Small-scale districts identification of Boletus bainiugan from Yunnan province of China based on residual convolutional neural network continuous classification models

Recent studies on the origin of food rarely focus on the source of counties or even small-scale districts, but traceability of small-scale districts of food is the research trend and difficulty for future research. The geographical origin of Boletus bainiugan is of great significance to its safety and economic value. The aim of this study was to provide a new way for the traceability of B. bainiugan in small-scale districts using synchronous two-dimensional correlation spectroscopy (2D-COS) images combined with residual convolutional neural network (ResNet) model. In our study, 550 wild-grown B. bainiugan mushrooms were collected in 28 small-scale districts and their fourier transform near infrared (FT-NIR) were collected. First, samples from nine regions in Yunnan province were identified, and then samples from Chuxiong, Kunming and Yuxi were identified. Most of these models had a 100