Skip to main content
NCBI home page
ACS Omega logoLink to ACS Omega
. 2026 Jan 4;11(2):2820–2831. doi: 10.1021/acsomega.5c08675

Changes in Components During Quality Formation and Evaluation of Honey-Processed Glycyrrhiza uralensis Fisch. from Different Geographical Origins by Sequential Electrospray Ionization Mass Spectrometry

Ying Liu †,‡, Linlin Yu §, Le Wang †,‡, Xiaowei Fang ‡, Tenggao Zhu ‡,∥, Huanwen Chen †,‡, Wenliu Zhang ⊥,*, Dong Wu †,‡,*
PMCID: PMC12824972  PMID: 41585634

Abstract

To explore the changes in quality formation during the honey-processing of Glycyrrhiza uralensis from six geographical origins, including Inner Mongolia, Xinjiang, Anhui, Gansu, Guangdong, and Jiangxi. In this study, raw G. uralensis was mixed with honey and smoldered, then heated and stir-fried, and finally cooled. The water-soluble components (WSCs) and methanol-soluble components (MSCs) in G. uralensis were simultaneously analyzed by sequential electrospray ionization mass spectrometry (SESI-MS) without the need for complex sample pretreatment. Fifteen WSCs (e.g., proline, arginine, serine, glabrol, monosaccharides, disaccharides, and trisaccharides) and twenty-eight MSCs (e.g., liquiritigenin, naringenin, glycyrrhizic acid, glycyrrhetinic acid, and formononetin) were identified within 2 min. Both principal component analysis and partial least-squares discrimination analysis showed that samples of honey-processed G. uralensis (HPGU) from six different geographical origins were effectively distinguished; liquiritin, daidzein, sinapic acid, and ferulic acid were identified as potential differential metabolites by variable importance for the projection; and the contents of saccharides, glabridin, formononetin, glabrol, and glycyrrhetinic acid were high in HPGU. Meanwhile, Inner Mongolia showed the highest contents of glycyrrhetinic acid and daidzein, suggesting these samples may possess superior quality based on these specific markers. SESI-MS could enhance our understanding of the changes in quality formation during the honey-processing of G. uralensis.

graphic file with name ao5c08675_0008.jpg

graphic file with name ao5c08675_0006.jpg

1. Introduction

Glycyrrhiza uralensis Fisch. is a valuable industrial and medicinal plant species of Fabaceae, and is widely used in medicine, food, animal feed, cosmetics, and cigarette industries. As the source of bioactive compounds, G. uralensis contains triterpenoids, flavonoids and polysaccharides, and is frequently used to treat cough, influenza, and cancer in the traditional Chinese medicine (TCM) system. It is mainly distributed in Gansu, Qinghai, Xinjiang, and Inner Mongolia in China, and has attracted much attention in the international markets. Moreover, G. uralensis has been commonly processed and used in combination with honey for a long time. , Honey is a common natural food additive, and is rich in amino acids, and flavonoids, terpenes, and alkaloids. Therefore, HPGU could significantly improve its medicinal properties by mixing and heating with honey compared to raw G. uralensis (RGU). However, the mechanism underlying chemical composition variations during HPGU processing is still unclear, and the lack of quality control standards for HPGU poses challenges for its industrial development. Given the unique nature of honey-processing techniques, the final product is a complex system in which the original components of G. uralensis and the additives of honey are integrated and transformed. Therefore, using a single solvent cannot comprehensively capture the complete chemical information on the processed products. This study used deionized water and methanol as continuous extraction solvents, aiming to systematically capture the polar WSCs derived from G. uralensis and honey as well as the moderately polar MSCs. This dual-solvent analysis strategy was crucial for accurately interpreting the role of honey in the processing and comprehensively evaluating the quality formation of HPGU. Besides, studies have shown that the geographical origin is one of the critical factors affecting the chemical composition (i.e., active components) of HPGU. For example, Cui et al. reported that among the samples from eight geographical regions, including Gansu, Ningxia, Shanxi, Inner Mongolia, Xinjiang, Heilongjiang, Jilin, and Liaoning, the highest level of glycyrrhizic acid was detected in G. uralensis from Gansu, the highest levels of glycyrrhetinic acid and total flavonoids were revealed in G. uralensis from Inner Mongolia, and the highest level of total saponins was detected in G. uralensis from Xinjiang.

At present, a total of 46 pharmacodynamic components in G. uralensis, e.g., liquiritigenin, isoliquiritigenin, and glycyrrhizic acid, have been identified by liquid chromatography–mass spectrometry (LC-MS). In addition, a total of 51 methanol-soluble phenolic compounds, including flavonoids, flavanones, chalcones, and isoflavones, were chromatographically separated and characterized by high-performance liquid chromatography with time-of-flight mass spectrometry (HPLC-TOF-MS) in G. uralensis. Farag et al. reported that a total of 33 primary metabolites in the MSCs of G. uralensis, such as proline, serine, and saccharides, were analyzed by gas chromatography–mass spectrometry (GC-MS). While the aforementioned chromatographic techniques (e.g., LC-MS, GC-MS) are powerful for detailed compound separation and absolute quantification, they often involve time-consuming procedures, including complex sample preparation, large solvent consumption, and lengthy analysis times per sample. More importantly, these methods typically require separate analytical procedures to cover the full spectrum of compounds with different polarities such as WSCs and MSCs. In contrast, the SESI-MS employed in this study offers a distinct paradigm for rapid chemical profiling. SESI-MS foregoes the chromatographic separation step, enabling direct analysis of sequentially extracted compounds without sample preparation. This results in a reduction in analysis time (from hours to minutes per sample), significantly lower solvent usage, and an inherent high-throughput capability. The primary advantage of SESI-MS in the context of HPGU analysis is its ability to provide a comprehensive, untargeted chemical fingerprint of both WSCs and MSCs from a single tiny sample. This makes it exceptionally suitable for applications requiring rapid screening and origin tracing, where speed and holistic profiling are prioritized over individual compound separation. Therefore, this study aims to leverage the unique advantages of SESI-MS to simultaneously analyze both MSCs and WSCs of HPGU without the need for a complex sample pretreatment.

SESI-MS could simultaneously accurately analyze compounds extracted by different solvents in a sample in a high-throughput manner, with high sensitivity and without complicated sample pretreatment. Currently, SESI-MS has been commonly used in the study of foods, environmental samples, ores, and metallic materials. For example, SESI-MS is combined with inductively coupled plasma mass spectrometry (ICP-MS) to sequentially analyze WSCs and dilute nitric acid-soluble trace elements in eggshells, demonstrating that 15 trace elements in a single sample were detected in less than 10 min and a unique “fingerprint spectrum” was generated according to the content, morphology, and distribution of each trace element in different eggshell samples. Furthermore, electrochemical mass spectrometry (EC-MS) was used to sequentially determine lead (Pb) species in PM2.5 particles in the atmosphere without any sample preparation, revealing four kinds of Pb species, including water-soluble Pb compounds, fat-soluble Pb compounds, water/fat-soluble Pb compounds, and a water/fat-insoluble Pb element. Moreover, sequential EC-MS (SEC-MS) could rapidly detect copper (Cu), nickel (Ni), and Pb metal impurities, which were located on the surface of several typical irregular metal products such as gold necklaces, fuel nozzles, and bearing balls. However, there are no studies on the simultaneous detection of compounds from different solvents in HPGU using SESI-MS, and there are few comprehensive studies of HPGU on changes in components during quality formation and evaluation from different geographical origins.

In this study, SESI-MS was applied for the first time to G. uralensis, enabling the simultaneous and rapid online sequential quantitative detection of WSCs and MSCs in HPGU, with changes in components during quality formation. This method was combined with multivariate analysis to rapidly distinguish and identify HPGU samples from six geographical origins. It was also used to quantify amino acids, flavonoids, and other compounds by comparison with standards. Therefore, this study aims to provide strong experimental evidence by investigating the honey-processing of G. uralensis and supporting the quality evaluation of HPGU based on information-rich SESI-MS fingerprints.

2. Materials and Methods

2.1. Chemicals and Reagents

Methanol (gradient grade for liquid chromatography, contains ≤100%), liquiritigenin, liquiritin, glycyrrhizic acid, glycyrrhetinic acid gallic acid, vanillic acid, epicatechin, biochanin A, glucose, and sucrose were purchased from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Caffeic acid, naringenin, malic acid, serine, proline, valine, threonine, leucine, asparagine, glutamic acid, glutamine, arginine, aspartic acid, and ferulic acid were obtained from Shanghai Bailingwei Technology Co., Ltd. (Shanghai, China). Acacia honey was purchased from Jiangxi Wang’s Bee Garden Co., Ltd. (Jiangxi, China). Deionized water used in all experiments was prepared by using a Field-P Comprehensive Ultrapure Water Machine (Felder Beijing Scientific Instrument Co., Ltd., Beijing, China).

