Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks

• Published in: International journal of food science & technology 2020-05, Vol.55 (5), p.2080-2091
• Main Authors: Jia, Zhixin, Shi, Ce, Wang, Yanbo, Yang, Xinting, Zhang, Jiaran, Ji, Zengtao
• Format: Article
• Language: English
• Subjects: Aerobic bacteria; Amines; Ammonia; Aromatic compounds; Cold storage; electronic nose; Electronic noses; Fillets; Freshness; freshness index prediction; gas chromatograph ‐ ion mobility spectrometry; Gas chromatography; Ionic mobility; Neural networks; Nitrogen; Prediction models; Principal components analysis; Radial basis function; radial basis function neural networks; Salmon; salmon fillets; Spectrometry; Thiobarbituric acid; Volatiles
• ISSN: 0950-5423; 1365-2621
• DOI: 10.1111/ijfs.14451

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. 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.