Modeling of pH and Acidity for Industrial Cheese Production

Abstract

A three-layer feedforward neural network was successfully used to model and predict the pH of cheese curd at various stages during the cheese-making process. An extended database, containing more than 1800 vats over 3 yr of production of Cheddar cheese with eight different starters, from a large cheese plant was used for model development and parameter estimation. Neural network models were developed with inputs selected among 33 quantitative and qualitative process variables for final pH of cheese, pH at cutting, and acidity at whey drawing-off and at pressing. In all cases, very high correlation coefficients, ranging from 0.853 to 0.926, were obtained with the validation data. A sensitivity analysis of neural network models allowed the relative importance of each input process variable to be identified. The sensitivity analysis in conjunction with a priori knowledge permitted a significant reduction in the size of the model input vector. A neural network model using only nine input process variables was able to predict the final pH of cheese with the same accuracy as for the complete model with 33 original input variables. This significant decrease in the size of neural networks is important for applications of process control in cheese manufacturing.