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ORIGINAL ARTICLE

Rapid identification of the volatile markers in lotus seed using headspace-gas chromatography ion-mobility spectrometry

Xiaoluan Tang1,2, Min Ni1, Xiao Guo3, Jiayao Liu3, Shanshuo Liu3, Dan Huang3*

1School of Pharmacy, Fujian Health College, Fuzhou, China;

2College of Chemistry, Fuzhou University, Fuzhou, China;

3State Key Laboratory of Chinese Medicine Powder and Medicine Innovation in Hunan (Incubation), Science and Technology Innovation Center, Hunan University of Chinese Medicine, Changsha, China

Abstract

To rapidly identify the volatile markers in lotus seeds, this research compared the volatile organic compounds (VOCs) of five lotus seed samples using gas chromatography-ion mobility spectroscopy (GC-IMS) and chemometric analysis. The results revealed that 49 VOCs were identified from the lotus seed samples, including 16 aldehydes, 15 alcohols, 8 ketones, 3 esters, 3 acids, 3 terpenes, and 1 heterocyclic compound, respectively. Among these, 1-pentanol M, 2-methylbutan-1-ol M, 2-methylbutan-1-ol D, and 1-hexanol M were identified as the volatile markers. Based on the VOC analysis using GC-IMS, effective differentiation of lotus seeds was achieved through chemometric techniques, such as principal component analysis (PCA), cluster analysis (CA), and partial least squares discriminant analysis (PLS-DA). 2-furaldehyde D, hexanoic acid D, and 1-propanol M were found to be the most important components contributing to the differences among the five lotus seed samples. This research demonstrates that GC-IMS coupled with chemometric analysis provides valuable reference information for the identification and authenticity evaluation of lotus seeds, helping ensure their quality on the market and offering theoretical support for their identification and quality assessment.

Key words: cluster analysis, gas chromatography ion-mobility spectrometry, lotus seed, partial least squares discriminant analysis, principal component analysis, volatile organic compounds

*Corresponding Author: Dan Huang, State Key Laboratory of Chinese Medicine Powder and Medicine Innovation in Hunan (Incubation), Science and Technology Innovation Center, Hunan University of Chinese Medicine, Changsha 410208, China. Email: huangdan110@hnucm.edu.cn

Academic Editor: Mehran Moradi, PhD., Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, Urmia University, Urmia, Iran

Received: 8 October 2024; Accepted: 23 December 2024; Published: 22 January 2025

DOI: 10.15586/qas.v17i1.1523

© 2025 Codon Publications
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)

Introduction

Lotus seeds are widely used worldwide and hold significant medicinal and nutritional value. They have a history of over 2000 years as a functional food (Yu et al., 2022; Feng et al., 2016). Lotus seeds are the dried, ripe seeds of Nelumbo nucifera Gaertn. They are harvested in the autumn when ripe, separated from the pericarp, and dried (Chinese Pharmacopoeia Commission, 2020). Lotus seed-based foods include lotus seed porridge, lotus seed cake, lotus seed biscuits, lotus seed tea, lotus seed soup, and more. These seeds are typically used to tonify the spleen, relieve diarrhea, replenish the kidneys, arrest seminal emission, nourish the heart, and induce tranquility. They are also used to treat leukorrhea, palpitations, and insomnia. Lotus seeds contain a variety of phytochemicals, including alkaloids, flavonoids, polysaccharides, essential oils, glycosides, polyphenols, and triterpenes (Chen et al., 2012; Huang et al., 2010; Zheng et al., 2010). Lotus seeds are abundant in China, with varieties such as Jianning lotus seeds from Jianning, Fujian province; Xiangtan lotus seeds from Xiangtan, Hunan province; and Xuanping lotus seeds from Xuanping, Zhejiang province, collectively known as the three major lotus seeds in China. Additionally, lotus seeds from Guangchang, Jiangxi province, and lotus seeds from Honghu, Hubei province, have been recognized as national geographical indication products in China.

