Technical exploration of the origins, storage periods and species identification of Boletus bainiugan

Abstract

To prevent fraud in Boletus bainiugan commodities, this study provides the market with two fast and stable identification models for accurate identification of Boletus bainiugan origins, storage periods and species. Partial least squares discrimination analysis (PLS-DA), Support vector machine (SVM), Residual convolutional neural network (ResNet) and Data-driven soft independent modeling of class analogy (DD-SIMCA) models were built by combining with Fourier transform near-infrared spectroscopy (FT-NIR). The results show that the ResNet model is significant in solving the Boletus bainiugan origin identification problems. The ResNet model had the best performance and highest accuracy compared to the PLS-DA and SVM models. The DD-SIMCA model was the preferred method for the one-class classification problem, achieving an accuracy of over 96% for the Boletus bainiugan storage period and species identification. Non-target class classification accuracy reached 100%. In summary, FT-NIR combined with ResNet and DD-SIMCA models were able to solve the related identification problems of Boletus bainiugan with more satisfactory results.