Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish.

Electronic nose (E-nose), electronic tongue (E-tongue) and colorimeter combined with data fusion strategy and different machine learning algorithms (artificial neural network, ANN; extreme gradient boosting, XGBoost; random forest regression, RFR; support vector regression, SVR) were applied to quantitatively assess and predict the freshness of horse mackerel (Trachurus japonicus) during the 90-day frozen storage. The results showed that the fusion data of the E-nose, E-tongue and colorimeter could contain more information (with a total variance contribution rate of 94.734 %) than that of the independent one. ANN, RFR and XGBoost showed good performance in predicting biochemical indexes with the R (the square correlation coefficient of the Test set) ≥ 0.929, 0.936, 0.888, respectively, while SVR models showed a bad performance (R ≤ 0.835). In addition, among the established quantitative models, the RFR model had the best prediction effect on K value (freshness index) with R of 0.936, ANN model had the highest fitting degree in predicting carbonyl content (protein oxidation degree) with R of 0.978, XGBoost model had the best performance in predicting the TBA value (lipid oxidation degree) with R of 0.994, RFR model was the best strategy for predicting Ca-ATPase activity (protein denaturation degree) with R of 0.969. The results demonstrated that the freshness of frozen fish can be effectively evaluated and predicted by the combination of electronic sensor fusion signals.