Computerization of Stumbo’s method of thermal process calculations using neural networks Original Research Article

The four heat penetration parameters in Stumbo’s method of thermal process calculations were correlated using artificial neural networks (ANN). The process involved the development of two different artificial neural network models, one named ANNG for the parameter g (the difference between the retort and food center temperatures) and the other named ANNFU for the parameter fh/U (the ratio of heating rate index to the sterilizing value). Both these models replace the 57 tables developed by Stumbo for assessing sterilizing effects. The ANNG model deals with estimating the process time for a given process lethality and involves g as the dependent (output) variable while fh/U, z (representing the temperature interval difference that causes a tenfold change in decimal reduction time), and jcc (the cooling rate lag factor) are taken as the independent (input) variables. The ANNFU model involves the prediction of the lethality of a given process with the fh/U being taken as the dependent variable and z, jcc, and g as the independent variables. In developing each of the ANN models, several configurations were evaluated: (i) the input and output parameters were taken on a linear scale, (ii) the input and output parameters were taken after the transformation of some or all the input and output parameters using a logarithmic scale to the base 10, and (iii) all input and output parameters were transformed using a logarithmic scale to the base two. The optimum ANN models, ANNG and ANNFU, were those of the third configuration. ANNG involved a network with six neurons in each of the three hidden layers while ANNFU included 16 neurons in each of the two hidden layers. The two optimal ANN models are capable of predicting the g and fh/U parameters in the range given in Stumbo’s tables. In each instance, the predicted values were in close agreement with those listed in the tables. In addition, the developed ANN models can predict the intermediate values of any combination of inputs. Therefore, they eliminate the need for excessive storage requirements of tables and interpolations while computerizing thermal process calculations using Stumbo’s method.