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. 2025 Dec 27;33:103462. doi: 10.1016/j.fochx.2025.103462

Metabolomic and phytochemical insights into tree age effects on Lycium barbarum fruits quality

Ningli Wang a,b, Hao Meng c, Tingting Zhao b, Dong Pei b, Duolong Di b, Yingli Yang a,⁎, Jianfei Liu b,⁎
PMCID: PMC13216721  PMID: 42221994

Abstract

Tree age plays a crucial role in determining metabolite accumulation in Lycium barbarum L. fruits (LBF), yet the optimal age for achieving peak nutraceutical value remains unclear. In this study, we systematically investigated the effects of tree age (2, 4, 6, 8, 10, and 15 years; Y2–Y15) on the bioactive compounds and metabolic profiles of LBF. The results revealed pronounced age-dependent variations: total polysaccharides and carotenoids peaked at Y6 and Y8, whereas total polyphenols and flavonoids reached their highest levels at Y10. Distinct accumulation patterns were also observed for individual metabolites, with zeaxanthin dipalmitate (ZDP) showing a maximum at Y6, AA-2βG at Y8, and betaine at Y10. Non-targeted metabolomic analysis further indicated highly similar metabolic profiles among Y4–Y10, as reflected by their close clustering in the PCA model. Polysaccharides extracted from Y6–Y8 exhibited the strongest hepatoprotective activity, significantly improving oxidative stress markers and liver injury indicators. Specifically, high-dose polysaccharides extracted from Y8 reduced MDA levels by over 40 %, while those from Y6 decreased ALT and AST levels by approximately 32 % and 40 %, respectively, relative to the model group. Collectively, these findings identify Y6–Y10 as the optimal harvesting window for obtaining LBF with superior nutraceutical potential.

Keywords: Tree age, Lycium barbarum fruits, Metabolites, Bioactive compounds, Lycium barbarum polysaccharides

Highlights

  • •

    Systematic investigation of tree age effects on phytochemical composition and metabolomic profiles of Lycium barbarum fruits.

  • •

    Tree age strongly shapes phytochemical composition and metabolomic profiles of Lycium barbarum fruits.

  • •

    Polysaccharides from Lycium barbarum fruits harvested from 6- to 8-year-old trees exhibited optimal hepatoprotective activity.

1. Introduction

Lycium barbarum L., native to China, is widely cultivated in alkaline and sandy loam soils and is internationally recognized for its nutrient-rich fruits. Lycium barbarum fruits (LBF) contain abundant bioactive compounds, including Lycium barbarum polysaccharides (LBPs), carotenoids, flavonoids, alkaloids, organic acids, and other compounds. These constituents exhibit diverse biological activities, such as antioxidant, hepatoprotective, immunomodulatory, glucose-regulatory, neuroprotective, and anti-tumor effects (Bouyahya et al., 2021; Xiao et al., 2012). Among them, LBPs, which exist as glycoconjugates bound to small amounts of proteins, peptides, or lipids, are the major active fraction and are well-documented for their antioxidant, hypoglycemic, and hepatoprotective functions. The carotenoids of LBF comprise free carotenoids, carotenoid esters (e.g., zeaxanthin dipalmitate), and carotenoid glycosides, which contribute to eye, skin, and neurological health. Flavonoids, with strong antioxidant and anti-tumor properties, also modulate reproductive functions and gene expression. Several bioactive compounds, including betaine, N-trans-feruloyloctopamine (NFT), and 2-O-β-d-glucopyranosyl-l-ascorbic acid (AA-2βG), have attracted considerable attention in recent years.

The chemical composition and resulting quality of fruits are shaped by a complex interplay of intrinsic and extrinsic factors (Wang et al., 2022). Extensive research has focused on LBF extraction, separation, and bioactivity evaluation, as well as on the influence of genotype, geographical origin, and maturity stage on its chemical composition and quality (Donno et al., 2015; Gong et al., 2022; Li et al., 2017; Ma et al., 2023). These studies collectively show that environmental and genetic factors can markedly alter the profile and concentration of LBF bioactive compounds. However, another important but less explored factor is tree age. Tree age is known to affect fruit quality in many perennial crops through complex physiological and metabolic mechanisms. For instance, in mango (Mangifera indica), sugar content follows a unimodal trend with age, whereas phenolic content steadily declines (Meena & Asrey, 2018a). In Ziziphus jujuba, the highest citric and malic acid concentrations appear in trees of specific ages depending on fruit development stage (Wang, Wang, et al., 2023). In citrus, fruits from certain intermediate-age trees show higher phenolic content and antioxidant activity than those from younger or older trees (Khalid et al., 2016b). Such findings highlight that tree age can influence sugar–acid balance, phenolic metabolism, and antioxidant capacity, thereby impacting both nutritional and medicinal properties. Despite the growing recognition of this relationship in other fruit crops, systematic investigations of age-dependent variation in LBF bioactive compounds and metabolic profiles remain scarce. This knowledge gap limits our understanding of how cultivation practices and orchard management strategies might be optimized to improve LBF quality.