2.2. Sample Preparation and Honey-Processing

All samples were slices of cultivated triennial G. uralensis Fisch., the samples of Inner Mongolia were purchased from Inner Mongolia Houde Traditional Chinese Medicine Pieces Co., Ltd. (Inner Mongolia, China), the samples of Xinjiang were purchased from Xinjiang Yuhetang Pharmaceutical Co., Ltd. (Xinjiang, China), the samples of Gansu were purchased from Gansu Jingge Biotechnology Co., Ltd. (Gansu, China), the samples of Anhui were purchased from Anhui Zhengqiren E-commerce Co., Ltd. (Anhui, China), the samples of Guangdong were purchased from Dongguan Dekangtang Pharmaceutical Co., Ltd. (Guangdong, China), and the samples of Jiangxi were purchased from Jiangxi Jiangzhong Traditional Chinese Medicine Decoction Pieces Co., Ltd. (Jiangxi, China). The samples of RGU were purchased from Inner Mongolia Houde Traditional Chinese Medicine Pieces (Inner Mongolia, China).

HPGU was prepared according to the Pharmacopoeia of the People’s Republic of China 2020 (Provision No. 0213). Briefly, 100 g of RGU slices were mixed thoroughly with 25 g of honey and diluted with an appropriate amount of boiling water. The mixture was smoldered at 25 °C for 30 min to produce MSGU, followed by stir-frying in a preheated pan at 120 °C for 10–15 min over simmering heat until the slices turned yellow to deep yellow to yield HSGU. The material was then cooled to room temperature for 5–10 min until no longer sticky, producing the final HPGU product. Ten groups of samples, i.e., RGU, MSGU, HSGU, HPGU, Inner Mongolia, Xinjiang, Gansu, Anhui, Guangdong, and Jiangxi, were prepared for subsequent analysis. Detailed information about the geographically sourced HPGU samples is provided in Table S1.

2.3. SESI-MS Analysis

Figure shows the experimental scheme for rapid compound analysis in HPGU using SESI-MS. Approximately 1 mg of each sample was sequentially extracted in a microcell using deionized water followed by methanol. After activating the syringe pump, the sample was sequentially extracted online with deionized water to isolate WSCs and then with methanol to extract MSCs. At an optimized constant flow rate of 8 μL/min, the extracted fractions were atomized into charged droplets by nitrogen gas under high voltage (HV) and transported to MS for analysis. The MS data of WSCs and MSCs were collected separately for 2 min each to ensure comprehensive access to the chemical information. The key operational parameters were systematically optimized to achieve a maximum sensitivity. The spray voltage was tested from 0.5 to 4.0 kV, with 3.5 kV selected as optimal. The nebulization gas pressure was optimized from 0.2 to 0.7 MPa, with 0.5 MPa providing the best performance. The distance between the spray emitter and the MS inlet was maintained at 5 mm. For positive ion mode analysis, the ion source temperature was set to 260 °C with voltages of 30 V (ion source) and 230 V (ion transfer tube). For negative ion mode analysis, the temperature remained at 260 °C, with voltages of −50 and −220 V, respectively. Full-scan mass spectra were acquired over the range of m/z 50–1000. For compound identification, tandem mass spectrometry (MS2) analyses were performed via collision-induced dissociation (CID) with an isolation width of 1.0–1.5 Da and normalized collision energies ranging from 10 to 50%. All data were processed by using the Xcalibur software system (Thermo Scientific).

1.

1

Open in a new tab

Experimental scheme of SESI-MS for online, high-throughput, sensitive, and rapid analysis of G. uralensis. WSCs and MSCs are sequentially isolated from the samples. After the ionizing HV is applied, the sample solution forms a charged spray for analysis using the MS.

2.4. Data Processing and Statistical Analysis

All data were generated from three biological replicates. The raw mass data were preprocessed with Microsoft Excel (Office 2016, Microsoft), and all values were expressed as arithmetic means ± standard deviation (SD). One-way ANOVA and Tukey’s test were performed using IBM SPSS Statistics 19 (Armonk, New York) to identify significant differences (p < 0.05). The raw mass spectral data, represented by the signal intensities of the detected features, were processed for multivariate analysis. All data were standardized prior to modeling. Principal component analysis (PCA) was performed using MATLAB R2016a (Mathworks). Partial least-squares discriminant analysis (PLS-DA) and analysis of variable importance in the projection (VIP) were conducted using SIMCA software (version 14.0, Umetrics, Sweden). Heatmap analysis of compounds was performed by TBtools (version 2.09, South China Agricultural University, China). The volcano map and orthogonal PLS-DA (OPLS-DA) were analyzed using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/).

3. Results and Discussion

3.1. Changes in the Appearance and Levels of Saccharides of HPGU

RGU was light yellow in color, while the color of MSGU was determined by the time when honey was added to RGU and the color of HSGU was further intensified after high-temperature frying (Figure A); HPGU appeared to be deep yellow. The yellow color of the honey probably intensified the color of G. uralensis, and the Maillard reaction occurred at high temperatures further deepened the color of G. uralensis. For samples of HPGU from six geographical origins (Figure B), the color spectrum ranged from dark to light in the following order: Xinjiang, Inner Mongolia, Gansu, Anhui, Jiangxi, and Guangdong. Herein, SESI-MS was used to further analyze the quality changes during the honey-processing of G. uralensis and comparatively evaluate the quality variations of HPGU from different geographical origins. As shown in Figure S1, we systematically optimized key parameters such as spray voltage, extractant flow rate, and spray pressure. Taking the spray voltage as an example, when the voltage increased from 0.5 to 4.0 kV, the signal intensity showed an increasing trend. Although the signal intensity increased at 4.0 kV, for experimental safety, we ultimately chose 3.5 kV as the optimal ionization voltage (Figure S1A). This choice was based on a comprehensive consideration of maintaining sufficient signal intensity while maximizing the experimental stability and avoiding the risk. Similarly, the selection of the extractant flow rate and spray pressure was also the result of a trade-off between signal response and solvent consumption/ionization efficiency. When methanol was used as the extractant with the flow rate at 8 μL/min and the spray pressure at 0.5 MPa, the highest MS signal intensity was reached. Thus, the extractant flow rate was set to 8 μL/min (Figure S2B), and the spray pressure was set to 0.5 MPa (Figure S2C). Under the optimized experimental conditions, the monosaccharides, disaccharides, and trisaccharides were detected from all samples in positive ion mode, mainly in the forms of [M + Na]+ and [M + K]+. The results showed that the disaccharide content was significantly higher than those of monosaccharides and trisaccharides in all four groups of samples, i.e., RGU, MSGU, HSGU, and HPGU (Figure C). Compared with RGU, the trisaccharide contents in MSGU, HSGU, and HPGU were significantly increased after heat-frying. The disaccharide content in RGU accounted for 93.26%, and the contents of disaccharides and total saccharides were increased with the addition of honey and heating, reaching the highest level in HPGU, probably due to the hydrolysis of saccharides after heating. Previous studies have shown that the contents of mannose, rhamnose, and glucose in HPGU were significantly higher than those in RGU, and the polysaccharide content in HPGU (168.44 mg/g) was 67.96% higher than that in RGU (100.28 mg/g). In the samples of HPGU from six geographical origins, the high levels of total saccharides were revealed in samples from Inner Mongolia, Xinjiang, Gansu, and Guangdong, reaching 57.49, 60.36, 73.73, and 84.94% higher than that from Jiangxi, respectively. No significant difference was detected in the monosaccharide contents among all six samples, while the highest level of trisaccharides (44.26%) was detected in the sample of Jiangxi, and the trisaccharide contents in the other five samples ranged from 7 to 9% (Figure D).

2.

2

Open in a new tab

Morphological observations and saccharide contents of G. uralensis. (A) Morphology of G. uralensis of different honey-processing degrees. (B) Morphology of HPGU from different geographical origins. (C) Saccharide contents of G. uralensis of different honey-processing degrees. (D) Saccharide contents of HPGU from different geographical origins. Different lowercase letters a, b, c, and d indicate significant differences.

