Identification of chinese herbal medicines from zingiberaceae family using feature extraction and cascade classifier based on response signals from E-Nose

Abstract

Identification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method for identification of ten different species of CHMs from Zingiberaceae family based on their response signals from E-nose. Ten Zingiberaceae CHMs were measured and their maximum response values were analyzed by principal component analysis (PCA). Result shows that E Zhu (Curcuma phaeocaulis Val.) and Yi Zhi (Alpinia oxyphylla Miq.) could not be distinguished completely by PCA. Two solutions were proposed: (i) using BestFirst+CfsSubsetEval (BC) method to extract more discriminative features to select sensors with higher contribution rate and remove the redundant signals; (ii) employing a novel cascade classifier with two stages to enhance the distinguishing-positive rate (DPR). Based on these strategies, six features were extracted and used in different stages of the cascade classifier with higher DPRs.