Metabolomics has emerged as a powerful and versatile analytical platform for characterizing plant phytochemical diversity and elucidating biological mechanisms at the molecular level (Shi et al., 2022). By enabling the simultaneous detection, identification, and quantification of a broad range of primary and secondary metabolites, metabolomics provides a holistic view of metabolic networks and their dynamic responses. This approach has been successfully employed to dissect metabolic pathways, identify quality-related biomarkers, and uncover mechanisms underlying developmental stages, environmental stresses, and genetic variation (Lang et al., 2023; Wu et al., 2024). Coupled with multivariate statistical analysis and pathway enrichment tools, metabolomics allows researchers to capture subtle metabolic shifts that are often undetectable through conventional chemical analyses.

In this study, an integrated analytical strategy combining spectrophotometry, ion chromatography, high-performance liquid chromatography (HPLC), and non-targeted metabolomics was employed to investigate LBF harvested from trees aged 2 to 15 years. This approach enabled not only the quantification of key metabolites—including LBPs, carotenoids, flavonoids, and alkaloids—but also a comprehensive characterization of LBPs with respect to conjugation type, monosaccharide composition, molecular weight distribution, and in vitro hepatoprotective activity (Fig. 1). By integrating data from metabolomic analyses and phytochemical insights, this study establishes a consistent relationship between tree age and LBF quality. Consequently, these findings provide guidance for the functional food industry: to select LBF from the 6- to 10-year age window for the development of high-efficacy products.

Fig. 1.

Fig. 1

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Experimental overview of Lycium barbarum fruits (LBF) quality from different tree ages. Note: Y2–Y15: LBF from trees aged 2–15 years. AA-2βG: 2-O-β-d-glucopyranosyl-l-ascorbic acid.

2. Materials and methods

2.1. Materials and chemicals

Lycium barbarum fruits (LBF) were obtained from Zhongning, China, and its identification was conducted by Professor Ying Wang from the South China Botanical Garden, Chinese Academy of Sciences. The samples were stored at −20 °C and protected from light and moisture. LBF were collected from trees of six different ages (2, 4, 6, 8, 10, and 15 years), designated as Y2, Y4, Y6, Y8, Y10, and Y15 based on planting records (Gong et al., 2022). Sampling was performed using a randomized plum-blossom distribution pattern to ensure representativeness, with detailed records of cultivar, cultivation age, and location maintained for all samples. Gallic acid, rutin, β-carotene, NFT, and zeaxanthin were purchased from Chengdu Pusi Biotechnology Co., Ltd. (Chengdu, China). Zeaxanthin dipalmitate (ZDP) and AA-2βG were isolated in-house by Ningli Wang. Reference substances, including betaine, mannose (Man), galactose (Gal), glucose (Glc), rhamnose (Rha), glucuronic acid (GlcA), galacturonic acid (GalA), glucosamine (GlcN), xylose (Xyl) and arabinose (Ara) were obtained from the China National Institute for Drug and Food Control. Bovine serum albumin (BSA) was supplied by Beyotime Biotechnology (Nanjing, China). The aspartate aminotransferase (AST), alanine aminotransferase (ALT), malondialdehyde (MDA), nitric oxide (NO), and glutathione (GSH) assay kits were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). HPLC-grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany), while other analytical reagents were obtained from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China).

