Solving the identification problems of Bolete origins based on multiple data processing: Take Boletus bainiugan as an example.

Wild porcini mushrooms have high nutritional value and are a kind of medicinal food. It is rich in protein and micronutrients needed by the human body, which is popular among consumers. In this paper, we take the problem of origin traceability of Boletus bainiugan as an example and use multiple data processing methods (preprocessing, feature extraction, and data fusion strategies) to establish partial least squares discriminant analysis and support vector machine models. In addition, a deep learning model (3DCOS-ResNet: three-dimensional correlation spectra combined with residual convolutional neural network model) is built to compare with the above two models. It was found that 3DCOS-ResNet was the best model to solve the Boletus baniugan origin traceability problem. Compared to the chemometrics model, it does not require complex processing of the data. The ideal identification effect can be achieved by directly utilizing the raw data, and it saves a lot of time and cost. [Display omitted] • Various data processing methods were used for model building. • SVM, PLS-DA and 3DCOS-ResNet models were built for comparison. • 3DCOS-ResNet was established under the data fusion strategy for the first time. [ABSTRACT FROM AUTHOR]