Volatile organic compounds (VOCs) are key components (either harmful or beneficial) in food, and the types and concentrations of volatile components can vary significantly across different food types. VOCs are closely related to the quality, flavor, and aroma of food. They play a crucial role in identifying different varieties and origins of food, providing a theoretical basis for food quality control, origin traceability, and flavor analysis (Yin et al., 2024; Duan et al., 2023). Gas chromatography–mass spectrometry (GC-MS) has been used to analyze the volatile constituents of Nelumbinis stamen and Nelumbinis plumula in different medicinal parts of the lotus (Wang et al., 2020); however, there is currently no research analyzing the VOCs of lotus seeds for identification purposes.

Several fast methods are currently available for identifying plant-based foods, including gas chromatography-flame ionization detection (GC-FID) (Welke et al., 2022; Nedeltcheva-Antonova et al., 2022), gas chromatography with electron capture detection (GC-ECD) (da Silva et al., 2015; Pendem et al., 2010), gas chromatography–mass spectrometry (GC-MS), and near-infrared spectroscopy (NIRS). In addition to these techniques, gas chromatography–ion mobility spectroscopy (GC-IMS) may serve as a feasible alternative to traditional flavor analysis methods (Wang et al., 2019; Zhu, et al., 2023; Duan et al., 2023; Li et al., 2022; Tian et al., 2020; Valli et al., 2020).

GC-IMS can qualitatively analyze different plant-based foods and enable the rapid and accurate identification and separation of these foods through fingerprint recognition and isomer differentiation of VOCs. The robustness and simplicity of the instrument significantly enhance the application of GC-IMS in food certification, processing, storage monitoring, identification of illegal additives, and detection of harmful compounds.

GC-IMS is a powerful technique that combines gas chromatography and ion mobility spectroscopy for the separation and sensitive detection of VOCs. It is characterized by fast response speed, high sensitivity, ease of operation, and low cost. In the field of food analysis, it has been widely used for various purposes, including flavor and quality analysis, trace detection of toxic chemicals, and adulteration identification. The rapid, non-destructive, high-throughput detection and screening of volatile components play a crucial role in food flavor analysis (Valli et al., 2020; Zhang et al., 2020; Tian et al., 2020; Yan et al., 2024). For example, GC-IMS has been employed in flavor analysis to evaluate VOCs in apple cider (Wu et al., 2023), tea (Xu et al., 2023), and yellow croaker (Zhao et al., 2021).

PCA is an unsupervised machine learning and statistical technique used to identify patterns and relationships in large datasets. It is a widely used method for data and dimensionality reduction, which involves reducing the number of variables in the dataset while preserving as much original information as possible (Younes et al., 2023). CA is an unsupervised machine learning algorithm designed to identify subgroups in a dataset, characterized by discrete differences. Due to this unique feature, CA has gradually gained popularity over traditional statistical analysis (Dalmaijer et al., 2022). It has been widely used to analyze changes in food ingredients (Duan et al., 2023). PLS-DA is a supervised analysis method that can more effectively reveal differences and similarities between groups compared to unsupervised PCA analysis (Yin, et al., 2024).

In this study, GC-IMS technology was combined with principal component analysis (PCA), cluster analysis (CA), and partial least squares discriminant analysis (PLS-DA) to detect and analyze VOCs in five lotus seed varieties from China, enabling the rapid identification of volatile markers in lotus seeds. This approach provides valuable insights for the identification of lotus seeds, which helps ensure their quality in the market and offers theoretical support for the quality evaluation of lotus seeds.

Materials and Methods

Materials

The five types of lotus seeds (dried, pre-packaged) were purchased from Jingdong, Beijing, China, and then crushed into powder. The lotus seeds from Jianning, Fujian province of China (Jianlian) were designated as LZ-01; the lotus seeds from Xiangtan, Hunan province of China (Xianglian) were designated as LZ-02; the lotus seeds from Xuanping, Zhejiang province of China (Xuanlian) were designated as LZ-03; the lotus seeds from Guangchang, Jiangxi province of China (Guangchang white lotus seed) were designated as LZ-04; and the lotus seeds from Honghu, Hubei province of China (Honghu lotus seed) were designated as LZ-05. A voucher specimen (HNUCM2024-LZ001) was stored in the sample room of the Science and Technology Innovation Center at Hunan University of Chinese Medicine.

Analysis by GC–IMS

Sample Preparation

An amount of 0.5 g of the powder from each sample was placed in a 20 mL headspace vial.

