Analysis of the Impact of Data Augmentation on the Performance of Deep Learning Models in Multispectral Food Authenticity Identification

Food authenticity is a significant concern in the meat industry, demanding effective detection methods. This study explores the use of multispectral imaging (MSI) and deep learning for meat adulteration detection. We evaluate different deep learning models using transfer learning and preprocessing techniques in a multi-level adulteration classification task. In addition, we propose a novel approach called one-band mixed augmentation for band selection in MSI data, which outperforms traditional reflectance-based feature selection and enhances model robustness. Furthermore, employing the ninecrop approach for dataset augmentation improved the accuracy from 0.63 to 0.74 for DenseNet201 model without transfer learning. This research contributes to advancing food safety assessment practices and provides insights into the application of deep learning for preventing food adulteration. The proposed one-band mixed augmentation approach offers a novel strategy for handling band selection challenges in MSI data analysis.