Superiority of two-dimensional correlation spectroscopy combined with ResNet in species identification of bolete

Food safety is an important topic of social concern. There are many species of bolete on the market, and shoddy products often occur. This phenomenon not only disrupts the market order, infringes on the rights and interests of consumers, and even endangers the lives of consumers. Therefore, it is of great significance to find a comprehensive, efficient and modern technology to identify and evaluate it in order to ensure its quality and safety. Spectroscopy analysis is fast, nondestructive and green. In our research, a fast and reliable bolete species identification method was established by combining Fourier transform near infrared spectroscopy (FT-NIR) with random forest (RF) method. In order to adapt to the development of the times, a practical method beyond traditional spectral analysis is established. Therefore, we establish a residual convolution neural network model (ResNet). The results show that the classification accuracy of the model is low and the out of bag error (OOB) error is high. After data preprocessing, the accuracy of RF model can be significantly improved. ResNet model has absolute advantages in bolete species identification. It is hardly affected by factors such as data type and sample size. Its overall recognition ability is obviously better than RF model. By comparing the identification accuracy and market application prospect of the two models, this research believes that ResNet model is more suitable for the identification of bolete species. In addition, the method can also be extended to the further study of other food, medicinal plants and agricultural products.