Silver carp (Hypophthalmichthys molitrixi) was processed by sous-vide method at different temperatures (60, 65, 70, and 75°C). Then, the microbiological quality of the processed samples was monitored during cold storage (4°C) for 21 days. The target microorganisms were Enterobacteriaceae, Lactic Acid bacteria (LAB), Pseudomonas, Psychrotrophs, and total viable count (TVC). In samples processed at 75°C, the presence of Enterobacteriaceae, Pseudomonas and Psychrotrophs were not detectable up to 15 days of storage and lactic acid bacteria were not detectable even at the end of the storage period. A radial basis function neural network (RBFNN) model was established to predict the changes in the microbial content of silver carp. In this step, the relationship between processing temperature and storage duration on microbial growth was modeled by ANNs (artificial neural networks). The optimal ANN topology for modeling Enterobacteriaceae, Pseudomonas, and Psychrotroph contained 9 neurons in the hidden layer, but it contained 15 and 14 neurons for TVC and LAB, respectively. By experimenting with the temperature of -80°C, it was revealed that the obtained ANN model has a high potential for prediction.
Copyright: © 2023 Hosseini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.