近红外漫反射光谱法快速测定麦冬多指标成分含量

Rapid Determination of Multi-index Components in Ophiopogonis Radix by Near-infrared Spectroscopy

目的利用近红外光谱(NIR)技术建立麦冬5种代表性成分的偏最小二乘(PLS)定量分析模型,为快速控制麦冬药材质量提供参考。方法收集3个麦冬主产区的120批样品,采集NIR原始光谱,采用高效液相色谱-蒸发光散射检测器(HPLC-ELSD)同时测定麦冬皂苷B、麦冬皂苷D、麦冬皂苷D′、甲基麦冬二氢高异黄酮A、甲基麦冬二氢高异黄酮B含量的化学参考值,以决定系数(R^(2))、校正均方差(RMSEC)、预测均方差(RMSEP)和留一法交叉验证均方差(RMSECV)为指标,选择最佳光谱预处理方法、最佳波段及最佳因子数,建立PLS模型。结果麦冬皂苷B的PLS模型R^(2)=0.9702,RMSEC=0.5207,RMSEP=0.5541,交叉验证R^(2)=0.9430,RMSECV=0.6465,外部验证平均回收率为101.63%;麦冬皂苷D的PLS模型R^(2)=0.9498,RMSEC=0.6627,RMSEP=0.3157,交叉验证R^(2)=0.9206,RMSECV=0.3583,外部验证平均回收率为102.07%;麦冬皂苷D’的PLS模型R^(2)=0.9804,RMSEC=0.0853,RMSEP=0.4155,交叉验证R^(2)=0.9516,RMSECV=0.6933,外部验证平均回收率为99.44%;甲基麦冬二氢高异黄酮A的PLS模型R^(2)=0.9593,RMSEC=0.8122,RMSEP=0.5674,交叉验证R^(2)=0.9395,RMSECV=0.7444,外部验证平均回收率为103.11%;甲基麦冬二氢高异黄酮B的PLS模型R^(2)=0.9408,RMSEC=0.6451,RMSEP=0.7476,交叉验证R^(2)=0.9283,RMSECV=0.8849,外部验证平均回收率为101.59%。校正集与验证集样品均匀分布在回归线两侧,表明模型预测值与实测值之间具有较好的相关性,模型预测性能较为理想。结论本研究建立的定量模型操作简单,预测结果准确,可用于麦冬5种成分含量的快速测定。

Objective To establish a partial least squares(PLS)quantitative analysis model of five representative components in Ophiopogonis Radix by using near-infrared spectroscopy(NIR)technology;To provide a reference for the rapid quality control of Ophiopogonis Radix.Methods Totally 120 batches of Ophiopogonis Radix from three main producing areas were collected,and the original NIR spectra were measured.The chemical reference values of ophiopogonin B,ophiopogonin D,ophiopogonin D',methylophiopogonanone A and methylophiopogonanone B in Ophiopogonis Radix were simultaneously determined by HPLC-ELSD.The optimal spectral preprocessing method,the optimal NIR bands and the optimal number of factors were selected to establish a PLS quantitative analysis model,with R^(2),RMSEC,RESEP and RMSECV as indexes.Results The parameters of the established PLS model for ophiopogonin B were as follows:R^(2)=0.9702,RMSEC=0.5207,RMSEP=0.5541;R^(2)=0.9430,RMSECV=0.6465for cross validation;the average recovery was 101.63% for external verification.The parameters of the established PLS model for ophiopogonin D were as follows:R^(2)=0.9498,RMSEC=0.6627,RMSEP=0.3157;R^(2)=0.9206,RMSECV=0.3583 for cross validation;the average recovery was 102.07% for external verification.The parameters of the established PLS model for ophiopogonin D’were as follows:R^(2)=0.9804,RMSEC=0.0853,RMSEP=0.4155;R^(2)=0.9516,RMSECV=0.6933 for cross validation;the average recovery was 99.44%for external verification.The parameters of the established PLS model for methylophiopogonanone A were as follows:R^(2)=0.9593,RMSEC=0.8122,RMSEP=0.5674;R^(2)=0.9395,RMSECV=0.7444 for cross validation;the average recovery was 103.11%for external verification.The parameters of the established PLS model for methylophiopogonanone B were as follows:R^(2)=0.9408,RMSEC=0.6451,RMSEP=0.7476;R^(2)=0.9283,RMSECV=0.8849 for cross validation;the average recovery was 101.59% for external verification.The samples of the calibration set and the validation set were uniformly distributed on both sides of the regression line,indicating that the predicted value of the model had a good correlation with the measured value,and the prediction performance of the model was relatively ideal.Conclusion The established quantitative model is simple to operate and the predictive results are accurate,which can be used for rapid content determination of five components in Ophiopogonis Radix.