published literature. This analysis supports previous reports that evaluation of predictive models by comparison to published microbial growth rate data may be inappropriate because of limitations in that data. The indices may fail to reveal some forms of systematic deviation between observed and predicted behaviour. It is concluded, however, that the indices provide an objective and readily interpreted summary of model performance and may serve as a first step towards the development of an objective and useful definition of the term 'validated model' in predictive food microbiology.
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Citations
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Effect of fish meal replacement by plant protein sources on non-specific defence mechanisms and oxidative stress in gilthead sea bream (Sparus aurata)
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References
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S A Alber,Donald W. Schaffner +1 more
TL;DR: A comparison was made between mathematical variations of the square root and Schoolfield models for predicting growth rate as a function of temperature and a natural logarithm transformation of growth rate was more effective than a square root transformation at correcting for the heterogeneity of variance.
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Choosing probability distributions for modelling generation time variability
TL;DR: The results show that the 'gamma' distribution is a suitable stochastic assumption when modelling generation time, and enables one to predict, for example, a mean generation time of 615 min at 2.4°C, and that 0.1% of the observed values will fall below 471 min and one in a million below 405 min.
53
Response surface model of the effect of pH, sodium chloride and sodium nitrite on growth of Yersinia enterocolitica at low temperatures
TL;DR: In this paper, a fractional factorial design was used to measure the effects and interactions of temperature (5, 12, 19°C), pH (4.5-8.5), sodium chloride (0.5−5%), and sodium nitrite (0−200 μg/ml) on the aerobic growth of Y. enterocolitica in brain heart infusion broth.
52
•Dissertation
A philosophy for the development of kinetic models in predictive microbiology
Tom Ross
- 01 Jan 1993
TL;DR: It is concluded that the experimental strategy proposed offers an efficient method for generating the quantity of data required for the development of reliable kinetic models from which to predict the growth of spoilage and pathogenic organisms of relevance to foods.
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