Li Kaiyue, Wang Weiying, Liu Yanping, Jiang Su, Huang Guo, Ye Liming
Department of Pharmacy, West China School of Pharmacy, Sichuan University, Chengdu 610041, PR China.
Department of Application, Sichuan Vspec Technologies Co., Ltd. Chengdu 610000, PR China.
Pharmacogn Mag. 2017 Apr-Jun;13(50):332-337. doi: 10.4103/pm.pm_416_16. Epub 2017 Apr 18.
The active ingredients and thus pharmacological efficacy of traditional Chinese medicine (TCM) at different degrees of parching process vary greatly.
Near-infrared spectroscopy (NIR) was used to develop a new method for rapid online analysis of TCM parching process, using two kinds of chemical indicators (5-(hydroxymethyl) furfural [5-HMF] content and 420 nm absorbance) as reference values which were obviously observed and changed in most TCM parching process.
Three representative TCMs, Areca ( L.), Malt ( L.), and Hawthorn ( Bge.), were used in this study. With partial least squares regression, calibration models of NIR were generated based on two kinds of reference values, i.e. 5-HMF contents measured by high-performance liquid chromatography (HPLC) and 420 nm absorbance measured by ultraviolet-visible spectroscopy (UV/Vis), respectively.
In the optimized models for 5-HMF, the root mean square errors of prediction (RMSEP) for Areca, Malt, and Hawthorn was 0.0192, 0.0301, and 0.2600 and correlation coefficients () were 99.86%, 99.88%, and 99.88%, respectively. Moreover, in the optimized models using 420 nm absorbance as reference values, the RMSEP for Areca, Malt, and Hawthorn was 0.0229, 0.0096, and 0.0409 and were 99.69%, 99.81%, and 99.62%, respectively.
NIR models with 5-HMF content and 420 nm absorbance as reference values can rapidly and effectively identify three kinds of TCM in different parching processes. This method has great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online analysis and quality control in TCM industrial manufacturing process.
Near-infrared spectroscopy.(NIR) was used to develop a new method for online analysis of traditional Chinese medicine.(TCM) parching processCalibration and validation models of Areca, Malt, and Hawthorn were generated by partial least squares regression using 5.(hydroxymethyl) furfural contents and 420.nm absorbance as reference values, respectively, which were main indicator components during parching process of most TCMThe established NIR models of three TCMs had low root mean square errors of prediction and high correlation coefficientsThe NIR method has great promise for use in TCM industrial manufacturing processes for rapid online analysis and quality control. NIR: Near-infrared Spectroscopy; TCM: Traditional Chinese medicine; Areca: L.; Hawthorn: Bge.; Malt: L.; 5-HMF: 5-(hydroxymethyl) furfural; PLS: Partial least squares; D: Dimension faction; SLS: Straight line subtraction, MSC: Multiplicative scatter correction; VN: Vector normalization; RMSECV: Root mean square errors of cross-validation; RMSEP: Root mean square errors of validation; : Correlation coefficients; RPD: Residual predictive deviation; PAT: Process analytical technology; FDA: Food and Drug Administration; ICH: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use.
中药在不同炒制程度下的有效成分及药理功效差异很大。
采用近红外光谱法(NIR),以两种在大多数中药炒制过程中明显可见且会发生变化的化学指标(5-羟甲基糠醛[5-HMF]含量和420nm吸光度)作为参考值,建立一种快速在线分析中药炒制过程的新方法。
本研究选用了三种代表性中药,即槟榔(Areca L.)、麦芽(Malt L.)和山楂(Hawthorn Bge.)。通过偏最小二乘法回归,分别基于两种参考值建立NIR校准模型,即通过高效液相色谱法(HPLC)测定的5-HMF含量以及通过紫外可见光谱法(UV/Vis)测定的420nm吸光度。
在以5-HMF为参考值的优化模型中,槟榔、麦芽和山楂的预测均方根误差(RMSEP)分别为0.0192、0.0301和0.2600,相关系数( )分别为99.86%、99.88%和99.88%。此外,在以420nm吸光度为参考值的优化模型中,槟榔、麦芽和山楂的RMSEP分别为0.0229、0.0096和0.0409, 分别为99.69%、99.81%和99.62%。
以5-HMF含量和420nm吸光度为参考值的NIR模型能够快速有效地识别不同炒制过程中的三种中药。该方法极有希望取代当前主观的颜色判断以及耗时的HPLC或UV/Vis方法,适用于中药工业生产过程中的快速在线分析和质量控制。
采用近红外光谱法(NIR)建立一种在线分析中药炒制过程的新方法。分别以5-羟甲基糠醛含量和420nm吸光度为参考值,通过偏最小二乘法回归建立槟榔、麦芽和山楂的校准及验证模型,这两种指标是大多数中药炒制过程中的主要指标成分。所建立的三种中药的NIR模型预测均方根误差低,相关系数高。NIR方法在中药工业生产过程的快速在线分析和质量控制方面具有很大的应用前景。NIR:近红外光谱法;TCM:中药;槟榔:Areca L.;山楂:Hawthorn Bge.;麦芽:Malt L.;5-HMF:5-羟甲基糠醛;PLS:偏最小二乘法;D:维度系数;SLS:直线减法;MSC:多元散射校正;VN:向量归一化;RMSECV:交叉验证均方根误差;RMSEP:验证均方根误差; :相关系数;RPD:剩余预测偏差;PAT:过程分析技术;FDA:食品药品监督管理局;ICH:人用药品注册技术要求国际协调会议