2.2. Analysis of LBF extract content

2.2.1. Total polysaccharide analysis

Total polysaccharide content was determined in accordance with the Chinese Pharmacopoeia (ChP., 2020), utilizing glucose as a standard. A precisely weighed sample of coarse powder (0.5 g) was refluxed with 100 mL of ether for 1 h. Following cooling, the ether was discarded, and the residue was dried. The dried residue was then refluxed again with 100 mL of 80 % ethanol for 1 h. The resulting solution was filtered and washed with hot 80 % ethanol. Subsequently, the residue was transferred into a flask containing 150 mL of water and refluxed for 2 h. The mixture was filtered once more, and the filtrate was diluted to a final volume of 250 mL. Finally, 1 mL of the solution was reacted with 1 mL of water, 1 mL of 5 % phenol, and 5 mL of sulfuric acid. After standing for 10 min and heating at 40 °C for 15 min, the absorbance was measured at 490 nm against a blank reference.

2.2.2. Total carotenoid analysis

Carotenoid extraction using a hydrophobic deep eutectic solvent (H-DES) was performed according to a modified version of our previous protocol (Wei et al., 2022). In brief, H-DES composed of L-menthol and octyl alcohol in a 1:2 M ratio, was stirred at 70 °C until a clear solution was achieved. Each sample powder (4.0 g) was combined with H-DES at a ratio of 1:20 (w/v) and processed using a high-speed disperser emulsifier at 10,000 r/min for 5 min in the dark. Following centrifugation at 3500 r/min in the dark for 10 min, the supernatant was made up to a final volume of 100 mL with H-DES. A β-carotene standard (4 mg) was dissolved in H-DES and diluted to 50 mL to prepare calibration solutions. The absorbance was measured at 450 nm against an H-DES blank, and the total carotenoid content was quantified using the regression equation of the β-carotene standard.

2.2.3. Total polyphenol analysis

The total polyphenol content of LBF was assessed using spectrophotometry, with gallic acid as the standard for the calibration curve, as detailed by Liu et al. (2017). Specifically, a 1.0 g dried sample was ultrasonically extracted with 30 mL of 70 % ethanol for 90 min. After centrifugation at 9000 r/min for 5 min, the supernatant was diluted tenfold. Subsequently, 1.0 mL of the diluted extract was mixed with 6.0 mL of water and 1.0 mL of 1.0 mol/L Folin-Ciocalteu reagent and incubated for 6 min. Following this, 4.0 mL of a 10.6 % sodium carbonate solution was added and incubated for 60 min at room temperature. After the mixture was diluted to a final volume of 25 mL, the absorbance was measured at 760 nm. Each measurement was replicated three times, and the mean value was calculated.

2.2.4. Total flavonoid analysis

The total flavonoid content was determined using rutin as the standard, following the method outlined by Liu et al. (2017). A 1.0 g dried sample was initially defatted with 50 mL of petroleum ether through 30 min of ultrasonication, followed by centrifugation and the disposal of the supernatant. The residue was then extracted twice with 40 mL of ethanol at 70 °C using 120 W ultrasonication for 30 min each time. The combined extracts were filtered, transferred to a 100 mL volumetric flask, and brought to volume with ethanol. A 10 mL aliquot of this solution was subsequently mixed with 0.8 mL of sodium nitrite, 0.8 mL of aluminum nitrate, and 10.0 mL of sodium hydroxide. The mixture was diluted to 25 mL with ethanol, incubated for 15 min, and the absorbance was measured at 510 nm. Each measurement was replicated three times, and the mean value was calculated.

2.3. Quantification of bioactive compounds in LBF

2.3.1. Quantification of ZDP and zeaxanthin

LBF powder (1.000 ± 0.001 g) was ultrasonically extracted with 20 mL petroleum ether for 30 min, followed by centrifugation at 4000 r/min for 10 min to obtain the supernatant. This extraction procedure was repeated. Combined supernatants were diluted to 50 mL with petroleum ether in volumetric flask, and then filtered through a 0.22-μm filter membrane. The resulting filtrate was directly subjected to HPLC analysis for ZDP and zeaxanthin quantification according to the validated method (Gong et al., 2021). The analysis employed a YMC C30 column (4.6 mm i.d. × 150 mm, 5 μm) with mobile phase A (MeOH/MTBE/H2O, 92:4:4, v/v/v) and B (MTBE/MeOH/H2O, 90:6:4, v/v/v). Chromatographic separation used a gradient program: 0–10 min, 10 % to 75 % B; 10–40 min, 75 % B. Other chromatographic conditions were as follows: flow rate, 0.5 mL/min; detection wavelength, 450 nm; column temperature, 25 °C; injection volume, 20 μL.

