Learning objectives
<p>The course is aimed to give a critical insight about the potential of predictive microbiology applied to food systems and food industry. The possible applications (i.e. formulation of innovative products, shelf life determination, evaluation of safety, HACCP, risk analysis, etc.) will be discussed. The most important statistical and mathematical instruments available will be analyzed in relation to the interpretation of results and their significance</p>
<p>The different sections in which the course is subdivided will be structured in theorical lessons and practical examples regarding food fermentations and food microbiology. In particular, case studies will be proposed inherent to microbial growth under different conditions, death kinetics following sanitizing treatments (thermal and not thermal), shelf life evaluation of foods and growth of pathogenic bacteria or toxin production. <br />
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Prerequisites
<p>Fundamentals of microbiology, statistics and mathematics</p>
Course unit content
The lessons will be focused on the fundamental application of predictive microbiology. The main topics will be the following: <br />
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- Aim, purposes and use of predictive microbiology; <br />
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- Primary models: microbial dynamics in the ration to the application of specific factors: Growth models: logistic function, Gompertz equation and Baranyi model: Death models: linear model, inverse (mirror) logistic and Gompertz models, Weibull model and models for thermal death kinetics at not constant temperature<br />
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- Secundary models: Ratkowski models, experimental designs, polynomial equations, response surface metodology. Theoretical aspects and practical examples. Use of themodels obtained through predictive microbiology. Validation and significance <br />
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- Tertiary models: use of the most important and diffused software in the field of predictive microbiology (ComBase, PMP, etc.) <br />
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- Probabilistic models: Logit function and log linear models applied to food microbiology. Use of the models with predictive purposes. Validation and significance. <br />
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- Future needs and perspectives: Use of classification techniques to exploit genetic data. <br />
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Full programme
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Bibliography
Materials and information provided during the lessons
Teaching methods
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Assessment methods and criteria
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Other information
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