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Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks
Author(s) -
Jia Zhixin,
Shi Ce,
Wang Yanbo,
Yang Xinting,
Zhang Jiaran,
Ji Zengtao
Publication year - 2020
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/ijfs.14451
Subject(s) - electronic nose , principal component analysis , fillet (mechanics) , chemistry , thiobarbituric acid , food science , biological system , artificial intelligence , biochemistry , materials science , computer science , biology , oxidative stress , composite material , lipid peroxidation
Summary 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.