2.3.2. Quantification of betaine and NFT

The quantitative determination of betaine in LBF powder was performed as follows (ChP., 2020). Precisely 1.000 ± 0.001 g of the powder was subjected to reflux extraction with 50 mL of methanol at 85 °C for 1 h. After cooling to room temperature, the extract was adjusted to its original weight with methanol. The extract was purified using a solid-phase extraction (SPE) cartridge (packed with 2 g of basic alumina), which was eluted with 30 mL of ethanol. Following N2-evaporation, the residue was reconstituted with water in a 2.0 mL volumetric flask, filtered, and then analyzed by HPLC. The HPLC analysis was performed using a Hypersil NH2-S column (4.6 mm i.d. × 250 mm, 5 μm) under isocratic conditions with a mobile phase of acetonitrile/water (85:15, v/v) at a flow rate of 1.0 mL/min, with detection at 195 nm and an injection volume of 10 μL. For the quantification of NFT, following Khan et al. (2021) with modifications, dried LBF powder (1.000 ± 0.001 g) was subjected to ultrasonic extraction using 10 mL of 70 % methanol at 40 kHz for 30 min. Following being made up to the original weight with 70 % methanol, the extract underwent purification via Oasis® HLB solid-phase extraction (SPE) (3 cc, 60 mg), preconditioned with 5 mL of methanol and eluted with three 2 mL portions of 70 % methanol. The purified extract was subsequently analyzed using a Thermo Scientific™ Acclaim™ 120 C18 column (4.6 mm i.d. × 250 mm, 5 μm) under isocratic conditions with a mobile phase of 0.03 % H3PO4/acetonitrile (65:35, v/v) at a flow rate of 1.0 mL/min and a temperature of 30 °C, with detection at 318 nm and a 5 μL injection volume.

2.3.3. Quantification of AA-2βG

The quantification of AA-2βG was performed using an Agilent 1290 UHPLC system according to Zhu et al. (2022) with some modifications. Chromatographic separation utilized a ZORBAX SB-Aq column (2.1 mm i.d. × 100 mm, 1.8 μm) with a 10-min gradient elution of 0.1 % H3PO4 and methanol (98:2, v/v). The operating conditions were as follows: column temperature, 25 °C; flow rate, 0.2 mL/min; detection wavelength, 260 nm. Analytes were identified by comparison of their retention times with those of authentic standards under identical conditions.

2.4. Metabolomics analysis of LBF

For the extraction of metabolites, 100 mg of LBF powder was combined with 800 μL of a specially formulated extraction solution, which consisted of a 4:1 (v:v) mixture of methanol and water. This solution also included four internal standards at a concentration of 0.02 mg/mL, including L-2-chlorophenylalanine among others. To ensure thorough extraction, the mixture was processed using a Wonbio-96c frozen tissue grinder from Shanghai Wanbo Biotechnology Co., Ltd., under conditions of −10 °C at a frequency of 50 Hz for a duration of 6 min. Following this grinding step, low-temperature ultrasonic extraction was performed for 30 min at 5 °C and a frequency of 40 kHz. After the extraction process, the samples were allowed to rest at −20 °C for 30 min before being centrifuged at 4 °C for 15 min at 13,000 g. The resulting supernatant was then carefully transferred to an injection vial for subsequent LC-MS/MS analysis.

The LC-MS analysis was performed using a UHPLC-Q Exactive HF-X system, which was equipped with an ACQUITY HSS T3 column having dimensions of 2.1 mm in inner diameter and 100 mm in length, with a particle size of 1.8 μm, all supplied by Waters, USA. This analytical process took place at Majorbio Bio-Pharm Technology Co. Ltd. in Shanghai, China. The mobile phase was composed of solvent A, which consisted of 0.1 % formic acid in a water-acetonitrile mixture at a ratio of 95:5 (v/v), and solvent B, comprising 0.1 % formic acid in a mixture of acetonitrile, isopropanol, and water at a ratio of 47.5:47.5:5 (v/v/v). The detailed gradient elution program is provided in Table S1. The flow rate during the analysis was maintained at 0.40 mL/min, with the column temperature set at 40 °C. An injection volume of 5 μL was utilized to facilitate the analysis.

