Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
Department of Food Science, Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
PLoS One. 2023 Mar 29;18(3):e0246708. doi: 10.1371/journal.pone.0246708. eCollection 2023.
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.
银鲫(Hypophthalmichthys molitrixi)采用低温慢煮法在不同温度(60、65、70 和 75°C)下进行加工。然后,在 4°C 的冷藏过程中监测加工样品的微生物质量,持续 21 天。目标微生物为肠杆菌科、乳酸菌(LAB)、假单胞菌、嗜冷菌和总活菌数(TVC)。在 75°C 加工的样品中,在储存 15 天内未检测到肠杆菌科、假单胞菌和嗜冷菌,甚至在储存期结束时也未检测到乳酸菌。建立了径向基函数神经网络(RBFNN)模型来预测银鲫微生物含量的变化。在这一步中,通过神经网络(ANNs)对加工温度和储存时间对微生物生长的关系进行建模。用于建模肠杆菌科、假单胞菌和嗜冷菌的最佳 ANN 拓扑结构包含 9 个隐藏层神经元,但对于 TVC 和 LAB,分别包含 15 和 14 个神经元。通过实验 -80°C 的温度,结果表明所获得的 ANN 模型具有很高的预测潜力。