Evaluation of the value of near infrared (NIR) spectromicroscopy for the analysis of glycyrrizhic acid in licorice

• Published in: Chinese journal of natural medicines Vol. 13; no. 4; pp. 316 - 320
• Main Authors: WU, Zhi-Sheng, ZHOU, Lu-Wei, DAI, Sheng-Yun, SHI, Xin-Yuan, QIAO, Yan-Jiang
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.04.2015
• Subjects: Calibration; Drugs, Chinese Herbal - chemistry; Glycyrrhiza - chemistry; Glycyrrhiza uralensis; Glycyrrhizic Acid - analysis; Least squares support vector regression; Least-Squares Analysis; Microscopy - methods; NIR hyperspectral imaging; Partial least squares; Spectroscopy, Near-Infrared - methods; Glycyrrhiza uralensis; Partial least squares; NIR hyperspectral imaging; Least squares support vector regression
• ISSN: 1875-5364; 1875-5364
• DOI: 10.1016/S1875-5364(15)30022-4

It has been reported that hyperspectral data could be employed to qualitatively elucidate the spatial composition of tablets of Chinese medicinal plants. To gain more insights into this technology, a quantitative profile provided by near infrared (NIR) spectromicroscopy was further studied by determining the glycyrrhizic acid content in licorice, Glycyrrhiza uralensis. Thirty-nine samples from twenty-four different origins were analyzed using NIR spectromicroscopy. Partial least squares, interval partial least square (iPLS), and least squares support vector regression (LS-SVR) methods were used to develop linear and non-linear calibration models, with optimal calibration parameters (number of interval numbers, kernel parameter, etc.) being explored. The root mean square error of prediction (RMSEP) and the coefficient of determination (R2) of the iPLS model were 0.717 7% and 0.936 1 in the prediction set, respectively. The RMSEP and R2 of LS-SVR model were 0.515 5% and 0.951 4 in the prediction set, respectively. These results demonstrated that the glycyrrhizic acid content in licorice could barely be analyzed by NIR spectromicroscopy, suggesting that good quality quantitative data are difficult to obtain from microscopic NIR spectra for complicated Chinese medicinal plant materials.