3.2. Feature Fingerprint Analyses of WSCs and MSCs in HPGU

The MS fingerprints of water and methanol extracts of HPGU were directly recorded in positive and negative ion modes by SESI-MS under the optimized experimental conditions (Figure A,B). It was noted that these MS fingerprints were recorded in only 2 min. In the positive ion mode, peaks from the fingerprints of water extract were detected at m/z 116 ([proline + H]+), m/z 175 ([arginine + H]+), m/z 365 ([disaccharide + Na]+), m/z 381 ([disaccharide + K]+), and m/z 543 ([trisaccharide + K]+) (Figure C), and peaks from the fingerprints of methanol extract were detected at m/z 257 ([liquiritigenin + H]+) and m/z 269 ([formononetin + H]+) (Figure S2B). In the negative ion mode, peaks from the fingerprints of methanol extract were detected at m/z 165 ([phloretinic acid – H]−), m/z 255 ([liquiritigenin – H]−), m/z 271 ([naringenin – H]−), m/z 417 ([liquiritin – H]−), m/z 550 ([liquiritin apioside – H]−), and m/z 822 ([glycyrrhizic acid – H]−) (Figure D). Overall, in the samples of HPGU, the WSCs were mainly amino acids and saccharides, while the MSCs were mainly flavonoids, triterpenoids, and saponins. Studies have reported that the methanol extract of HPGU contained flavonoids and chalcones in higher levels than those of the water extract. In addition, studies have shown that HPGU contains amino acids, liquiritin, glycycoumarin, and glycyrrhizic acid. These results were consistent with the findings revealed in the present study.

3.

3

Open in a new tab

Analysis of WSCs and MSCs in HPGU based on SESI-MS. (A) Total ion chromatogram of the sequential analysis of HPGU in positive ion mode showing the response for WSCs and MSCs. The WSCs were detected within the 2–4.5 min period, while the MSCs were detected within the 5.5–7 min period. (B) Total ion chromatogram of the sequential analysis of HPGU in negative ion mode, showing the response for WSCs and MSCs. The WSCs were detected within the 0.5–2.5 min period, while the MSCs were detected within the 4–6 min period. (C) SESI-MS mass spectra of WSCs of HPGU in positive ion mode. Major ion peaks are detected at m/z 116 ([proline + H]+), m/z 175 ([arginine + H]+), m/z 365 ([disaccharide + Na]+), m/z 381 ([disaccharide + K]+), and m/z 543 ([trisaccharide + K]+). (D) SESI-MS mass spectrum of MSCs of HPGU in negative ion mode. Major ion peaks are detected at m/z 165 ([phloretinic acid – H]−), m/z 255 ([liquiritigenin – H]−), m/z 271 ([naringenin - H] –), m/z 417 ([liquiritin – H]−), m/z 550 ([liquiritin apioside – H]−), and m/z 822 ([glycyrrhizic acid – H]−). (E) Product ion mass spectrum of the precursor ion at m/z 255 ([liquiritigenin – H]−) acquired in negative ion mode. (F) Product ion mass spectrum of the precursor ion at m/z 822 ([glycyrrhizic acid – H]−) in negative ion mode.

The active compounds of G. uralensis were identified by MS2 analysis based on CID coupled to the standard compounds and available databases. In our study, a total of 43 compounds were identified in the samples of HPGU, including 15 WSCs (e.g., proline, arginine, serine, glabrol, monosaccharides, disaccharides, and trisaccharides) and 28 MSCs (e.g., liquiritigenin, naringenin, glycyrrhizic acid, glycyrrhetinic acid, and formononetin) (Table S2). The MS2 spectrum of the ion at m/z 106 is shown in Figure S3A; the main m/z 88 fragment ions produced by m/z 106 were probably due to the loss of a neutral fragment of H2O of [serine + H]+. The m/z 175 was assigned to [arginine + H]+ on the basis of the major fragment ions at m/z 157 and m/z 158 generated from ions of m/z 175, probably by the loss of neutral fragments of H2O and OH, and the m/z 130 fragment was generated by the loss of COOH (Figure S3B). These fragmentation behaviors were identical to those of the standard compounds. According to CID, an additional 9 amino acids were successfully identified, including [proline + H]+ (m/z 116), [threonine – H]− (m/z 118), [aspartic acid – H]− (m/z 132), [agmatine + H]+ (m/z 131), [asparagine + H]+ (m/z 133), [leucine + H]+ (m/z 132), [glutamine – H]− (m/z 145), [valine + H]+ (m/z 118), and [glutamic acid + H]+ (m/z 148) (Figure C–K). Furthermore, the MS2 spectrum of the ion at m/z 365 is shown in Figure S3L, which was assigned to [disaccharide + Na]+ on the basis of the major fragment ions at m/z 203 and m/z 185 generated from ions of m/z 365, probably by the loss of glycosyl group and monosaccharide. Notably, [monosaccharide + Na]+ (m/z 203), [trisaccharide + Na]+ (m/z 527), [monosaccharide + K]+ (m/z 219), [disaccharide + K]+ (m/z 381), and [trisaccharide + K]+ (m/z 543) were equally identified (Figure S3M–Q). Also, [glabrol + H]+ (m/z 394) (Figure S3R) generated major fragments of m/z 376 and m/z 377 by the losses of H2O and OH, respectively. Besides, [liquiritigenin – H]− (m/z 255) (Figure E) generated major fragments of m/z 135 by the loss of C8H8O. And [liquiritin – H]− (m/z 417) (Figure S3S) generated major fragments of m/z 255 by the loss of C6H10O5, and [glycyrrhizic acid – H]− (m/z 822) (Figure F) generated major fragments of m/z 645 and m/z 351 by the loss of C6H9O6 and C30H47O4, respectively. These characteristic peaks were consistent with those of liquiritigenin, liquiritin, and glycyrrhizic acid standard compounds. By the same way, other 23 compounds were identified, including [glycyrrhetinic acid – H]− (m/z 470), [liquiritin apioside – H]− (m/z 550), [genistein – H]− (m/z 431), [caffeic acid – H]− (m/z 179), [acacetin – H]− (m/z 283), [glabridin – H]− (m/z 323), [daidzein – H]− (m/z 253), [vanillic acid – H]− (m/z 167), [salicylic acid – H]− (m/z 137), [4-nitrobenzoic acid – H]− (m/z 166), [phloretinic acid – H]− (m/z 165), [epicatechin – H]− (m/z 289), [malic acid – H]− (m/z 133), [gallic acid – H]− (m/z 169), [ferulic acid + H]+ (m/z 195), [formononetin + H]+ (m/z 269), [naringenin + H]+ (m/z 273), [isoangustone + H]+ (m/z 423), [glycycoumarin + H]+ (m/z 369), [quercetin + H]+ (m/z 303), [aromadedrin + H]+ (m/z 289), [taxifolin + H]+ (m/z 305), and [3,4-dimethoxycinnamic acid + H]+ (m/z 209) (Figure S3T–AQ).

