Rapid Detection of Endogenous Impurities in Walnuts Using Near-Infrared Hyperspectral Imaging Technology

Impurities in walnut-based products pose a significant risk to human health, necessitating the development of rapid and efficient methods for detecting endogenous impurities in walnut shell-breaking materials. To address this issue, this study aims to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) in conjunction with conventional chemometric methods and deep learning models. First, biochemical experiments demonstrated differences in the total phenolic and flavonoid contents of different walnut kernels. An improved support vector machine model based on the butterfly optimization algorithm was constructed for classification using an NIR dataset of endogenous impurities in walnuts, achieving an accuracy of 96.12%. In addition, this study proposed the WT-NIRSNet model, a deep neural network with an additional attention mechanism, and compared it to a traditional chemometric approach. The study demonstrated an accuracy of 99.03% on the test set using WT-NIRSNet to solve the fast classification problem of endogenous impurities in walnuts. It can achieve desirable classification results using only raw spectral data without complex preprocessing operations and is superior to traditional chemometric models. Therefore, the NIR-HSI technique combined with deep neural networks has a significant potential for detecting endogenous impurities in walnuts.