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On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

机译:使用近红外光谱和不同的PLS算法在线监测复方双花口服液的提取过程

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An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2 mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R-2) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines. (C) 2015 Published by Elsevier B.V.
机译:针对复方双花口服液(FSOS)的提取工艺,建立了一种具有适当的多元校正方法的在线近红外光谱监测方法。通过两个光纤探头收集在线NIR光谱,这些探头被设计为通过2 mm法兰传输NIR辐射。比较使用偏最小二乘(PLS),区间PLS(iPLS)和协同区间PLS(siPLS)算法来建立校准回归模型。在提取过程中,利用近红外光谱法确定绿原酸(CA)含量,总酚酸含量(TPC),总类黄酮含量(TFC)和可溶性固体含量(SSC)的浓度。参考方法采用高效液相色谱法(HPLC),紫外分光光度法(UV)和干燥失重法。实验结果表明,与PLS和iPLS相比,siPLS模型的性能最佳。 AC,TPC,TFC和SSC的校准模型具有较高的(R-2)测定系数值(0.9948、0.9992、0.9950和0.9832),并且交叉验证的均方根误差(RMSECV)低(0.0113、0.0341、0.1787)和1.2158),表明参考值与NIR预测值之间具有良好的相关性。总体结果表明,在线检测方法在实际应用中是可行的,对于监测FSOS与其他中成药的混合煎煮过程具有重要的价值。 (C)2015由Elsevier B.V.发布

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