3.3. Quality Changes of G. Uralensis during Processing

PCA was performed to visualize the quality changes among the samples from the four stages of honey-processing in G. uralensis (Figure A). Three PCs with eigenvalues of 61.2% (PC1), 26.8% (PC2), and 8.8% (PC3) representing about 96.8% of the total variance were selected for modeling. There was a clear distinction among the four groups of samples, i.e., RGU, MSGU, HSGU, and HPGU, revealing evident variations in the compound profiles during the processing of G. uralensis. Groups of RGU and MSGU were well distinguished from groups of RGU and HPGU along PC1, indicating that the addition of honey and further heating affected the compound profiles in G. uralensis. After the unsupervised PCA, the statistics were then supervised by PLS-DA (Figure B). The results revealed similar discrimination of the four groups of samples to that of PCA. Both R 2 and Q 2 reached 0.987 and 0.985 (>0.5), respectively, indicating the robust interpretation and prediction among the models. VIP analysis was used to identify a total of 37 potential differential metabolites during different processing stages of G. uralensis (Figure S4) out of a total of 408 substances with a VIP value >1, mainly including [disaccharide + Na]+ (m/z 365) (VIP = 1.51), [monosaccharide + K]+ (m/z 219) (VIP = 1.45), [trisaccharide + Na]+ (m/z 527) (VIP = 1.39), [proline + H]+ (m/z 116) (VIP = 1.37), and [liquiritigenin – H]− (m/z 255) (VIP = 1.30) (Table S2). In order to precisely evaluate the quality changes in samples of HPGU, OPLS-DA was performed using compounds detected in samples of RGU and HPGU. The results of the score graph revealed an evident distinction between RGU and HPGU (Figure S5A), and the metabolite profiles of samples were significantly changed during processing, with both R 2 and Q 2 > 0.99 (p < 0.05) in the permutation test (Figure S5B). The interpretation and prediction capabilities of the model were further verified. The 43 compounds identified in the samples of HPGU were further analyzed by volcano map (Figure C) showing that the log2 (fold change) values of most compounds were >1 and p < 0.05, indicating significant variations among most substances, especially saccharides as well as flavonoids (e.g., naringenin, liquiritigenin, and liquiritin). Besides, the heatmap results showed that the flavonoid contents in G. uralensis were reduced during processing, e.g., naringenin, liquiritigenin, liquiritin, and liquiritin apioside (Figure E). Takla et al. evaluate the variations in metabolites between RGU and HPGU using ultra-performance liquid chromatography mass spectrometry (UPLC-MS) combined with multivariate analysis, showing that the flavonoid contents (glabridin and baicalin) were significantly declined in HPGU compared with those in RGU. Studies have also shown that flavonoid compounds would be converted into chalcone and isoflavones during heating, and flavonoid glycosides in G. uralensis were hydrolyzed into aglycones during the heating process with honey, such as the liquiritin decomposed into liquiritigenin and glucose (Figure D (1)). Su et al. found that the contents of polar compounds identified as saccharide derivatives in G. uralensis were increased during heating. , Furthermore, the glycyrrhizic acid content was decreased in HPGU during processing, while the glycyrrhetinic acid content was relatively increased. Shabkhiz et al. studied the extraction of glycyrrhizic acid from G. uralensis root using superheated water extraction and found that the hydrolysis reaction causing the breakage of the etheric bond between glycon and aglycon was probably due to the high extraction temperature. At high temperature, glycyrrhizic acid was decomposed into one molecule of glycyrrhetinic acid and two molecules of glucuronic acid (Figure D (2)). For the extraction of natural ingredients from medicinal plants, the extraction temperature makes a critical impact on the extraction efficiency, i.e., increasing the extraction temperature from 150 to 250 °C would result in higher efficiency, whereas the analytes tend to degrade with a continuous increase in extraction temperature. Wang et al. confirmed a significant increase in glycyrrhetinic acid production at the extraction temperature of 130 °C. These results are consistent with the findings revealed in our study. Moreover, the contents of various amino acids (e.g., serine, threonine, and leucine) in HPGU were significantly increased after heating, probably due to the rich amino acids, such as proline, arginine, serine, aspartic acid, and glutamic acid, in honey. In addition, the changes of the top marker compounds with high VIP value in G. uralensis during the honey-processing were also summarized (Table S3). Such as the liquiritin content decreasing after honey-processing, as mentioned above. It provided clearer insights into the honey-processing-induced transformations.

4.

4

Open in a new tab

Multivariate analysis of SESI-MS data from G. uralensis of different honey-processing degrees. (A) PCA of G. uralensis of different honey-processing degrees. (B) PLS-DA of G. uralensis of different honey-processing degrees. (C) Volcano map of the 43 compounds involved in the honey-processing of samples of RGU and HPGU. (D) (1) Liquiritin decomposes into liquiritigenin and glucose. (2) Glycyrrhizic acid decomposes into glycyrrhetinic acid and glucuronic acid. (E) Heatmap of 43 compounds involved in the honey-processing of HPGU. The color scale represents the relative abundance (log2-scaled intensity) of each compound, with red indicating higher abundance and blue indicating lower abundance. Note: Monosaccharides, disaccharides, and trisaccharides show two forms of [M + K]+ and [M + Na]+ in SESI-MS.

3.4. Quality Evaluation of HPGU from Different Geographical Origins

PCA was performed to visualize the quality differences among the samples from Inner Mongolia, Xinjiang, Gansu, Anhui, Guangdong, and Jiangxi (Figure A). Three PCs (PC1, eigenvalue 70.5%; PC2, eigenvalue 16.9%; and PC3, eigenvalue 5.9%) representing about 93.3% of the total variance were selected for modeling. The results revealed a clear distinction among samples from Inner Mongolia, Xinjiang, Gansu, Anhui, Guangdong, and Jiangxi, and varied compound profiles among the samples of HPGU. Samples from Inner Mongolia, Xinjiang, and Jiangxi were well distinguished along PC1, and samples from Gansu, Anhui, and Guangdong were well distinguished along PC2, indicating that the geographical origins affected the compound profiles of HPGU and the six samples from different origins could be easily distinguished. Then, the statistics were supervised by PLS-DA (Figure B) after the unsupervised PCA. The results showed similar discrimination of the six groups of samples to that of PCA, with both R 2 and Q 2 reaching 0.996 and 0.995 (>0.5), respectively, indicating the robust interpretation and prediction capabilities of the model. Then, VIP analysis was performed to identify a total of 32 potential differential metabolites to distinguish samples of HPGU from different geographical origins (Figure D) out of a total of 514 substances with a VIP value >1, mainly including [daidzein – H]− (m/z 253) (VIP = 1.50), [ferulic acid – H]− (m/z 193) (VIP = 1.43), [disaccharide + Na]+ (m/z 365) (VIP = 1.34), [monosaccharide + K]+ (m/z 219) (VIP = 1.33), [gallic acid – H]− (m/z 169) (VIP = 1.31), [aspartic acid – H]− (m/z 132) (VIP = 1.27), and [liquiritin – H]− (m/z 417) (VIP = 1.27). OPLS-DA was further performed using the active compounds detected in two groups of samples from Inner Mongolia and Jiangxi with evident discrimination. The score graph revealed a clear distinction between samples from Inner Mongolia and Jiangxi, with R 2 and Q 2 values reaching 0.99 and 0.98, respectively, in the permutation test (Figure S5C,D). It could be concluded that there were great differences in metabolites between the samples of HPGU from Inner Mongolia and Jiangxi. Volcano map analysis of 43 identified substances (Figure C) showed that the log2 (Fold Change) values of most compounds were >1 (p < 0.05), mainly including valine, proline, and liquiritin, which were identified as potential indicators to evaluate the quality of HPGU from different origins. The contents of 43 substances in samples from different origins were further comparatively analyzed by heatmap (Figure E). The results showed that the contents of proline, aspartic acid, glutamine, daidzein, glycyrrhetinic acid, and acacetin in samples from Inner Mongolia were the highest among all six geographical origins, and the contents of amino acids in samples from Guangdong were generally high, such as serine, glutamic acid, leucine, and threonine, while the proline content was relatively low (1.81 ± 0.08 μg/g). Natural plants are generally rich in amino acids, and the most abundant amino acids include both glutamic acid and aspartic acid, whereas significant differences in amino acid contents are revealed among different families and genera. For example, Liang et al. used ultrahigh performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to detect 10 amino acids in Astragalus. This genus of medicinal plants from the Fabaceae family contained amino acids such as arginine, asparagine, threonine, proline, and leucine. Furthermore, the contents of active compounds, such as liquiritin, glabrol, and naringenin, were high in the samples from Inner Mongolia and Guangdong, and the contents of liquiritigenin, glycyrrhizic acid, glycyrrhetinic acid, and liquiritin apioside were high in the samples from Inner Mongolia and Anhui. Notably, some top marker compounds with high VIP values, such as daidzein, gallic acid, aspartic acid, and proline, were most abundant in samples from Inner Mongolia, providing strong chemical evidence for its superior quality (Table S4). The formation of this excellent quality is likely closely related to the unique ecological environmental factors in the Inner Mongolia region. For example, Inner Mongolia is usually located in a typical temperate grassland region. Such soil has good drainage and is rich in minerals, which is highly suitable for the deep root system of G. uralensis and the accumulation of secondary metabolites. Besides, Inner Mongolia has a temperate continental monsoon climate. Its characteristics include abundant sunlight, large temperature differences between day and night, and the synchronization of rainfall and heat during the growing season. Adequate sunlight is beneficial for photosynthesis, while the large temperature difference between day and night can reduce nighttime respiration consumption and promote the accumulation of photosynthetic products in the roots. More importantly, G. uralensis grown in Inner Mongolia usually has a longer growth period. The longer growth cycle provides sufficient time for the full synthesis and storage of secondary metabolites, which is consistent with the higher content of indicators detected in the Inner Mongolia samples in this study. Consistently, Zhang et al. evaluated and analyzed the quality of G. uralensis root in three geographical areas (i.e., Inner Mongolia, Gansu, and Xinjiang), showing that all samples were rich in flavonoids, phenols, and polysaccharides, with the highest levels detected in the total contents of liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, and glycyrrhizic acid in the roots of G. uralensis from Inner Mongolia.

