Analysis on Dynamic Change of Stir-fried Glycyrrhizae Radix et Rhizoma Quality Based on "Exterior-interior Correlation"
10.13422/j.cnki.syfjx.20241246
- VernacularTitle:基于“表里关联”的炒甘草质量动态变化分析
- Author:
Yue XU
1
;
Zhe JIA
2
;
Yun WANG
2
;
Bing LI
2
;
Deling WU
1
;
Cun ZHANG
1
Author Information
1. School of Pharmacy，Anhui University of Chinese Medicine，Hefei 230012，China
2. Institute of Chinese Materia Medica，China Academy of Chinese Medical Sciences，Beijing 100700，China
- Publication Type:Journal Article
- Keywords:
stir-fried Glycyrrhizae Radix et Rhizoma;
exterior-interior correlation;
process samples;
dynamic change;
chromaticity;
correlation analysis;
quality markers
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2025;31(5):194-202
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveIn order to provide a reference for the optimization of preparation process of stir-fried Glycyrrhizae Radix et Rhizoma（sf-GRR）， the quality changes during the processing was studied. MethodsGlycyrrhizae Radix et Rhizoma was processed by stir-frying for 17 min， and samples were collected every 1 min during the processing. The appearance color of the samples was determined by visual analysis technology， the moisture and extract of the process samples were detected by the drying method and the hot extraction method of alcohol-soluble extract in the general rules of the 2020 edition of Chinese Pharmacopoeia（part Ⅳ）， and the contents of liquiritin apioside， liquiritin， isoliquiritin apioside， isoliquiritin， licoricesaponin G2 and glycyrrhizic acid in the process samples were determined by high performance liquid chromatography（HPLC）. Then principal component analysis（PCA）， partial least squares-discriminant analysis（PLS-DA） and Spearman correlation analysis were used for clustering， discrimination and correlation analysis of the appearance color， moisture， extract and the contents of six internal components. Based on artificial neural network and random forest algorithm， the prediction model of processing degree of sf-GRR was established. On this basis， based on the five principles of quality marker（Q-Maker）， explore the monitoring Q-Maker of sf-GRR. ResultsThe color of Glycyrrhizae Radix et Rhizoma deepened after stir-frying， and the appearance color of the sample changed from light yellow to dark yellow during processing. During the stir-frying process， the moisture content showed a decreasing trend with the extension of processing time， while the extract content showed an increasing trend with the extension of processing time. After stir-frying， the contents of liquiritin apioside， liquiritin and licoricesaponin G2 showed an overall decreasing trend， while the contents of isoliquiritin apioside and isoliquiritin increased， and the content of glycyrrhizic acid increased slightly. The correlation analysis showed that moisture was positively correlated with brightness（L*） and red/green value（a*）， and negatively correlated with yellow/blue value（b*） and total color difference（E*ab）. Isoliquiritin apioside and isoliquiritin had negative correlation with L* and a*， and positive correlation with b* and E*ab. The processing process of sf-GRR could be divided into two stages of the early stage（0-14 min） and the late stage（15-17 min）， and could be divided into three stages of the early stage（0-6 min）， the middle stage（7-14 min） and the late stage（15-17 min） by combining the moisture， extract， the contents of 6 components and color values. Based on artificial neural network analysis and random forest algorithm， isoliquiritin apioside， isoliquiritin， liquiritin and glycyrrhizic acid were selected as monitoring markers for sf-GRR. ConclusionBased on the analysis of the exterior-interior indicators of process samples of sf-GRR， this paper ultimately identifies four processing monitoring markers， which can provide a basis for optimizing the processing technology of sf-GRR.
