Quality and quantity classification models of Fructus Amomi applying electronic nose with multiple mathematical statistics methods / 国际药学研究杂志

ABSTRACT

Objective Fructus Amomi (Sharen) is derived from the dry ripe fruit of Amomum villosum Lour., A.villosum Lour. var. xanthioides T.L. Wu et Senjen and A.longiligulate T.L.Wu, which is widely utilized for its clinic effects on digestive system. However, Fructus Amomi from different species and habitats, possessing different quality, is difficult to identify. In this study, we aim to develop a simple, rapid and reliable method for authentication of Fructus Amomi. Method- Twenty-five batches of samples of Fructus Amomi were collected and electronic nose was introduced into analyzing their odor with multiple mathematical statistics methods. Naïve bayes network (NBN), radical basis function (RBF) and random forest (RF) were applied to establish different classifiers while BestFirst+CfsSubsetEval (BC) was used to screen the attributes for searching sensor array with higher contributions. Results- Firstly, after attribute-screening via BC, the established discriminative models via NBN, RBF and RF could successfully identify genuine and non-genuine samples, presenting correct judging ratios of 78% and 84% through ten-fold cross validation and external test set validation, respectively. Besides, quantity predictive models were constructed as well. In case of content of bornyl acetate, one of the effective components in Fructus Amomi, values were higher than 3.5 mg/g and lower than 1.8 mg/g with sensor response of 0.04 and 0.03, respectively. Conclusion In this paper, quality discriminative model and quantity predictive model of Fructus Amomi were established via electronic nose and multiple mathematical statistics methods. It indicates that electronic nose could be a promising method for quality evaluation of Chinese material medica.