5.

5

Open in a new tab

Multivariate analysis of SESI-MS data from HPGU from different geographical origins. (A) PCA of HPGU from different geographical origins. (B) PLS-DA of HPGU from different geographical origins. (C) Volcano map of 43 compounds of samples from Inner Mongolia and Jiangxi. (D) VIP of SESI-MS data of 43 compounds of HPGU from different geographical origins. (E) Heatmap of 43 compounds of HPGU from different geographical origins. The color scale represents the relative abundance (log2-scaled intensity) of each compound, with red indicating higher abundance and blue indicating lower abundance. Note: Monosaccharides, disaccharides, and trisaccharides show two forms of [M + K]+ and [M + Na]+ in SESI-MS.

3.5. Quantitative Analysis of Amino Acids and Flavonoids in HPGU

The contents of 12 target compounds (i.e., proline, arginine, serine, leucine, aspartic acid, liquiritigenin, liquiritin, glycyrrhizic acid, glycyrrhetinic acid, caffeic acid, malic acid, and naringenin) detected by ESI-MS were quantified based on regression curve, linear range (LR), limit of detection (LOD), limit of quantification (LOQ), recovery, and relative standard deviation (RSD). The regression curves were established based on the signal intensities of MS2 quantifier ions (Table S5). Correlation coefficients (R 2) of 12 compounds were between 0.9909 and 0.9997, suggesting a robust correlation between the regression curves of compounds and regression fit. The LR was from 0.01 to 100 μg/mL, the LOD was between 0.01 and 12.21 ng/mL, the LOQ was between 0.02 and 36.99 ng/mL, the recovery was between 90.22 and 121.81%, and the RSD was between 0.45 and 5.27%. In the methodological validation, the recovery rates of most compounds fell within the satisfactory range of 80–120%, demonstrating the accuracy of the method. The only exception was the recovery rate of proline at 121.81%, which we speculate was mainly due to the slight ionic enhancement effect of the HPGU complex matrix on this compound. Nevertheless, this value was still close to the upper limit of the acceptable range. And the quantification was robust for most compounds, though precision was lower for aspartic acid at near-LOQ levels. These indicate excellent precision and reproducibility of the method. Therefore, this minor deviation does not affect the overall reliability of SESI-MS for semiquantitative comparison and chemical fingerprint analysis.

The contents of the 12 compounds were varied in the ten groups of samples (Table ). The highest and lowest total contents of 12 compounds were revealed in samples from Anhui (180.04 ± 2.51 mg/g) and Xinjiang (64.46 ± 0.51 mg/g), respectively. Among the 12 compounds in HPGU, the highest and lowest levels of contents were detected in liquiritigenin (30.85 ± 0.42 mg/g) and aspartic acid (0.16 ± 0.04 μg/g), respectively. Heating with honey significantly reduced the contents of the compounds in G. uralensis: liquiritigenin decreased by 55.78% (from 69.77 ± 1.65 to 30.8 ± 0.42 mg/g), liquiritin by 20.69% (from 24.81 ± 0.39 to 19.68 ± 0.50 mg/g), and glycyrrhizic acid by 50.14% (from 20.93 ± 0.17 to 10.43 ± 0.24 mg/g). In contrast, the glycyrrhetinic acid content in RGU was 3.99 ± 0.12 mg/g and was increased by 89.88% (7.57 ± 0.28 mg/g) in HPGU. In addition to aspartic acid and arginine, the contents of serine, proline, and leucine in HPGU were increased by 88.82, 211.22, and 30.25%, respectively. The highest level in the content of proline (69.06 ± 0.23 μg/g) was detected in HPGU. It is well-known that honey is one of the foods rich in nutrients, especially amino acids. Our results showed that the contents of a variety of amino acids in HPGU were increased, with the most significant changes detected in proline content, probably due to the highest content of proline in honey, accounting for more than 50% of the total free amino acids. Also, the arginine content in honey was relatively rich, about 12.86 mg/kg. In our study, the contents of naringenin (67.80 ± 15.70 μg/g) and malic acid (2579.77 ± 40.87 μg/g) were decreased by 11.74% and 27.55%, respectively, and the content of caffeic acid (3.78 ± 0.07 mg/g) was increased by 10.84% in HPGU.

1. Contents of 12 Compounds in Four Groups of G. Uralensis and HPGU from Six Different Geographical Origins .

compound RGU MSGU HSGU HPGU inner mongolia xinjiang gansu anhui guangdong jiangxi
liquiritigenin (mg/g) 69.77 ± 1.65a 31.86 ± 1.44b 46.37 ± 1.71bc 30.85 ± 0.42c 8.85 ± 0.77e 8.79 ± 0.86e 50.33 ± 0.70b 79.87 ± 0.88a 25.80 ± 0.42d 34.59 ± 1.85c
liquiritin (mg/g) 24.81 ± 0.39a 5.85 ± 0.21c 5.94 ± 0.14c 19.68 ± 0.50b 3.75 ± 0.16b – 1.84 ± 0.02d 0.65 ± 0.01e 5.91 ± 0.11a 3.29 ± 0.09c
glycyrrhizic acid (mg/g) 20.93 ± 0.17b 15.55 ± 0.17c 51.98 ± 1.15a 10.43 ± 0.24d 19.25 ± 0.17d 29.78 ± 0.17b 25.01 ± 0.11c 56.29 ± 0.37a 18.60 ± 0.30d 26.00 ± 0.17c
glycyrrhetinic acid (mg/g) 3.99 ± 0.12c 3.14 ± 0.16d 15.29 ± 0.42a 7.57 ± 0.27b 45.88 ± 0.71a 21.04 ± 0.06c 12.15 ± 0.17d 38.13 ± 1.07b 7.22 ± 0.13f 9.09 ± 0.46e
caffeic acid (mg/g) 3.41 ± 0.25b 3.48 ± 0.10b 1.59 ± 0.06c 3.78 ± 0.07a 7.79 ± 0.58b 1.39 ± 0.17e 3.25 ± 0.03d 5.66 ± 0.17c 13.76 ± 0.39a 3.77 ± 0.14d
naringenin (μg/g) 76.82 ± 12.42a – 69.88 ± 12.72a 67.80 ± 15.70a 259.34 ± 18.19b – – – 203.82 ± 80.78b 438.39 ± 16.96a
malic acid (μg/g) 3560.82 ± 110.52a 172.42 ± 3.92d 323.36 ± 13.38c 2579.77 ± 40.87b 963.28 ± 63.22c 3221.92 ± 90.82a 2486.13 ± 10.22b – 2486.13 ± 3.68b 952.13 ± 6.69c
serine (μg/g) 23.34 ± 8.16c 78.46 ± 9.20a 79.41 ± 2.45a 44.07 ± 3.74b 22.87 ± 6.46a 25.22 ± 12.02a 24.75 ± 18.00a 19.57 ± 12.20a 40.30 ± 9.24b 21.93 ± 6.82a
proline (μg/g) 22.19 ± 0.23c 108.60 ± 1.89a 67.17 ± 1.51b 69.06 ± 0.23b 130.87 ± 0.82b 156.90 ± 3.43a 34.57 ± 1.29c 6.17 ± 0.46d 1.81 ± 0.08e 37.06 ± 1.76c
leucine (μg/g) 12.20 ± 1.24ab 17.24 ± 1.43a 8.67 ± 0.68b 15.89 ± 4.93a 7.03 ± 1.19b 3.77 ± 0.74c 8.10 ± 1.18b 2.45 ± 0.39c 12.03 ± 0.63a 7.61 ± 1.14b
arginine (μg/g) 312.79 ± 6.45a 169.49 ± 1.82c 217.75 ± 2.01b – 3.55 ± 0.10a – – – 39.45 ± 2.61b –
aspartic acid (μg/g) 0.47 ± 0.06a 0.72 ± 0.05b 1.19 ± 0.11a 0.16 ± 0.04c 0.82 ± 0.04a 0.65 ± 0.09b 0.32 ± 0.01c 0.64 ± 0.10b 0.17 ± 0.01d 0.04 ± 0.01e
total (mg/g) 126.90 ± 2.73a 60.43 ± 2.10d 121.93 ± 3.51b 75.09 ± 1.56c 86.89 ± 2.50c 64.46 ± 0.51f 95.13 ± 1.07b 180.04 ± 2.51a 74.05 ± 1.46e 78.18 ± 2.77d
Open in a new tab
a

RGU, raw G. uralensis; MSGU, raw G. uralensis mixed with honey and smoldered; HSGU, G. uralensis mixed with honey, heated, and stir-fried; HPGU, honey-processed G. uralensis. Values are means ± standard deviation (SD). Different lowercase letters a, b, c, d, e, and f in the same row indicate significant differences based on p < 0.05. Symbol “–” indicates data not detected.

