Identification of Panax Notoginseng Powder in Different Parts Based on the Electronic Nose and Time-Domain Feature Extraction

Abstract

To realize the non-destructive identification of Panax notoginseng powder in different parts, this research proposes a non-destructive identification method based on the electronic nose and time-domain feature extraction. First, the electronic nose technology combined with statistical analysis method was used to collect and extract nine time-domain characteristics of the response information of Panax notoginseng whole root powder, tap root powder, rhizome powder, and fibrous powder, including the data at 110 s, the mean value between 101–120 s, the maximum value, minimum value, integral value, differential value, skewness factor, kurtosis factor, and standard deviation between 0–120 s. Next, three classical feature selection method was used to reduce the data dimension. Subsequently, the classification models of support vector machine (SVM), least-square support vector machine (LSSVM), and extreme learning machine (ELM) were established based on original data, multi-feature data, and feature selection data. Finally, the Grey Wolf Optimization (GWO) algorithms were introduced to optimize the parameters of the classification model. The results show that the GWO-CARS-LSSVM achieved the best modeling effect, and the classification accuracy on the test set was 97.92%. This study provides a theoretical basis and technical support for rapid identification of adulteration of Panax notoginseng powder.