Quality Classification of Panax notoginseng Based on Near Infrared Spectroscopy Combined with Multi-class Correlation Vector Machine

Panax notoginseng(P.notoginseng), also known as Tianqi in Chinese, is a rare medicinal herb. Panax notoginseng with different quality grades have distinct variance of medicinal values. In this paper, the near-infrared spectroscopy (NIRs) measurement technology is used to collect the spectral information of different grades. The quality identification model of Panax notoginsen is established by multi-classification correlation vector machine (mRVM). And three dimensionality reduction methods, the competitive adaptive reweighted algorithm (CARS) and the firefly algorithm (FA) and successive projections algorithm (SPA), are used to simplify the mRVM model. The results show that FA-mRVM, CARS-mRVM and SPA-mRVM can eliminate a large number of feature variables and filter out the effective feature variables. While FA-mRVM has better performance, with less numbers of feature variables and the highest accuracy. The correct identification rate, root mean square error and average cross-entropy error of the correction set and the prediction set are 100%, 0.0048, 0.0143 and 100%, 0.0046, 0.0141, respectively. Hence, NIRS combined with mRVM can be a useful, rapid, and nondestructive tool to discriminate quality grade of Panax notoginseng.