Validating an artificial neural network model of Leuconostoc mesenteroides in vacuum packaged sliced cooked meat products for shelf-life estimation.

摘要

An Artificial Neural Network-based predictive model (ANN) for Leuconostoc mesenteroides growth in response to temperature, pH, sodium chloride and sodium nitrite, developed by Garcia-Gimeno et al. [Int. J. Food Microbiol. (2005)] was validated on vacuum packed, sliced, cooked meat products and applied to shelf-life determination. Lag-time (Lag), growth rate (Gr), and maximum population density (yEnd) of L. mesenteroides, estimated by the ANN model, were compared to those observed in vacuum-packed cooked ham, turkey breast meat, and chicken breast meat stored at 10.5 degrees C, 13.5 degrees C and 17.7 degrees C, using bias and accuracy factors. The ANN model provided reliable estimates for the three kinetic parameters studied; with a bias factor of 1.09; 0.73 and 1.00 for Lag, Gr and yEnd, respectively and an accuracy factor of 1.26; 1.58 and 1.13 for Lag, Gr and yEnd, respectively. From the three kinetic parameters obtained by the ANN model, commercial shelf-life were estimated for each temperature and compared with the tasting panel evaluation. The commercial shelf life estimated microbiologically, i.e. times to reach 106.5 cfu/g, was shorter than the period estimated using sensory methods..