Discrimination of Zanthoxylum bungeanum Maxim through volatile aroma compounds analysis with artificial neural network

Abstract

Zanthoxylum bungeanum Maxim (ZBM), a special spice from Chinese different areas, have a widespread variation in quality and price. To avoid the commercial adulteration of ZBM, it is necessary to discriminate them from different areas. As volatile aroma compounds (VAC) have the potential to discriminate ZBM, electronic nose (E-nose) was used to preliminarily discriminate the VAC through sensor response analysis, radar chart analysis, and principal component analysis. Then, Gas chromatography-mass spectrometry (GC-MS) was utilized to identify VAC through hierarchical cluster analysis and quantitative analysis. Finally, artificial neural network (ANN) was employed to assess the accuracy of the discrimination of ZBM. As a result, we found that ZBM could be successfully discriminated between Chinese Sichuan and the other areas. Our findings would provide guidance for evaluating and predicting the variation of VAC of ZBM from different areas in further study. PRACTICAL APPLICATIONS: Zanthoxylum bungeanum Maxim (ZBM) is a traditional and important spice used in Sichuan cuisine especially hotpot, which are famous all over overseas. However, the ZBM from different producing areas bring various flavors, hampering the quality of Sichuan cuisine developing toward to standardization. Therefore, the authors in this work pursuit an effective way to distinguish the ZBM produced in Sichuan rather than in other province. According to the results of the present study, ZBM could be successfully discriminated between Chinese Sichuan and the other producing areas by using E-nose and GC-MS through artificial neural network. These findings would provide the guidance for evaluating the producing areas of ZBM to be whether or not Sichuan, which could offer the practical help in the purchase of the raw material in the supply chain. Besides, these also can be applied to predict the variation of volatile aroma compounds of the ZBM in the further study.

Keywords: E-nose; GC-MS; Zanthoxylum bungeanum Maxim; artificial neural network; volatile aroma compounds.