Rapid discrimination of Apiaceae plants by electronic nose coupled with multivariate statistical analyses

Since many Apiaceae plants, with antimicrobial activities, have similar characteristics, it is difficult to separate them from one another. The aim of this study is to distinguish different kinds of Apiaceae plants by an electronic nose (EN) and multivariate statistical analyses. The dynamic response of a metal oxide sensor array to Apiaceae plants showed that the response values and different kinds of Apiaceae plants were positively related. Atractylodis Macrocephalae Rhizoma (as the reference sample) and other nine different kinds of Apiaceae plants were measured. Multivariate statistical analyses, including linear discrimination analysis (LDA), principal component analysis (PCA), hierarchical clustering analysis (HCA) and artificial neural network (ANN), were employed. The result showed that these samples could be classified correctly by this method, which suggested that the EN system could be used as a simple and rapid technique for the discrimination of Apiaceae plants.