Headspace conditions

The samples were incubated at 80 °C for 15 min. After incubation, 500 μL were injected into the headspace using non-shunt injection, and the vials were rotated at 500 revolutions per minute (rpm) for 20 mins. The temperature of the injection needle was set to 85°C.

GC conditions

The instrument used in this study was the FlavorSpec® Gas Phase Ion Mobility Spectrometer from GAS (Dortmund, Germany). An MXT-WAX GC Metal Capillary Column (15 m × 0.53 mm × 1.0 μm, Restek Inc, USA) was used for chromatography. The column temperature was set to 60°C. Initially, 2.00 mL/min of high-purity N2 was used as the carrier gas. Over the course of 8 min, the flow rate increased linearly to 10.00 mL/min, then to 100.00 mL/min over the next 10 min, and was held for an additional 10 min. The chromatography runtime was 30 min, and the injection temperature was set to 80°C.

IMS conditions

The analysis was conducted using tritium (3H), a 53 mm drift tube, with an electric field intensity of 500 V/cm, a drift tube temperature of 45°C, and high-purity N2 (99.999%) at a flow rate of 150 mL/min, in positive ionization mode.

Statistical analysis

This instrument is paired with the analysis software Vocal, which displays spectra and data for both qualitative and quantitative analysis. Databases from NIST and IMS are included in the application software to facilitate the qualitative analysis of substances. A Porter plugin was used to analyze and compare the differences in spectral characteristics between samples, including three-dimensional spectra, two-dimensional top views, and difference spectra. VOCs were intuitively and quantitatively compared using the gallery plot plugin. Principal component analysis (PCA) was performed using OriginPro 2023b software, cluster analysis (CA) was conducted using TBtools, and partial least squares discriminant analysis (PLS-DA) was performed using SIMCA.

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Results and Discussion

GC-IMS Analysis of VOCs in five lotus seed samples

The three-dimensional spectrum of the VOCs is shown in Figure 1, with the samples labeled as LZ-01, LZ-02, LZ-03, LZ-04, and LZ-05. The x, y, and z axes in the figure represent drift time, gas chromatography retention time, and signal peak intensity, respectively. From the three-dimensional spectrum of the five lotus seed samples, it can be directly observed that there are distinct differences in the VOCs among the samples from different sources.

Figure 1. The 3D spectra of VOCs from five lotus seed samples.

From Figure 1, it can be visually observed that there are certain differences in the VOCs among the samples from different sources. For easier comparison, the top view is shown in Figure 2. Each point on either side of the RIP peak represents a volatile organic compound. The color indicates the peak intensity of a substance, ranging from blue to red, with darker colors signifying higher peak intensity. The background of the figure is blue, and the red vertical line at 1.0 represents the RIP peak (reactive ion peak). The vertical axis represents the gas chromatography retention time (s), and the horizontal axis represents the relative drift time (normalized treatment).

Figure 2. The 2D spectra of VOCs from five lotus seed samples. The graph is blue, with red vertical line representing the RIP (normalized reaction ion peak) at 1.0 on the horizontal axis. Retention time in gas chromatography is displayed on the vertical axis, and drift time is displayed on the horizontal axis (normalized, a.u.). Each point on either side of the RIP peak represents a volatile organic compound. The colors range from blue to red, with darker colors indicating higher peak intensities.

To further visually compare the differences in their VOCs, the spectra of the LZ-01 sample were selected as a reference, and the spectra of the other samples were subtracted from the reference to generate a comparison chart of the differences between the samples, as shown in Figure 3. Based on Figure 2, if the VOC content in the target sample is the same as that in the LZ-01 sample, the point cancels out and is displayed as white. If the concentration of the substance in the target sample is higher than that in the LZ-01 sample, it is displayed as red; if it is lower, it is displayed as blue.

Figure 3. Differences in the 2D spectra of VOCs from five lotus seed samples.

From Figure 1 to Figure 3, it is evident that there are certain differences in the VOCs of the five lotus seed samples. The specific differences in VOCs will be further analyzed based on fingerprint spectra and other data.