The analysis of HPGU samples from six geographical origins revealed the highest level of liquiritigenin from samples from Anhui (79.87 ± 0.88 mg/g). Liquiritigenin, a major component of G. uralensis, belongs to the flavonoids with a range of biological activities, including anti-inflammatory, anticancer, and immunomodulatory. It has been shown that liquiritigenin was used to reduce the occurrence of rheumatoid arthritis and inhibit angiogenesis, and could be used as a dietary supplement to prevent obesity and inhibit lipid accumulation. In our study, the liquiritigenin content (8.85 ± 0.77 mg/g) in HPGU was relatively abundant. It was noted that Kong et al. detected the liquiritigenin content (4.22 mg/g) by ultrafast liquid chromatography combined with electrospray ionization tandem mass spectrometry (UFLC-ESI-MS/MS) in G. uralensis from Inner Mongolia, which was slightly lower than that detected in our study. As another major component of total flavonoids in G. uralensis, liquiritin was confirmed to play a critical role in protecting the heart, skin lesions, and reducing the level of rheumatoid arthritis. Our results showed that the liquiritin contents in samples from Inner Mongolia and Guangdong were 3.75 ± 0.16 and 5.91 ± 0.11 mg/g, respectively, which were higher than those from Xinjiang, Gansu, and Anhui (Table ). Li et al. detected a similar liquiritin content (4.39 ± 0.05 mg/g) in G. uralensis from Xinjiang by UPLC-MS. To date, as one of the main active ingredients of G. uralensis, glycyrrhizic acid, which is a type of tetracyclic triterpenoid, has attracted the increasing attention of researchers worldwide, revealing various pharmacological effects, such as anti-inflammatory, antioxidant, antiviral, anticancer and immunomodulatory, inhibition of liver fibrosis, and hepatocyte steatosis and necrosis. Our study detected a high amount of glycyrrhizic acid in samples, e.g., reaching 19.25 ± 0.17 mg/g in samples from Inner Mongolia. Kong et al. reported an extremely similar level of glycyrrhizic acid (19.24 mg/g). Nevertheless, our study revealed a great difference in the contents of amino acids in the samples of G. uralensis, e.g., the level of proline was significantly higher in Inner Mongolia (130.87 ± 0.82 μg/g), which was increased by 253.13% compared with that of Jiangxi (37.06 ± 1.76 μg/g). The aspartic acid content was low, ranging from 0.03 to 0.86 μg/g in the HPGU samples from six geographical origins, with samples from Inner Mongolia and Jiangxi reaching the highest (0.82 ± 0.04 μg/g) and lowest (0.04 ± 0.01 μg/g) levels, respectively. It has been reported that free amino acids are one of the important metabolites in leguminous plants, e.g., the Astragalus extract is rich in amino acids. In this study, we observed significant geographical distribution differences for certain characteristic components. For instance, liquiritin was not effectively detected in the samples from Xinjiang. This phenomenon might be caused by multiple factors. First, the unique climate and soil conditions in the Xinjiang region may prevent the growth of G. uralensis from synthesizing or accumulating liquiritin, resulting in a much lower background content of liquiritin in the samples compared to other regions. Second, honey-processing is a complex process involving heating. The liquiritin in the Xinjiang samples may be more prone to hydrolysis or react with other components of the honey during the processing, converting into other derivatives, resulting in the content below the LOD. Similarly, naringenin and arginine showed a relatively weak signal or were not detected in several sample groups, which might also be due to similar reasons. In conclusion, the absence or weak signals of these components were more likely to be the result of inherent content differences due to geographical origin and specific transformations during honey-processing.

4. Conclusion

The chemical compositions of G. uralensis in the honey-processing are complex, and the geographical origin plays an important role in the quality of G. uralensis. In this study, for the first time, a total of 43 compounds (i.e., 15 WSCs, such as proline, arginine, serine, glabrol, monosaccharides, disaccharides, and trisaccharides, and 28 MSCs, such as liquiritigenin, naringenin, glycyrrhizic acid, glycyrrhetinic acid, and formononetin) of HPGU were rapidly detected and characterized by SESI-MS without the need for complex sample pretreatment. Furthermore, a significant difference in the metabolites of G. uralensis during honey-processing was confirmed. Moreover, the accumulation of saccharides, glabridin, formononetin, glabrol, and glycyrrhetinic acid in G. uralensis could be affected by processing. In addition, extremely varied levels of quality were detected in samples of HPGU from six geographical areas: Inner Mongolia, Xinjiang, Gansu, Anhui, Guangdong, and Jiangxi. The quality and contents of glycyrrhetinic acid, daidzein, acacetin and glutamine were higher in samples from Inner Mongolia. This work provided an important reference and a new method for rapidly and comprehensively analyzing the quality of plant-derived foods and Chinese herbal medicines. Furthermore, the established SESI-MS approach presents a promising platform for the real-time quality monitoring of herbal medicine processing or for the rapid authentication of botanical origins.

Supplementary Material

ao5c08675_si_001.pdf (1.5MB, pdf)

Acknowledgments

This work was financially supported by The National Natural Science Foundation of China (22504047, 22304065), Science and Technology Project of Education Department of Jiangxi Province of China (GJJ2200963), Jiangxi University of Chinese Medicine School-level Science and Technology Innovation Team Development Program (CXTD22005), Jiangxi University of Chinese Medicine School National Major Scientific Research and Cultivation Project (2023ZDPY003), and Key Project at Central Government Level: The Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources (2060302).

All data supporting the findings of this study are available within the article and its Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c08675.

  • Figures of SESI-MS experimental parameter optimization; SESI-MS spectral fingerprints of G. uralensis; SESI-MS2 spectra for all identified compounds; VIP plots of 43 compounds across honey-processing degrees; OPLS-DA score and loading plots; tables detailing HPGU samples from six geographical origins; compound identifications with VIP values; key marker compounds for processing stages and geographical discrimination; and characterization data of standard compounds (PDF)

Y.L.: conceptualization, methodology, investigation, data curation, writingoriginal draft, take Figure and the table of contents (TOC) graphic. L.Y.: formal analysis, writingreview and editing. L.W.: methodology, writingreview and editing, funding acquisition. X.F.: methodology, writingreview and editing. T.Z.: methodology, writingreview and editing. H.C.: methodology, writingreview and editing, funding acquisition. W.Z.: methodology, writingreview and editing. D.W.: conceptualization, writingreview and editing, funding acquisition.

The authors declare no competing financial interest.

