Distinguishing fresh and frozen-thawed beef using hyperspectral imaging technology combined with convolutional neural networks

Abstract

In this study, hyperspectral imaging (HSI) technology combined with a convolutional neural network (CNN) was used to distinguish fresh and frozen-thawed beef samples. After obtaining hyperspectral data of beef with different freezing/thawing cycles, the CNN was used to extract spectral features of all bands to compare with recursive feature elimination based on random forest and feature importance based on random forest. Then, eight characteristic wavelengths extracted by the first derivative-feature importance based on the random forest were used to establish the CNN model with an accuracy of 86.11%. Textural features of beef were used in the CNN model with early feature fusion of spectra and texture and late feature fusion of spectra and texture, and the CNN model using early feature fusion of spectra and texture showed more excellent results with an accuracy of 88.89%. Finally, beef samples in different states were well visualised. The research in the current study should provides a potential detection method for non-destructive and rapid tracing beef of different states.