Qualitative analysis of VOCs in five lotus seed samples

In the GC-IMS two-dimensional spectrum, the difference in VOC content between the five groups of samples is reflected in the concentration of each volatile substance. Based on this, qualitative analysis of the VOCs was conducted by combining the NIST and IMS databases built into the software. This study detected a total of 49 VOCs, including 16 aldehydes, 15 alcohols, 8 ketones, 3 esters, 3 acids, 3 terpenes, and 1 pyridine. The qualitative analysis results of the VOCs are shown in Table 1.

Table 1. Results of the VOC analysis of five lotus seed samples.

No Compounds CAS Molecular Formula RI Rt/s Dt/ms
1 Nonanal M C124196 C9H18O 1105.9 790.521 1.467
2 Nonanal D C124196 C9H18O 1105.1 788.781 1.93816
3 1-Hexanol M C111273 C6H14O 882.5 367.221 1.33177
4 1-Hexanol D C111273 C6H14O 880.3 364.417 1.65191
5 1-Hexanol P C111273 C6H14O 876.5 359.744 1.98831
6 Benzaldehyde D C100527 C7H6O 967.3 512.602 1.46735
7 Benzaldehyde M C100527 C7H6O 966.7 511.353 1.14862
8 Butyrolactone M C96480 C4H6O2 925.3 433.327 1.08407
9 Butyrolactone D C96480 C4H6O2 926 434.575 1.2979
10 Cyclohexanone M C108941 C6H10O 903.5 397.122 1.16073
11 Cyclohexanone D C108941 C6H10O 901.2 393.377 1.45525
12 Benzeneacetaldehyde C122781 C8H8O 1054.1 680.516 1.25621
13 Hexanal D C66251 C6H12O 792.9 270.599 1.5639
14 1-hexanal M C66251 C6H12O 794.6 272.137 1.26461
15 Butanoic acid C107926 C4H8O2 804.4 281.367 1.16875
16 2-Methylbutan-1-ol D C137326 C5H12O 735.6 218.623 1.50268
17 2-Methylbutan-1-ol M C137326 C5H12O 747.9 228.978 1.23704
18 1-Pentanol D C71410 C5H12O 766.2 245.35 1.51093
19 1-Pentanol M C71410 C5H12O 770.2 249.092 1.25641
20 1-propanol D C71238 C3H8O 617.3 142.437 1.24396
21 1-propanol M C71238 C3H8O 622.9 145.244 1.10563
22 1-Propanol, 2-methyl- D C78831 C4H10O 642.4 155.535 1.36845
23 1-Propanol, 2-methyl- M C78831 C4H10O 644.1 156.471 1.17894
24 2-Pentanone D C107879 C5H10O 694.8 187.345 1.3726
25 2-Pentanone M C107879 C5H10O 692.8 185.941 1.11393
26 3-Methyl-3-buten-1-ol C763326 C5H10O 715.8 202.782 1.16649
27 3-Methyl butanal M C590863 C5H10O 673.1 173.311 1.18309
28 3-Methylbutanal D C590863 C5H10O 671.6 172.376 1.40995
29 Ethyl Acetate C141786 C4H8O2 631.6 149.749 1.34133
30 Butanal C123728 C4H8O 625.1 146.415 1.2973
31 2,3-Butanedione C431038 C4H6O2 615.1 141.34 1.1714
32 2-Hexenal D C505577 C6H10O 855.2 334.622 1.51414
33 2-Hexenal M C505577 C6H10O 855.9 335.331 1.18411
34 Hexanoic acid D C142621 C6H12O2 970 518.194 1.63352
35 Hexanoic acid M C142621 C6H12O2 965.5 508.98 1.2871
36 Pinene C127913 C10H16 978.4 535.914 1.22039
37 2-furaldehyde M C98011 C5H4O2 833.3 310.524 1.0858
38 2-furaldehyde D C98011 C5H4O2 837.3 314.776 1.32455
39 3-Carene M C13466789 C10H16 1040.5 654.279 1.22039
40 3-Carene D C13466789 C10H16 1040.1 653.57 1.29295
41 Octanal C124130 C8H16O 982.2 543.963 1.40269
42 6-Methyl-5-hepten-2-one C110930 C8H14O 994.4 571.148 1.16736
43 1-Octen-3-ol C3391864 C8H16O 986.1 552.701 1.16039
44 2-Ethyl-1-hexanol C104767 C8H18O 1013.5 605.13 1.40618
45 2-Heptanone C110430 C7H14O 897.5 387.648 1.25103
46 1-Hydroxy-2-propanone C116096 C3H6O2 691.8 185.219 1.22922
47 n-pentanal C110623 C5H10O 702.5 192.84 1.42591
48 2-Methyl-2-propanol C75650 C4H10O 541.2 109.009 1.14551
49 Pyridine C110861 C5H5N 727.9 212.316 1.24637

The substance suffixes M, D, or P represent monomers, dimers, and polymers of the same substance, respectively. Dt represents drift time, Rt represents retention time, and RI represents retention index.