References

  1. Ji X., Liu N., Huang S., Zhang C.. A Comprehensive Review of Licorice: The Preparation, Chemical Composition, Bioactivities and Its Applications. Am. J. Chin. Med. 2024;52(3):667–716. doi: 10.1142/S0192415X24500289. [DOI] [PubMed] [Google Scholar]
  2. Carmines E. L., Lemus R., Gaworski C. L.. Toxicologic evaluation of licorice extract as a cigarette ingredient. Food Chem. Toxicol. 2005;43(9):1303–1322. doi: 10.1016/j.fct.2005.01.012. [DOI] [PubMed] [Google Scholar]
  3. Xiang L., Sun W., Zhang S., Zhang H., Lv B., Qin L., Li C.. Discovery, Biomanufacture, and Derivatization of Licorice Triterpenoids. J. Agric. Food Chem. 2024;17(1):4c08110. doi: 10.1021/acs.jafc.4c08110. [DOI] [PubMed] [Google Scholar]
  4. Wang Z. F., Liu J., Yang Y. A., Zhu H. L.. A Review: The Anti-inflammatory, Anticancer and Antibacterial Properties of Four Kinds of Licorice Flavonoids Isolated from Licorice. Curr. Med. Chem. 2020;27(12):1997–2011. doi: 10.2174/0929867325666181001104550. [DOI] [PubMed] [Google Scholar]
  5. Ain N. U., Wu S., Li X., Li D., Zhang Z.. Isolation, Characterization, Pharmacology and Biopolymer Applications of Licorice Polysaccharides: Review. Materials. 2022;15(10):3654. doi: 10.3390/ma15103654. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Zhou Q., Zhang S., Geng X., Jiang H., Dai Y., Wang P., Hua M., Gao Q., Lang S., Hou L., Shi D., Zhou M.. Antioxidant Effects of Roasted Licorice in a Zebrafish Model and Its Mechanisms. Molecules. 2022;27(22):7743. doi: 10.3390/molecules27227743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ding Y., Brand E., Wang W., Zhao Z.. Licorice: Resources, applications in ancient and modern times. J. Ethnopharmacol. 2022;298:115594. doi: 10.1016/j.jep.2022.115594. [DOI] [PubMed] [Google Scholar]
  8. Xia W., Wang Y., Tao M., Li T., Fu X.. Effect of Licorice polysaccharides before and after honey-processing on improving chronic fatigue syndrome and its mechanism. Int. J. Biol. Macromol. 2024;276(Pt 2):133968. doi: 10.1016/j.ijbiomac.2024.133968. [DOI] [PubMed] [Google Scholar]
  9. Yang J., Liu Y., Cui Z., Wang T., Liu T., Liu G.. Analysis of Free Amino Acid Composition and Honey Plant Species in Seven Honey Species in China. Foods. 2024;13(7):1065. doi: 10.3390/foods13071065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Jiang L., Gong Y., Zhao Y., Dong W., Guo L., Ju J., Su N.. Changes in Biochemical Composition and Nutrient Materials in Apocynum pictum Honey During Storage. Foods. 2024;13(23):3790. doi: 10.3390/foods13233790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Sun P., Chen H., Fan X., Wang J., Lu L., Yang G., Liu J., Yao W., Ding F., Ding J., Liu J., Lu T., Chen L.. Exploring the effective components of honey-processed licorice (Glycyrrhiza uralensis Fisch.) in attenuating Doxorubicin-induced myocardial cytotoxicity by combining network pharmacology and in vitro experiments. J. Ethnopharmacol. 2024;329:118178. doi: 10.1016/j.jep.2024.118178. [DOI] [PubMed] [Google Scholar]
  12. Kong S., Li P., Verpoorte R., Wang J., Zhu C., Dai Y., Chen S.. Synergistic mechanism for the bioactivity fortification of licorice by honey. J. Ethnopharmacol. 2022;289:115048. doi: 10.1016/j.jep.2022.115048. [DOI] [PubMed] [Google Scholar]
  13. Zhu L., Li M., Yang W., Zhang J., Yang X., Zhang Q., Wang H.. Effects of Different Drying Methods on Drying Characteristics and Quality of Glycyrrhiza uralensis (Licorice) Foods. 2023;12(8):1652. doi: 10.3390/foods12081652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Peng C., Zhu Y., Yan F., Su Y., Zhu Y., Zhang Z., Zuo C., Wu H., Zhang Y., Kan J., Peng D.. The difference of origin and extraction method significantly affects the intrinsic quality of licorice: A new method for quality evaluation of homologous materials of medicine and food. Food Chem. 2021;340:127907. doi: 10.1016/j.foodchem.2020.127907. [DOI] [PubMed] [Google Scholar]
  15. Cui X., Lou L., Zhang Y., Yan B.. Study of the distribution of Glycyrrhiza uralensis production areas as well as the factors affecting yield and quality. Sci. Rep. 2023;13(1):5160. doi: 10.1038/s41598-023-31946-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Tan G., Zhu Z., Zhang H., Zhao L., Liu Y., Dong X., Lou Z., Zhang G., Chai Y.. Analysis of phenolic and triterpenoid compounds in licorice and rat plasma by high-performance liquid chromatography diode-array detection, time-of-flight mass spectrometry and quadrupole ion trap mass spectrometry. Rapid Commun. Mass Spectrom. 2010;24(2):209–218. doi: 10.1002/rcm.4373. [DOI] [PubMed] [Google Scholar]
  17. Xu T., Yang M., Li Y., Chen X., Wang Q., Deng W., Pang X., Yu K., Jiang B., Guan S., Guo D.. An integrated exact mass spectrometric strategy for comprehensive and rapid characterization of phenolic compounds in licorice. Rapid Commun. Mass Spectrom. 2013;27(21):2297–2309. doi: 10.1002/rcm.6696. [DOI] [PubMed] [Google Scholar]
  18. Farag M. A., Porzel A., Wessjohann L. A.. Comparative metabolite profiling and fingerprinting of medicinal licorice roots using a multiplex approach of GC-MS, LC-MS and 1D NMR techniques. Phytochemistry. 2012;76:60–72. doi: 10.1016/j.phytochem.2011.12.010. [DOI] [PubMed] [Google Scholar]
  19. Zhang X., Xu Y., Li T., Li H., Yu Z., Song L., Xu J.. Sequential speciation analysis of heavy metals on suspended particulate matter in water using electrochemical mass spectrometry. J. Anal. At. Spectrom. 2023;38(3):716–720. doi: 10.1039/D2JA00314G. [DOI] [Google Scholar]
  20. Zhong D., Kang L., Liu J., Li X., Zhou L., Huang L., Qiu Z.. Development of sequential online extraction electrospray ionization mass spectrometry for accurate authentication of highly-similar Atractylodis macrocephalae . Food Res. Int. 2024;175:113681. doi: 10.1016/j.foodres.2023.113681. [DOI] [PubMed] [Google Scholar]
  21. Xu J., Yu Z., Liu N., Li T., Chingin K., Wang S., Li H., Song L.. Online Sequential Determination of Organic/Inorganic Lead Speciation in PM2.5 Using Electrochemical Mass Spectrometry. Anal. Chem. 2023;95(10):4728–4734. doi: 10.1021/acs.analchem.2c05430. [DOI] [PubMed] [Google Scholar]
  22. Lin Y.-B., Wei H.-Z., Jiang S.-Y., Hohl S., Lei H.-L., Liu X., Dong G.. Accurate Determination of Barium Isotopic Compositions in Sequentially Leached Phases from Carbonates by Double Spike-Thermal Ionization Mass Spectrometry (DS-TIMS) Anal. Chem. 2020;92(3):2417–2424. doi: 10.1021/acs.analchem.9b03137. [DOI] [PubMed] [Google Scholar]
  23. Song L., Xu J., Zhong D., Chingin K., Qu Y., Chen H.. Rapid detection of metal impurities on the surfaces of intact objects with irregular shapes using electrochemical mass spectrometry. Analyst. 2019;144(11):3505–3510. doi: 10.1039/C8AN02472C. [DOI] [PubMed] [Google Scholar]
  24. Zhu T. G., Chen J., Wu D. B., Li F. L., Xu J. Q.. Analysis of phenolic and triterpenoid compounds in licorice and rat plasma by high-performance liquid chromatography diode-array detection, time-of-flight mass spectrometry and quadrupole ion trap mass spectrometry. Rapid Commun. Mass Spectrom. 2010;24(2):209–218. doi: 10.1002/rcm.4373. [DOI] [PubMed] [Google Scholar]
  25. Murata M.. Browning and pigmentation in food through the Maillard reaction. Glycoconj. J. 2021;38(3):283–292. doi: 10.1007/s10719-020-09943-x. [DOI] [PubMed] [Google Scholar]
  26. Ota M., Xu F., Li Y.-L., Shang M.-Y., Makino T., Cai S.-Q.. Comparison of chemical constituents among licorice, roasted licorice, and roasted licorice with honey. J. Nat. Med. 2018;72(1):80–95. doi: 10.1007/s11418-017-1115-4. [DOI] [PubMed] [Google Scholar]