The UPLC system was connected to a UHPLC-Q Exactive HF-X Mass Spectrometer that features an electrospray ionization (ESI) source functioning in both positive and negative modes. The optimal conditions for the experiment were established as follows: the source temperature was maintained at 400 °C, while the sheath gas flow rate was set at 40 arbitrary units (arb). Additionally, the auxiliary (Aux) gas flow rate was adjusted to 10 arb. The ion-spray voltage floating (ISVF) was configured to operate at −2800 V in negative mode and at 3500 V in positive mode, allowing for effective ionization in both directions. To optimize the fragmentation process for mass spectrometry, the normalized collision energy was varied in a rolling manner, set to 20 V, 40 V, and 60 V. Data acquisition was conducted using the Data Dependent Acquisition (DDA) mode, which is advantageous for selecting ions for further analysis based on their abundance in the preceding scans. Finally, the detection phase covered a mass range from 70 to 1050 m/z, ensuring comprehensive identification of the analytes of interest.

2.5. Structure and hepatoprotective activity of LBPs in LBF

2.5.1. Preparation of LBPs

The dried powder of LBF (5.0 g) from different tree ages (Y2, Y4, Y6, Y8, Y10, and Y15) was homogenized in 125 mL distilled water and extracted using ultrasound (200 W, 60 °C, 30 min). After cooling to room temperature, the extract was centrifuged (4500 r/min, 10 min), and the supernatant was concentrated to 20 mL. Absolute ethanol (80 mL) was added dropwise (4 mL/min) under stirring (120 r/min) using a separating funnel, and the mixture was left to stand for 10 h. The resulting precipitate was dissolved in 50 mL of distilled water, centrifuged again (4500 r/min, 10 min), and the supernatant was deproteinized with Sevag reagent until no interfacial flocs were observed. The polysaccharide solution was dialyzed (3000 Da cutoff) against tap water for 48 h and distilled water for 24 h, then lyophilized (−50 °C, 8 Pa) to obtain LBPs, designated as LBPs-Y2, LBPs-Y4, LBPs-Y6, LBPs-Y8, LBPs-Y10, and LBPs-Y15 (Wang, Chen, et al., 2023).

2.5.2. Analysis of the content of LBPs and their conjugation

The total carbohydrate content of LBPs was measured by the phenol‑sulfuric acid method (Guo et al., 2022) and calculated using a d-Glucose calibration curve. Protein content of LBPs was determined via the Coomassie Brilliant Blue G-250 method with BSA as a reference (Li et al., 2024). Uronic acid content in LBPs was quantified by the carbazole sulphate method using GalA as a standard (Wang et al., 2023).

2.5.3. Analysis of monosaccharide composition of LBPs

The monosaccharide composition was determined according to a reported method (Wang et al., 2023). Specifically, A total of 500 μL of 2.0 mol/L trifluoroacetic acid was used to hydrolyze LBPs (5.0 mg) at 110 °C for 6 h, and the hydrolysates were evaporated under nitrogen at 75 °C and dissolved in 0.5 mL water to prepare the hydrolyzed LBPs solution. In a 5 mL snap-top vial, 400 μL of the hydrolyzed LBPs solution, 400 μL of 500 mmol/L 1-phenyl-3-methyl-5-pyrazolone (PMP)-methanol solution, and 400 μL of 300 mmol/L NaOH solution were mixed thoroughly and reacted in a 70 °C water bath for 100 min. After that, 500 μL of 300 mmol/L HCl solution was mixed, and then this system was washed three times with 2 mL of chloroform, discarding the chloroform layer afterwards. The aqueous phase was filtered using a 0.45 μm filter membrane and subsequently analyzed using an LC-20 A HPLC system equipped with an Inertsil ODS-3 column (4.6 mm id × 250 mm, 5 μm) at a wavelength of 245 nm and a temperature of 30 °C. The flow rate and injection were set at 1.2 mL/min and 10 μL, respectively. Monosaccharide standards followed identical procedures.

2.5.4. Evaluation of molecular weight (MW) distribution of LBPs

MW was assessed using size exclusion chromatography combined with multi-angle laser light scattering, as described previously (Grube et al., 2020). At a temperature of 25 °C, the OHpak SB-803 HQ (8.0 mm i.d. × 300 mm, 6 μm) and OHpak SB-804 HQ (8.0 mm i.d. × 300 mm, 6 μm) columns were linked in series, and a 250 μL sample (1 mg/mL) was injected at 1 mL/min.

2.5.5. Analysis of fourier transform infrared (FT-IR) and ultraviolet-visible (UV–vis) spectroscopy of LBPs

The FT-IR spectral analysis of LBPs (1.0 mg) was performed using a Bruker Tensor 27 spectrometer, with measurements taken across the wavenumber range of 4000–500 cm−1 at 4 cm−1 resolution. Additionally, the UV–vis absorption characteristics of LBPs (0.1 mg/mL) were examined by a TU600 UV–Vis spectrophotometer from 200 to 800 nm.

