Prediction of food thermal process evaluation parameters using neural networks

Abstract

Two neural networks (ANN) were developed to predict thermal process evaluation parameters g and f h/ U (the ratio of heating rate index to the sterilizing value), respectively. The temperature change required for the thermal destruction curve to traverse one log cycle ( z), cooling lag factor ( j c) and f h/ U were input variables for predicting g and z, while j c and g were inputs for predicting f h/ U. The data used to train and verify the ANN were obtained from reported values. Shrinking of input and output variables using natural logarithm function improved the prediction accuracy. The use of “Wardnets” with three slabs of 14 nodes in each slab, with a learning rate of 0.7 and momentum of 0.9 provided the best predictions. The g (unshrunk values) was predicted with a mean relative error of 1.25±1.77%, and a mean absolute error of 0.11±0.16 °F. The f h/ U was predicted with a mean relative error of 1.41±3.40%, and a mean absolute error of 2.43±15.97, using 10 nodes in each slab. The process time calculated using the g from the ANN models closely followed the time calculated from the tabulated g values (RMS=0.612 min, average absolute error=0.466 min with an S.D. of 0.400 min).