RESUMO
The development of an analytical method for assessing pungency intensity and determining geographical origins is crucial for evaluating the quality of visually similar Zanthoxylum bungeanum pericarp (PZB). This study analyzed 210 PZB samples from 14 origins across China, focusing on origin adulteration identification and pungency intensity using a combination of differential pulse voltammetry (DPV) and machine learning algorithms. The artificial neural network (ANN) and K-nearest neighbor (KNN) algorithms provided the highest accuracy in origin identification (100 %) and adulteration detection (97.9 %) respectively. Moreover, the ANN excelled in predicting pungency intensity (R2 = 0.918). Assessment via feature importance analysis of DPV features revealed that segments of polyphenols (0.34-0.52 V and 1.0-1.2 V) and alkylamides (1.0-1.2 V) contributed significantly to the PZB pungency intensity. These findings highlight the potential of DPV as a reliable method for assessing the quality of PZB, and offer a promising solution for ensuring the geographical authenticity of this important crop.