Fingerprint analysis of VOCs in five lotus seed samples

The VOCs in the samples were further compared, and a fingerprint analysis was performed on all the volatile substances, as shown in Figure 4. The results of the comparison and analysis of the VOCs in samples LZ-01, LZ-02, LZ-03, LZ-04, and LZ-05 are also presented in Figure 4. As indicated in the purple box, nonanal, 2-ethyl-1-hexanol, 1-propanol, and benzeneacetaldehyde have a higher content in LZ-01. As shown in the red box, 1-hexanol, 1-pentanol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, pyridine, 2-hexenal, octanal, 2-furaldehyde, and 2-methylpropan-1-ol have a higher content in LZ-02 and LZ-05. As shown in the green box, 2-methyl-2-propanol, ethyl acetate, butyrolactone, 2-heptanone, cyclohexanone, and butanal have a higher content in LZ-01 and LZ-03. As indicated in the orange box, 1-octen-3-ol, benzaldehyde, 1-hexanal, butanoic acid, and 3-methyl-3-buten-1-ol have a higher content in LZ-01 and LZ-04. As shown in the yellow box, 2,3-butanedione, 1-hydroxy-2-propanone, 3-carene, and pinene have a higher content in LZ-04. Among these, 1-pentanol M, 2-methylbutan-1-ol M, 2-methylbutan-1-ol D, and 1-hexanol M are the volatile markers.

Figure 4. Fingerprints of VOCs from five lotus seed samples. Each row on the graph represents a sample with selected signal peaks. Each column represents the signal peak of the same VOC across samples. The graph provides complete information on the VOCs in each sample and highlights the differences between samples.

Chemometric analysis

Principal Component Analysis (PCA)

This study used OriginPro 2023b software to conduct a PCA on the VOCs in the five lotus seed samples. The results are shown in Figure 5, where different colors represent the different lotus seed samples. The PCA arranges the principal component scores from high to low based on the contribution rates and visualizes the scores of the first two principal components. According to Figure 5, PCA1 accounts for 59.3%, PCA2 accounts for 18.7%, and the cumulative contribution of the two principal components is 78%. The larger the distance between samples from different sources, the more significant the difference in VOCs among the samples. Conversely, the closer the distance between samples, the smaller the difference in VOCs. The distance between LZ-02 and LZ-05, as well as between LZ-01 and LZ-03, is relatively small, indicating that these samples have similar volatile organic compounds.

Figure 5. Results of the PCA of five lotus seed samples.

Cluster Analysis (CA)

To further analyze the differences in the VOCs among the five lotus seed samples, CA graphs were generated. Forty-nine VOCs from the lotus seed samples LZ-01, LZ-02, LZ-03, LZ-04, and LZ-05 were processed and imported into TBtools software for CA. The results are shown in Figure 6. It can be observed that LZ-05 and LZ-02 have the smallest difference in VOCs, while LZ-01, LZ-03, and LZ-04 have relatively small differences in their components, which is consistent with the results from the PCA plot. Among the components of LZ-04, 3-carene M, 3-carene D, pyridine, and 1-hydroxy-2-propanone have the highest content. In contrast, 2-ethyl-1-hexanol, 1-propanol D, nonanal D, and nonanal M are relatively abundant in the LZ-01 components. The results from the CA clearly reflect the differences in the content of each volatile organic compound among the different groups.

Figure 6. Results of the CA of five lotus seed samples.

