Predictive Microbial Modelling

Gonzales-Barrón, Úrsula

doi:10.1002/9781444355321.ch6

Cited by 2 publications

(2 citation statements)

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“…Two main probabilistic techniques that have been applied to predictive food microbiology are the logistic regression and the artificial neural network (ANN). The ANN is a computer program ”trained” from previous data (examples) through iteration and automatically derives a mathematical relationship between input‐output data through ”neurons”, without prior knowledge of their relationships (Gonzales‐Barron, 2011) and without explicit mathematical equations. Ratkowsky and Ross (1995) proposed a probabilistic approach using logistic regression (Equation 88) aiming to describe the environmental parametric boundary between growth and nongrowth conditions for microorganisms in food.…”

Section: Microbial Populations Modelingmentioning

confidence: 99%

“…In the initial proposal of Ratkowsky and Ross (1995),

θ_{i}

was assumed in the form presented in Equation (77) and the value of reference (minimal or maximum) could be pre‐known or estimated by using a standard logistic regression procedure. More details and other applications of the probabilistic models in modeling microbial populations can be found in Gonzales‐Barron (2011), Hajmeer and Basheer (2002), and Horowitz et al. (2010).…”

Section: Microbial Populations Modelingmentioning

confidence: 99%

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Engineering modeling frameworks for microbial food safety at various scales

Ranjbaran

Carciofi

Datta

2021

Comp Rev Food Sci Food Safe

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The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such modelbased approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer).A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.

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Section: Microbial Populations Modelingmentioning

confidence: 99%

“…In the initial proposal of Ratkowsky and Ross (1995),