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Abstract

An artificial neural network (ANN) for reliably predicting the process temperature (T_e) and process time (t) for minimum quality degradation (F_oq) during thermal processing of canned foods was developed. Five inputs (can size, initial temperature, thermal diffusivity, sensitivity indicator of micro-organism and sensitivity indicator of quality) were used to predict the process variables T_e, t, and F_oq. A measure of dependency and statistical tests were used to reduce the number of inputs with little degradation of ANN performance. The feedforward ANN showed satisfactory prediction error. The mean relative error (MRE) was 0.2% in predicting T_e, 3.9% in predicting t, and 1.5% in predicting F_oq. The ANN showed high MRE in predicting the outputs when tested with the radial basis function (RBF) network.

Keywords

artificial neural network food canning food quality food safety pasteurization sterilization thermal processing

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Predicting safety and quality of thermally processed canned foods using a neural network

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