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Experimental Design and Data Generation

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Predictive Microbiology in Foods

Part of the book series: SpringerBriefs in Food, Health, and Nutrition ((BRIEFSFOOD,volume 5))

Abstract

One of the most critical steps when generating a predictive model is to correctly design an experiment and collect suitable microbial data. Experimental design will influence model structure and validation conditions. The survival and growth of microorganisms in foods is affected not only by the chemical composition of the food and its storage conditions but also by the food matrix. In this sense, a better quantification of the food structure effect has been studied throughout these years. Regarding the method of data collection, although plating count has been widely used (and still is used), there are rapid methods to obtain reliable and cost-effective data. These achievements were primarily based on turbidimetry, although other methods (microscopy, image analysis, flow cytometry, etc.) have arisen as novel approaches in the predictive microbiology field. These aspects are further discussed in this chapter.

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  1. Dpt Bromatologia y Tec. de los Alimentos, University of Córdoba, Córdoba, Spain

    Fernando Pérez-Rodríguez & Antonio Valero

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  2. Antonio Valero
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© 2013 Fernando Pérez-Rodríguez and Antonio Valero

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Pérez-Rodríguez, F., Valero, A. (2013). Experimental Design and Data Generation. In: Predictive Microbiology in Foods. SpringerBriefs in Food, Health, and Nutrition, vol 5. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5520-2_2

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