Near-infrared spectroscopy for classification of licorice (Glycyrrhizia uralensis Fisch) and prediction of the glycyrrhizic acid (GA) content

Licorice, the roots of Glycyrrhizia uralensis Fisch, is used as a medicinal herb and food additive in China. At present, the quality grading and authentication of licorice products is generally done by visual inspection, which is subjective and often biased. In this study, a rapid and nondestructive method for the classification of licorice samples according to their growing conditions, geographic areas and plant parts was developed using fiber optic diffuse reflectance near-infrared spectroscopy (FODR-NIR). With the use of multiplicative signal correlation (MSC) and Norris derivative filter, the differences of the NIR spectra among different licorice samples were enhanced even though the raw spectra were very similar. The results showed that the NIR spectra of the samples were moderately clustered in the principle component spaces, and pattern recognition technique of soft independent modeling of class analogy (SIMCA) provided satisfactory classification results. Additionally, a partial least square (PLS) method using HPLC data set as reference was constructed to predict the value of glycyrrhizic acid (GA) in licorice. The results showed that PLS models with both data normalization (DN) coupled with first derivative pretreatments, and MSC coupled with first derivative pretreatments provided acceptable results.