  27. Wang D., Liang J., Zhang J., Wang Y., Chai X.. Natural Chalcones in Chinese Materia Medica: Licorice. Evidence- Based Complement Alternat. Med. 2020;2020:3821248. doi: 10.1155/2020/3821248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Fu Y., Chen J., Li Y. J., Zheng Y. F., Li P.. Antioxidant and anti-inflammatory activities of six flavonoids separated from licorice. Food Chem. 2013;141(2):1063–1071. doi: 10.1016/j.foodchem.2013.03.089. [DOI] [PubMed] [Google Scholar]
  29. Takla S. S., Shawky E., Mahgoub Y. A., Darwish R. S.. Tracking the effect of roasting and fermentation on the metabolites of licorice root (Glycyrrhiza glabra L.) using UPLC-MS analysis combined with multivariate statistical analysis. BMC Complement Med. Ther. 2023;23(1):419. doi: 10.1186/s12906-023-04239-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ota M., Xu F., Li Y. L., Shang M. Y., Makino T., Cai S. Q.. Comparison of chemical constituents among licorice, roasted licorice, and roasted licorice with honey. J. Nat. Med. 2018;72(1):80–95. doi: 10.1007/s11418-017-1115-4. [DOI] [PubMed] [Google Scholar]
  31. Su B. Z., Zhou Q., Sun L. L.. Fingerprint of ethyl acetate extract of Glycyrrhizae Radix et Rhizoma Praeparta. Chin. Tradit. Pat. Med. 2011;33(2):203–207. [Google Scholar]
  32. Shabkhiz M. A., Eikani M. H., Sadr Z. B., Golmohammad F.. Superheated water extraction of glycyrrhizic acid from licorice root. Food Chem. 2016;210:396–401. doi: 10.1016/j.foodchem.2016.05.006. [DOI] [PubMed] [Google Scholar]
  33. Wang R., Lin C., Liu J., Yu F., Gao J., Pan X.. Pressured Microwave-assisted Hydrolysis of Crude Glycyrrhizic Acid for Preparation of Glycyrrhetinic Acid. Chin. J. Chem. Eng. 2012;20(1):152–157. doi: 10.1016/S1004-9541(12)60375-9. [DOI] [Google Scholar]
  34. Ong E. S., Cheong J. S. H., Goh D.. Pressurized hot water extraction of bioactive or marker compounds in botanicals and medicinal plant materials. J. Chromatogr. A. 2006;1112(1):92–102. doi: 10.1016/j.chroma.2005.12.052. [DOI] [PubMed] [Google Scholar]
  35. Kumar V., Sharma A., Kaur R., Thukral A. K., Bhardwaj R., Ahmad P.. Differential distribution of amino acids in plants. Amino Acids. 2017;49(5):821–869. doi: 10.1007/s00726-017-2401-x. [DOI] [PubMed] [Google Scholar]
  36. Liang C., Yao Y., Ding H., Li X., Li Y., Cai T.. Rapid classification and identification of chemical components of Astragali radix by UPLC-Q-TOF-MS. Phytochem. Anal. 2022;33(6):943–960. doi: 10.1002/pca.3150. [DOI] [PubMed] [Google Scholar]
  37. Zhang X., Guo X., Zhao P., Zhang X., Gao W., Li X.. Chemometric analysis of active compounds and antioxidant and α-glucosidase inhibitory activities for the quality evaluation of licorice from different origins. Biomed. Chromatogr. 2021;35(12):e5215. doi: 10.1002/bmc.5215. [DOI] [PubMed] [Google Scholar]
  38. Fröschle M., Horn H., Spring O.. Characterization of Jatropha curcas honeys originating from the southern highlands of Madagascar. LWT. 2018;93:525–533. doi: 10.1016/j.lwt.2018.04.006. [DOI] [Google Scholar]
  39. Fernandes A., Rodrigues P. M., Pintado M., Tavaria F. K.. A systematic review of natural products for skin applications: Targeting inflammation, wound healing, and photo-aging. Phytomedicine. 2023;115:154824. doi: 10.1016/j.phymed.2023.154824. [DOI] [PubMed] [Google Scholar]
  40. Qin H., Song Z., Zhao C., Yang J., Xia F., Wang L., Ali A., Zheng W.. Liquiritigenin Inhibits Lipid Accumulation in 3T3-L1 Cells via mTOR-Mediated Regulation of the Autophagy Mechanism. Nutrients. 2022;14(6):1287. doi: 10.3390/nu14061287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kong W., Wen J., Yang Y., Qiu F., Sheng P., Yang M.. Simultaneous targeted analysis of five active compounds in licorice by ultra-fast liquid chromatography coupled to hybrid linear-ion trap tandem mass spectrometry. Analyst. 2014;139(8):1883–1894. doi: 10.1039/c3an02209a. [DOI] [PubMed] [Google Scholar]
  42. Thu V. T., Yen N. T. H., Ly N. T. H.. Liquiritin from Radix Glycyrrhizae Protects Cardiac Mitochondria from Hypoxia/Reoxygenation Damage. J. Anal. Methods Chem. 2021;2021:1857464. doi: 10.1155/2021/1857464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Li Y., Xia C., Yao G., Zhang X., Zhao J., Gao X., Yong J., Wang H.. Protective effects of liquiritin on UVB-induced skin damage in SD rats. Int. Immunopharmacol. 2021;97:107614. doi: 10.1016/j.intimp.2021.107614. [DOI] [PubMed] [Google Scholar]
  44. Zhai K.-f., Duan H., Cui C., Cao Y., Si J., Yang H., Wang Y., Cao W., Gao G., Wei Z. J.. Liquiritin from Glycyrrhiza uralensis Attenuating Rheumatoid Arthritis via Reducing Inflammation, Suppressing Angiogenesis, and Inhibiting MAPK Signaling Pathway. J. Agric. Food Chem. 2019;67(10):2856–2864. doi: 10.1021/acs.jafc.9b00185. [DOI] [PubMed] [Google Scholar]
  45. Li H., Zhang Y., Dai G., Zhaxi C., Wang Y., Wang S.. Identification and quantification of compounds with Angiotensin-converting enzyme inhibitory activity in licorice by UPLC-MS. Food Chem. 2023;429:136962. doi: 10.1016/j.foodchem.2023.136962. [DOI] [PubMed] [Google Scholar]
  46. Zhao Z., Xiao Y., Xu L., Liu Y., Jiang G., Wang W., Li B., Zhu T., Tan Q., Tang L., Zhou H., Huang X., Shan H.. Glycyrrhizic Acid Nanoparticles as Antiviral and Anti-inflammatory Agents for COVID-19 Treatment. ACS Appl. Mater. Interfaces. 2021;13(18):20995–21006. doi: 10.1021/acsami.1c02755. [DOI] [PubMed] [Google Scholar]
  47. Harikrishnan R., Devi G., Van Doan H., Jawahar S., Balasundaram C., Saravanan K., Arockiaraj J., Soltani M., Jaturasitha S.. Study on antioxidant potential, immunological response, and inflammatory cytokines induction of glycyrrhizic acid (GA) in silver carp against vibriosis. Fish. Shellfish Immunol. 2021;119:193–208. doi: 10.1016/j.fsi.2021.09.040. [DOI] [PubMed] [Google Scholar]
  48. Zuo J., Meng T., Wang Y., Tang W.. A Review of the Antiviral Activities of Glycyrrhizic Acid, Glycyrrhetinic Acid and Glycyrrhetinic Acid Monoglucuronide. Pharmaceuticals. 2023;16(5):641. doi: 10.3390/ph16050641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zuo Z., He L., Duan X., Peng Z., Han J.. Glycyrrhizic acid exhibits strong anticancer activity in colorectal cancer cells via SIRT3 inhibition. Bioengineered. 2022;13(2):2720–2731. doi: 10.1080/21655979.2021.2001925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Han S., Sun L., He F., Che H.. Anti-allergic activity of glycyrrhizic acid on IgE-mediated allergic reaction by regulation of allergy-related immune cells. Sci. Rep. 2017;7(1):7222. doi: 10.1038/s41598-017-07833-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Guo M., Wang Z., Dai J., Fan H., Yuan N., Gao L., Peng H., Cheng X.. Glycyrrhizic acid alleviates liver fibrosis in vitro and in vivo via activating CUGBP1-mediated IFN-γ/STAT1/Smad7 pathway. Phytomedicine. 2023;112:154587. doi: 10.1016/j.phymed.2022.154587. [DOI] [PubMed] [Google Scholar]
  52. Matsumoto Y., Matsuura T., Aoyagi H., Matsuda M., Hmwe S. S., Date T., Watanabe N., Watashi K., Suzuki R., Ichinose S., Wake K., Suzuki T., Miyamura T., Wakita T., Aizaki H.. Antiviral activity of glycyrrhizin against hepatitis C virus in vitro . PLoS One. 2013;8(7):e68992. doi: 10.1371/journal.pone.0068992. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ao5c08675_si_001.pdf (1.5MB, pdf)

Data Availability Statement

All data supporting the findings of this study are available within the article and its Supporting Information.

Articles from ACS Omega are provided here courtesy of American Chemical Society

RESOURCES