The prediction of baicalin content in the extraction process of Scutellaria baicalensis by near-infrared spectroscopy combined with different variable selection methods
10.16438/j.0513-4870.2018-0712
- VernacularTitle: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
- Author:
Xue-song LIU
1
;
Si-yu ZHANG
1
;
Man-qian ZHAO
2
;
Jun WANG
3
;
Ye-rui LI
3
;
Jun DAI
2
;
Chuan-zhen TENG
2
;
Xiao KE
2
;
Yong CHEN
1
;
Yong-jiang WU
1
Author Information
1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
2. Chengdu Kanghong Pharmaceutical Group Co., Ltd., Chengdu 610036, China
3. Suzhou ZeDaXingBang Pharmaceutical Co., Ltd., Suzhou 215000, China
- Publication Type:Research Article
- Keywords:
near infrared spectroscopy;
variable selection;
baicalin;
competitive adaptive reweighted sampling method;
random frog;
successive projections algorithm;
partial least squares
- From:
Acta Pharmaceutica Sinica
2019;54(1):138-143
- CountryChina
- Language:Chinese
-
Abstract:
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%.
