Abstract The feasibility of combining near-infrared (NIR) spectroscopy with chemometrics was explored to discriminate ginseng geographical origins. A total of 326 ginseng samples from three major ginseng producing regions were prepared and analyzed. After spectral pre-treatment, principal component analysis (PCA) was used for a preliminary analysis. Three algorithms, i.e., partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy classification (SIMCA) and successive projection algorithms-linear discriminant analysis (SPA-LDA), were applied to build models to discriminate origins of samples. The results showed that ginseng could be classified based on geographical origins with pattern recognition. By comparison, SPA-LDA is better than PLS-DA and SIMCA. It indicates that NIR spectroscopy combined with SPA-LDA is a potential and feasible tool for identifying ginseng according to geographical origin, but the effectiveness needs to be verified further.

中文翻译：

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近红外光谱和模式识别技术对人参地理来源的鉴别

摘要 探讨了将近红外 (NIR) 光谱与化学计量学相结合以区分人参地理来源的可行性。共制备和分析了来自三个主要人参产区的 326 份人参样品。光谱预处理后，使用主成分分析（PCA）进行初步分析。三种算法，即偏最小二乘判别分析（PLS-DA）、类类比分类的软独立建模（SIMCA）和连续投影算法-线性判别分析（SPA-LDA），被应用于建立模型来判别来源。样品。结果表明，人参可以通过模式识别基于地理来源进行分类。相比之下，SPA-LDA 优于 PLS-DA 和 SIMCA。