Rapid classification of the freshness grades of sea bass (Lateolabrax japonicus) fillets using a portable Raman spectrometer with machine learning method

Freshness is a key indicator for assessing the nutritional and safety qualities of fish products. This study aims to apply the convolutional neural network (CNN) for modeling the Raman spectra data, for the rapid classification of the freshness grades of sea bass (Lateolabrax japonicus) fillets. Results suggested that the classification accuracy of CNN model was superior to principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on Raman spectra data. In addition, analysis of variance (ANOVA) was demonstrated effective for extracting the Raman spectral features for CNN modeling. Based on the feature-selected CNN model using ANOVA, the average accuracy for identifying sea bass fillets freshness was improved to 90.6% and the testing time was reduced to 0.44 s. In particular, the classification accuracy for the second-grade freshness was significantly improved to 80.2% compared with the CNN model with full-band Raman spectra. This research provided an exemplar for combining the portable Raman spectrometer with deep learning for the rapid and non-destructive analysis of food quality.