Robust multi-class model constructed for rapid quality control of Cordyceps sinensis

Abstract

Cordyceps sinensis(C.sinensis) is a precious traditional Chinese herbal medicine with a wide variety of beneficial pharmacological effects. In recent years, due to the limitation of natural conditions and the over-harvesting, natural C.sinensis resources have been increasingly scarce and far from meeting the market demand. The phenomenon of counterfeit and substandard in the market is also becoming more and more serious. Thus the establishment of an identification method with good accuracy, high sensitivity and specificity is imminent. For this purpose, 3559 samples of C.sinensis were collected from different sources (part, region and phase) and we proposed the use of FTIR spectroscopy combined with chemometrics methods to construct robust and effective multi-class discriminant models. Cross-sectional comparison was performed between conventional machine learning methods (SVM, PLS-DA, LDA) and convolution neural network (CNN) under different classification tasks. Matthews correlation coefficient (MCC) was chosen as the optimized metrics for imbalanced classification problem. The best model for prediction of C.sinensis’s part was SVM combined with SNV as the pretreatment method (MCC was 0.8918 on cross-validation dataset and 0.9017 on independent testing dataset) and LDA with no pretreatment for prediction of C.sinensis’s region (MCC was 0.7721 on cross-validation dataset and 0.7332 on independent testing dataset). Classification of C.sinensis’s phase was less predictive (MCC was around 0.25) but was significantly superior to random prediction. The model performance of CNN is comparable to that of traditional machine learning methods and increasing training sample size might have the potential to further improve the model performance of CNN. Our results demonstrated that FTIR spectra combined with an optimized multivariable discriminant model could be used as an effective and sensitive method for rapid quality control of C.sinensis.