近红外光谱结合不同变量筛选方法用于黄芩提取过程中黄芩苷含量预测

The prediction of baicalin content in the extraction process of Scutellaria baicalensis by near-infrared spectroscopy combined with different variable selection methods

近红外光谱技术(NIRS)结合化学计量学可以实现中药过程分析中的快速定量,变量筛选算法的应用可以有效去除光谱中的冗余信息并筛选出与成分信息相关的关键变量,与全光谱模型相比可以显著降低模型复杂度,并提高预测精度。本文将近红外光谱技术结合不同变量筛选算法用于黄芩提取过程黄芩苷含量的快速测定,基于SPXY法划分数据集,采用竞争自适应加权重采样法(CARS)、随机青蛙算法(RF)、连续投影算法(SPA) 3种不同变量筛选方法,以偏最小二乘法(PLS)为基础,建立并比较了黄芩药材提取过程黄芩苷含量的定量校正模型。经CARS法、RF法和SPA法分别筛选出92、10、17个变量, CARS-PLS法建立的黄芩苷模型具有最佳性能, CARS法筛选的关健变量与指标成分黄芩苷的化学结构也有着较好的对应关系。模型的校正均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为0.528 2和0.720 2,与全光谱模型相比,模型的校正集相关系数(Rc)从0.917 0上升到0.979 9,相对预测偏差(RSEP)从10.58%降低到5.59%。

Near-infrared spectroscopy(NIRS)combined with chemometrics can achieve rapid detection in process analysis.After variable selection,the redundant information is effectively removed and the characteristic variables related to the response values are selected.Compared with global model,the complexity is significantly reduced and the prediction accuracy is also improved.In this study,near-infrared spectroscopy analysis combined with different variable selection methods was applied to achieve the rapid detection of baicalin in the extraction process of Scutellaria baicalensis.Data sets were divided based on sample set portioning based on joint x-y distance(SPXY)method.Competitive adaptive weighted resampling method(CARS),random frog(RF)and successive projections algorithm(SPA)were applied to variable selection.Partial least squares(PLS)models were constructed based on above three methods,and the prediction results were compared.After CARS,RF and SPA method,92,10 and 17 variables were screened out respectively.According to the performance of the models,CARS method is found to be more effective and suitable than RF and SPA.Furthermore,the characteristic variables selected by CARS method have a better correspondence with the chemical structure of baicalin.The root mean square error(RMSEC)of the calibration set and the root mean square error(RMSEP)of the prediction set are 0.528 2 and 0.720 2 respectively.Compared with the global PLS model,the correlation coefficient of the calibration set(Rc)is increased to 0.979 9 from 0.917 0,and the relative standard errors of prediction(RSEP)is reduced to 5.59%from 10.58%.