This study develops a predictive model for determining freshness of salmon fillets during cold storage at different temperatures using electronic nose combined with principal component analysis (PCA) and radial basis function neural networks (RBFNNs). The electronic nose sensed ammonia/amines, hydrocarbons, solvents and aromatics that increased during storage. The concentrations of the volatiles were compared with the increased thiobarbituric acid (TBA), total volatile basic nitrogen (TVB‐N), total aerobic bacteria count (TAC) and decreased of sensory assessments (SA). Gas chromatograph–ion mobility spectrometry analysis confirmed the changes in gas species. RBFNNs and PCA were used to establish predictive models and the relative errors of TBA, TVB‐N and TAC by the PCA‐RBFNNs model were all within ±10% and SA was within ±15%. These results suggest that the PCA‐RBFNNs model can be used to predict changes in the freshness of salmon fillets stored at −2 to 10 °C.

中文翻译：

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结合径向基函数神经网络的电子鼻系统无损确定不同温度下鲑鱼片的保鲜性

这项研究开发了一种预测模型，该模型可使用电子鼻，主成分分析（PCA）和径向基函数神经网络（RBFNN）来确定不同温度下冷藏鲑鱼片的新鲜度。电子鼻感测到在存储过程中增加的氨/胺，碳氢化合物，溶剂和芳烃。将挥发物的浓度与增加的硫代巴比妥酸（TBA），总挥发性碱性氮（TVB-N），总需氧菌数量（TAC）和感官评估（SA）降低进行了比较。气相色谱-离子迁移谱分析证实了气体种类的变化。RBFNN和PCA用于建立预测模型，PCA-RBFNNs模型对TBA，TVB-N和TAC的相对误差均在±10％之内，SA在±15％之内。