Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks
Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks
复制标题
电子鼻系统结合径向基函数神经网络无损测定不同温度存储过程中三文鱼片新鲜度
DOI：
10.1111/ijfs.14451
复制
发表时间：
2020-01-20
影响因子：
3.3
通讯作者：
Ji, Zengtao
中科院分区：
文献类型：
--
作者：
Jia, Zhixin;Shi, Ce;Ji, Zengtao
本研究开发了一种预测模型，利用电子鼻结合主成分分析（PCA）和径向基函数神经网络（RBFNN）来确定不同温度冷藏期间鲑鱼片的新鲜度。电子鼻检测到储存期间增加的氨/胺、碳氢化合物、溶剂和芳香族化合物。将挥发物浓度与增加的硫代巴比妥酸（TBA）、总挥发性碱氮（TVB-N）、总需氧菌数（TAC）和减少的感官评估（SA）进行比较。气相色谱-离子迁移谱分析证实了气体种类的变化。采用RBFNNs和PCA建立预测模型，PCA-RBFNNs模型的TBA、TVB-N和TAC相对误差均在+/-10%以内，SA在+/-15%以内。这些结果表明，PCA-RBFNNs 模型可用于预测储存在 -2 至 10 摄氏度的三文鱼片的新鲜度变化。
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 degrees C.