Feature Engineering in Discrimination of Herbal Medicines from Different Geographical Origins with Electronic Nose

Abstract

As pharmacists attach great significance to geographical origins of herbal medicines, cheap, nondestructive and convenient methods for discriminating herbal medicines originated from diverse regions are much in need. This work proposes a method of using electronic nose to discriminate herbal medicines from different origins. With 5 categories of herbal medicines and 3 to 4 geographical origins for each category, 8 pattern recognition algorithms prove the feasibility of the classification task and SVM, LDA and BP neural network have shown better classification accuracy. Additionally, feature engineering approaches are used to facilitate classification, showing that normalization based on each feature and each sensor and centralization prove to be better normalization approaches for classifiers; a proper degree of noise addition help classifiers get better generalization ability; finally, feature selection with SNR could lead to more efficient classifiers by selecting the most meaningful features and disregarding unnecessary features. This work provides insights for future herbal medicine evaluation based on electronic nose with better combinations of pattern recognition algorithms and feature engineering approaches for optimal classification performances.