Dear Colleagues,

Modern food science is supported by reliable research outcomes depicted or confirmed by data modelling and probability science. Although different types of models can be developed in food science, their objectives mostly revolve around explaining or representing physical, chemical, or biological phenomena in foods or food processes and/or estimating meaningful parameters that are necessary for simulation, prediction, control applications, or optimization/intervention strategies. The ever-growing computational resources available have stimulated the advances in food modelling. The modelling of food products and processes can vary in complexity, attending to factors such as the extent of knowledge on the inherent mechanisms and interactions to be described, the extent of uncertainty about food properties, and the intricacy of the dynamic processes/phenomena to be represented. In food science, models can be classified as inferential or predictive, static or dynamic, empirical or mechanistic, deterministic or stochastic, etc. This Topical Collection entitled “Food Modelling” seeks to gather work on new approaches and applications of modelling in diverse food science fields such as process development and optimization, food formulations, thermal and non-thermal processes, fermentation processes, safety and quality in the food chain, predictive microbiology and risk assessment, food control, heat and mass transfer in food engineering problems, sensory analysis, and chemometrics, among others. The Collection also welcomes submissions on the development of new meta-analysis methods and applications in food science, and the construction of open databases and/or software solutions.

Prof. Dr. Ursula Gonzales-Barron

Dr. Vasco Cadavez

Guest Editors