摘要： Mentha haplocalyx (mint) is a significant traditional Chinese medicine (TCM) listed in the Catalogue of “Medicinal and Food Homology”, therefore, its geographical origins (GOs) are critical to the medicinal and food value. Laser-induced breakdown spectroscopy (LIBS) is an advanced analytical technique for GOs certification, due to the fast multi-elemental analysis requiring minimal sample pretreatment. In this study, LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyses. The spectral data was analyzed by two chemometric algorithms, i.e. principal component analysis (PCA) and least squares support vector machines (LS-SVM). Specifically, the performance of LS-SVM with linear kernel and radial basis function (RBF) kernel was explored in sensitivity and robustness tests. Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance (a little inferior) in robustness test. Generally, LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel. The result indicated the potential for future applications in herbs and food, especially for in situ GOs applications of TCM authenticity rapidly.