title: Indices for performance evaluation of predictive models in food microbiology.
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.
authors.affiliation: Department of Agricultural Science, University of Tasmania, Hobart, Australia. Tom.Ross@agsci.utas.edu.au
authors.forename: T
authors.initials: T
authors.lastname: Ross
mesh_terms: D005069:Evaluation Studies as Topic; D005516:Food Microbiology; D008432:Mathematical Computing; D008962:Models, Theoretical; D013211:Staphylococcus aureus / Q000254:growth & development*