B. Vallejocordoba et al., PREDICTING MILK SHELF-LIFE BASED ON ARTIFICIAL NEURAL NETWORKS AND HEADSPACE GAS-CHROMATOGRAPHIC DATA, Journal of food science, 60(5), 1995, pp. 885-888
The usefulness of artificial neural networks (ANN) for milk shelf-life
prediction by multivariate interpretation of gas chromatographic prof
iles and flavor-related shelf-life was evaluated and compared to princ
ipal components regression (PCR). The training set consisted of dynami
c headspace gas chromatographic data collected during storage of paste
urized milk (input information for the neural network used to make a d
ecision) and its corresponding shelf-life (prediction or response). AN
N had better predictability than PCR. A standard error of the estimate
of 2 days in shelf-life resulting from regression analysis of experim
ental vs predicted values indicated a high predictability of ANN.