Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR

Introduction Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. Objective This research aimed to identify the geographical traceability for different parts of W. cocos. Methods The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm(-1), 6300-5150 cm(-1) and 4450-4050 cm(-1) of original spectra and the second-order derivative (SD) spectra preprocessed. Results From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. Conclusion Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

Automated summary

This study successfully identified the geographical traceability of different parts of Wolfiporia cocos using two-dimensional correlation spectroscopy and multivariate statistical analysis methods such as residual convolutional neural network and partial least square discriminant analysis. The results showed that synchronous SD 2DCOS was more suitable for identifying complex mixed systems. These methods can provide technical support for the identification and quality evaluation of herbal geographical origins.