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 AE10% and SA was within AE15%. These results suggest that the PCA-RBFNNs model can be used to predict changes in the freshness of salmon fillets stored at À2 to 10°C.Keywords Cold storage, electronic nose, freshness index prediction, gas chromatograph -ion mobility spectrometry, radial basis function neural networks, salmon fillets.

BACKGROUND Fish spoilage leads to an increase in the pH value of the fish. A colorimetric pH indicator can be used to monitor fish spoilage and has been exploited in intelligent packaging because of its simplicity, practicality and low cost. The aim of this study was to develop two pH‐indicator films comprising starch (S), tara gum (TG), polyvinyl alcohol (PVA) and anthocyanins extracted from blueberry peel and the films were then used to monitor the freshness of tilapia fillets during storage at 25 and 4 °C. RESULTS The ultraviolet‐visible (UV‐visible) spectra and color of anthocyanins changed within pH 2–10. Fourier‐transform infrared spectroscopy, UV‐visible spectrophotometry and scanning electron microscopy certified that blueberry peel extract (BPE) had been introduced into the S/PVA and TG/PVA matrices. Visual color changes in the films occurred at pH 2–8. A freshness application test was conducted in tilapia fillets stored at 4 and 25 °C, and visual color changes in the films were observed. The TG/PVA/BPE film had a greater color difference (ΔE) from pink and transparent to dark yellow at 25 °C and to dark purple at 4 °C than ΔE of S/PVA/BPE film, which sufficiently correlated with the change of total volatile base nitrogen (TVB‐N) and total aerobic counts (TACs) of fillets. CONCLUSION It can be concluded that the color changes of TG/PVA/BPE films were corresponded with TVB‐N and TAC values of tilapia fillets, which presented great potential as a visual package label to monitor fish freshness at ambient and chilled temperatures. © 2020 Society of Chemical Industry

BACKGROUND Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB‐N), total viable counts (TVCs), K value, and sensory assessment (SA). RESULTS The TBA, TVB‐N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB‐N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models. CONCLUSION RBFNN is a promising method for predicting the freshness of salmon fillets stored at −2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry

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