Nondestructive freshness prediction of large yellow croaker (Pseudosciaena crocea) using computer vision and machine learning techniques based on pupil color.

Conventional methods for evaluating of fish freshness based on physiological and biochemical methods are often destructive, complicated, and costly. This study aimed to predict the freshness of large yellow croaker which was sampled every second day in 9 consecutive days at 4°C, using computer vision technology combined with pupil color parameters and different machine learning algorithms (back propagation neural network, BPNN; radial basis function neural network; support vector regression; and random forest regression, RFR). In the process of model building, the RFR model provided the most accurate prediction for the value of total volatile basic nitrogen (TVB-N), with the R-square of the test set ( $R_p^2$ ) of 0.993. The BPNN model exhibited the best fit for predicting the value of thiobarbituric acid (TBA), with $R_p^2$ of 0.959. Additionally, the RFR model was the most effective in forecasting total viable count (TVC), with $R_p^2$ of 0.935. After validation, the root mean square error values of the RFR model for predicting TVB-N value, TBA value, and TVC value were the lowest, which were 0.764, 0.067, and 0.219, respectively. It demonstrated the applicability and good predictive performance of the RFR model for predicting biochemical and microbiological indicators. These findings also demonstrated that monitoring the changes in pupil color could successfully predict the freshness of chilled fish. PRACTICAL APPLICATION: Application Scenario: Quality inspectors detect changes in the freshness of large yellow croaker in real time from the beginning of distribution to the selling site.