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 analyzes. 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 least 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 tes...

中文翻译：

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LIBS和多元分析相结合对薄荷脑的地理真实性评价

薄荷（Mentha haplocalyx）（薄荷）是一种重要的中药（TCM），列在“药物和食品同源性”目录中，因此，其地域来源（GOs）对于药物和食品价值至关重要。激光诱导击穿光谱法（LIBS）是一种用于GOs认证的高级分析技术，这是因为快速的多元素分析需要最少的样品预处理。在这项研究中，LIBS结合多元统计分析，研究了从五个GO中提取的薄荷的LIBS数据。光谱数据通过两种化学计量学算法进行分析，即主成分分析（PCA）和最小二乘支持向量机（LS-SVM）。具体而言，在灵敏度和鲁棒性测试中探索了具有最小核和径向基函数（RBF）核的LS-SVM的性能。