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%.
ABSTRACT
Objective: To determine the content of chlorogenic acid in Lonicerae Japonicae Flos by the combined near-infrared and variable selection methods. Methods: Synergy interval partial least squares (SIPLS), competitive adaptive reweighted sampling method (CARS), variable importance in projection (VIP), and successive projections algorithm (SPA) were used to build a chlorogenic acid quantitative model in Lonicerae Japonicae Flos and compare. High performance liquid chromatography (HPLC) was used as a reference to select the optimum variable screening method. Results: Study results showed that SIPLS was the most desirable method for chlorogenic acid in regression performance with Rpre2 at 0.990 3 and RMSEP at 2.316%. Conclusion: The quantitative model of chlorogenic acid established by NIR combined with SIPLS has good performance and meets the requirement of real-time analysis of traditional Chinese medicine production process.