Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China.
Molecules. 2019 Jul 13;24(14):2559. doi: 10.3390/molecules24142559.
Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. var. is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis-PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild .
中药材及中成药的质量控制和疗效评价与产地密切相关。var. 分布广泛，在世界各地广为人知。本研究应用傅里叶变换中红外（FT-MIR）和近红外（NIR）两种光谱技术，结合低、中、高数据融合策略，对 196 个野生 var. 样本的地理起源进行追溯。偏最小二乘判别分析（PLS-DA）和随机森林（RF）用于建立分类模型。特征变量提取（主成分分析-PCA）和重要变量选择模型（递归特征消除和 Boruta）用于地理起源追溯，前者的分类能力优于后者。FT-MIR 光谱比 NIR 光谱贡献更大。此外，基于主成分（PCs）特征变量提取的高水平数据融合结果令人满意，准确率达到 100%。因此，FT-MIR 和 NIR 信号的数据融合可有效识别野生 var. 的地理起源。