Partial Least Squares Discriminant Analysis (PLS-DA)

SIMCA software was used to perform PLS-DA on the various samples using a supervised pattern recognition method to observe the differences in the VOCs of the lotus seed samples LZ-01, LZ-02, LZ-03, LZ-04, and LZ-05. The results are shown in Figure 7(A). From the figure, it can be seen that the LZ-02 and LZ-05 samples are on the left side of the coordinate axis, with the distance between them being closer than to the other samples, indicating the smallest difference in VOCs. The LZ-01, LZ-03, and LZ-04 samples are all on the right side of the coordinate axis, but the LZ-01 and LZ-03 samples are closer to each other. LZ-04 does not overlap with the other samples and shows a clear distinction, indicating that the difference in VOCs between the LZ-01 and LZ-03 samples is small, while there is a significant difference between the LZ-04 samples and the other samples. This is consistent with the PCA results. Additionally, according to the processed data, R2X = 0.963, R2Y = 0.982, and Q2 = 0.934. When R2 and Q2 are greater than 0.5, it indicates that the established model has relatively accurate generalization and predictive ability. A total of 200 permutation tests were conducted on the established PLS-DA model, with an R2 intercept value of 0.271 and a Q2 intercept value of -0.602 (<0), indicating that the model results were not overfitting Figure 7(B). Furthermore, a variable projection importance map was constructed, as shown in Figure 7(C). The VIP value is a quantitative indicator of the influence of each volatile component on the lotus seed samples. The larger the VIP value, the more important the component. Variables with a VIP value greater than 1 are considered more important, while those with a VIP value less than 0.5 are considered unimportant. From Figure 7(C), it can be seen that the VIP values of 2-furaldehyde D, hexanoic acid D, 1-propanol M, nonanal D, ethyl acetate, nonanal M, 2-pentanone D, 1-propanol D, 2,3-butanedione, 3-carene M, and other substances are greater than 1, indicating that they are important components affecting the differences in the five lotus seed samples. The difference in VOCs between the LZ-02 and LZ-05 samples and the LZ-01 and LZ-03 samples is relatively small, and these results are consistent with the PCA findings.

Figure 7. PLS-DA analysis of VOCs from five lotus seed samples: (A) score plots; (B) cross-validation plot with 200 permutation tests; (C) VIP diagram.

Modern research has shown that there are significant differences in the types and quantities of chemical components in food or traditional Chinese medicine from different geographical sources (Li et al., 2022; Hu et al., 2023; Zhang et al., 2023; Sammarco et al., 2023; Hai et al., 2023; Ji et al., 2023).

According to the research results, the differences in VOC content across the five groups of lotus seeds may be linked to their soil conditions, climate environment, and water quality. By analyzing the VOCs in lotus seeds using the GC-IMS method, lotus seeds can be identified.

Currently, there is limited research on the VOCs of lotus seeds in China. This study will contribute to the development of a range of lotus seed products rich in functional ingredients and promote the high-value utilization of lotus seeds.

Conclusions

Through the analysis of VOCs in five lotus seed varieties, it was found that there are distinct differences in the GC-IMS profiles among lotus seed varieties from different sources. Based on the feature components identified using the graph plugin software, a fingerprint map was created, which indicated that 1-Pentanol M, 2-Methylbutan-1-ol M, 2-Methylbutan-1-ol D, and 1-hexanal M are volatile markers. After performing PCA, CA, and PLS-DA, the lotus seed varieties from different sources were effectively and quickly distinguished. Additionally, VIP values were used in this experiment to assess the influence of each volatile component on the lotus seed samples, providing a basis for quality characteristic evaluation and germplasm identification. The combination of GC-IMS and chemometric analysis allows for efficient and rapid identification of lotus seeds. This research demonstrates that GC-IMS coupled with chemometric analysis provides valuable reference data for the identification and evaluation of lotus seeds from different sources, which aids in ensuring the quality of lotus seeds in the market and offers theoretical support for the identification and quality evaluation of lotus seeds from different sources.

Author Contributions

Conceptualization, X.T.; methodology, X.T., and M.N.; software, M.N., S.L.; validation, X.G.; formal analysis, X.G.; investigation, J.L.; resources, J.L.; data curation, S.L.; writing—original draft preparation, X.T., X.G., and J.L.; writing—review and editing, X.T. and D.H.; visualization, M.N.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Funding

This research was funded by the Hunan Provincial Natural Science Foundation of China (No. 2024JJ7358) and the Key Discipline Project on Chinese Pharmacology of Hunan University of Chinese Medicine [202302].

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