Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat

In the meat industry, ensuring product authenticity is important due to its impact on human diet, processing chain as well as for ensuring fair trade practices. This study aims at investigating the potential of snapshot hyperspectral imaging (HSI) and deep learning approach for red-meat classification by combining the spectral and spatial features of HSI data of red-meat products. Moreover, this study provides a comprehensive comparison between snapshot HSI and a standard line-scanning HSI system. A novel deep 3D convolution neural network (3D-CNN) model is proposed for extracting the combined features and then classifying the meat in the HSI image. An innovative graph-based post-processing method is also proposed for enhancing the prediction of the 3D-CNN approach. Results show that the 3D-CNN approach significantly enhanced the overall accuracy of state-of-the-art models. Despite limitations in the spectral information from snapshot HSI, the 3D-CNN model shows robustness in classifying red-meat with an overall accuracy of 96.9% and 97.1% for Near-Infrared (NIR) and visible (VIS) snapshot HSI, respectively. A comparison between the HSI systems revealed that state-of-the-art models are insufficient for achieving accurate classification with snapshot HSI data while the 3D-CNN achieves excellent classification accuracy on all systems by utilizing the whole image information. The current study opens the door for further research towards real-time red-meat authenticity and mobile HSI systems using snapshot HSI and deep learning models.