Jintao Xue, Quanwei Yang, Yun Jing, Yufei Liu, Chunyan Li, Jing Yang, Yanfang Wu, Peng Li, Guangrui Wan
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Department of pharmacy, Wu Han NO.1 Hospital, Wuhan, Hubei Province, PR China.
Pharmacogn Mag. 2016 Jul-Sep;12(47):188-92. doi: 10.4103/0973-1296.186350.
Gegen (Puerariae Labatae Radix) is one of the important medicines in Traditional Chinese Medicine. The studies showed that Gegen and its preparation had effective actions for atherosclerosis.
Near-infrared (NIR) was used to develop a method for rapid determination of puerarin during percolation and concentration process of Gegen.
About ten batches of samples were collected with high-performance liquid chromatography analysis values as reference, calibration models are generated by partial least-squares (PLS) regression as linear regression, and artificial neural networks (ANN) as nonlinear regression.
The root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively.
The NIR model for the rapid analysis of puerarin can be used for on-line quality control in the percolation and concentration process.
Near-infrared was used to develop a method for on-line quality control in the percolation and concentration process of GegenCalibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regressionThe root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively. Abbreviations used: NIR: Near-Infrared Spectroscopy; Gegen: Puerariae Loabatae Radix; TCM: Traditional Chinese Medicine; PLS: Partial least-squares; ANN: Artificial neural networks; RMSEP: Root mean square error of validation; R2: Correlation coefficients; PAT: Process analytical technology; FDA: The Food and Drug Administration; Rcal: Calibration set; RMSECV: Root mean square errors of cross-validation; RPD: Residual predictive deviation; SLS: Straight Line Subtraction; MLP: Multi-Layer Perceptron; MSE: Mean square error.
葛根是中药中的重要药材之一。研究表明，葛根及其制剂对动脉粥样硬化有显著疗效。
采用近红外光谱法建立一种在葛根渗漉浓缩过程中快速测定葛根素含量的方法。
采集约十批样品，以高效液相色谱分析值为参照，分别采用偏最小二乘法（PLS）线性回归和人工神经网络（ANN）非线性回归建立校正模型。
PLS模型和ANN模型的预测均方根误差分别为0.0396和0.0365，相关系数（r²）分别为97.79%和98.47%。
该近红外光谱法建立的葛根素快速分析模型可用于渗漉浓缩过程的在线质量控制。
采用近红外光谱法建立一种在葛根渗漉浓缩过程中进行在线质量控制的方法。分别采用偏最小二乘法（PLS）线性回归和人工神经网络（ANN）非线性回归建立校正模型。PLS模型和ANN模型的预测均方根误差分别为0.0396和0.0365，相关系数（r²）分别为97.79%和98.47%。使用的缩写：NIR：近红外光谱；葛根：葛根；中药：传统中药；PLS：偏最小二乘法；ANN：人工神经网络；RMSEP：验证均方根误差；R²：相关系数；PAT：过程分析技术；FDA：食品药品监督管理局；Rcal：校正集；RMSECV：交叉验证均方根误差；RPD：剩余预测偏差；SLS：直线减法；MLP：多层感知器；MSE：均方误差。
