School of Pharmacy, Xinxiang Medical University, Xinxiang 453002, Henan Province, PR China.
West China School of Pharmacy, Sichuan University, Chengdu 610041, Sichuan Province, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Jan 5;188:611-618. doi: 10.1016/j.saa.2017.07.053. Epub 2017 Jul 29.
Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).
近红外光谱（NIRS）最初用于开发一种快速同时测定黄连 4 个部位（根茎、须根、茎和叶）中 5 种活性生物碱（小檗碱、黄连碱、巴马汀、表小檗碱和药根碱）的方法。共收集 100 个来自 4 个主要产地的样本进行研究。以 HPLC 分析值为校准参考，采用两种不同的建模方法（偏最小二乘（PLS）回归作为线性回归和人工神经网络（ANN）作为非线性回归）对 5 种标记成分进行定量分析。结果表明，所建立的两种类型的模型对 5 种活性生物碱均具有稳健、准确和重复性，ANN 模型更适合测定小檗碱、黄连碱和巴马汀，而 PLS 模型更适合分析表小檗碱和药根碱。最优模型的性能如下：小檗碱、黄连碱、巴马汀、表小檗碱和药根碱的相关系数（R）分别为 0.9958、0.9956、0.9959、0.9963 和 0.9923；验证的均方根误差（RMSEP）分别为 0.5093、0.0578、0.0443、0.0563 和 0.0090。此外，为了综合开发和利用黄连植物资源，还利用所建立的 NIR 模型分析了黄连 4 个部位和 4 个主要产地 5 种活性生物碱的含量。这项工作表明，NIRS 可能是一种很有前途的方法，可以作为常规筛选，用于离线快速分析或在线质量评估中药（TCM）。
