Prediction of food thermal process evaluation parameters using neural networks

Two neural networks (ANN) were developed to predict thermal process evaluation parameters g and fh/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 (jc) and fh/U were input variables for predicting g and z, while jc and g were inputs for predicting fh/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 fh/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).