2.5.6. Analysis of hepatoprotective activity of LBPs

The assessment of hepatoprotective activity was performed based on established methods (Sung et al., 2014) with minor modification. HepG2 cell lines were obtained from the National Collection of Authenticated Cell Cultures (Shanghai, China) and cultured in DMEM medium supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin (P/S). A total of 5 × 103 cells were seeded in 96-well plates. The normal control group (NC) was treated with phosphate-buffered saline (PBS), while the model control group (MC) was exposed to 4 % ethanol for 6 h. Following this treatment, cells were incubated with LBPs for 24 h, and cell viability was assessed using the MTT assay to evaluate the hepatoprotective effects of LBPs on ethanol-induced HepG2 injury. After treatment, HepG2 cells cultured in 96-well plates underwent a washing procedure with minimal PBS to remove residual culture medium. Subsequently, a fluorescent probe, DCFH-DA, at a concentration of 10 μg/mL, was added to the cells, which were then incubated at 37 °C for 20 min to facilitate the reaction and staining. Upon completion of the incubation, the cells were washed twice with 1 mL of ice-cold PBS to eliminate any unincorporated dye. Finally, the stained cells were resuspended in 1 mL of PBS, preparing them for the subsequent detection of intracellular reactive oxygen species (ROS) levels using flow cytometry analysis. The levels of ALT, AST, MDA, NO, and GSH were measured according to the manufacturer's protocols.

2.6. Statistical analysis

Results are presented as mean ± standard deviation (mean ± SD) from at least three replicates. Data processing and graph analysis were performed using SPSS 22.0, Origin 2021, and GraphPad Prism. One-way analysis of variance (ANOVA) was employed for inter-group comparisons. Significant differences among experimental groups were determined at p < 0.05, with mean values labeled by different lowercase letters (e.g., a, b, c) indicating statistical significance. In the cell activity evaluations and correlation analyses, significant differences are denoted by asterisks: ⁎ (p < 0.05), ⁎⁎ (p < 0.01).

3. Results

3.1. Effect of tree age on LBF extract content

The chemical composition and quality of LBF are influenced not only by geographical origin and cultivar, but also, importantly, by tree age. Tree age is a critical determinant of fruit quality, modulating levels of total sugars, phenolic compounds, organic acids, and antioxidant capacity through complex nonlinear pathways. Some researchers believe that analyzing the relationship between changes in fruit bioactive compounds and tree age could help maximize health and medicinal benefits from these compounds (Khalid et al., 2016). The size, weight and phytochemical composition of LBF demonstrated significant tree-age dependency (Fig. 2). Morphologically, the longitudinal fruit diameter stabilized at over 13.5 mm from Y4 to Y10, with the lowest value observed at Y2 (Fig. 2A). Dry matter accumulation peaked at Y6 and Y8, as evidenced by the maximum dry weight per 100 seeds (15.25 ± 0.36 g and 15.38 ± 0.24 g, respectively) (Fig. 2B). Furthermore, the total polysaccharide content in LBF varied with tree age, as illustrated in Fig. 2C. Among the samples, Y6 and Y8 exhibited the highest polysaccharide levels, which were significantly higher than those of other age groups, although no difference was observed between Y6 and Y8. Polysaccharide contents at Y2, Y10, and Y15 did not differ significantly. Interestingly, while no difference was detected between Y2 and Y4, the polysaccharide level at Y4 was significantly higher than at Y10 and Y15. Similarly, the accumulation of total carotenoids was closely associated with tree age (Fig. 2D), with peak accumulation occurring between Y6 and Y8. From Y2 to Y6, the total carotenoid content increased rapidly with tree age, whereas between Y8 and Y15, it gradually declined with increasing tree age. Concurrently, the total polyphenol (Fig. 2E) and flavonoid content (Fig. 2F) varied markedly with tree age. Total polyphenols ranged from 9.2 mg/g to 11.8 mg/g, peaking at Y10 (11.83 ± 0.12 mg/g), which was significantly higher than in other age groups (p < 0.05), followed by Y6 and Y8 (10.18 ± 0.18 mg/g and 9.99 ± 0.28 mg/g). The lowest polyphenol levels occurred at Y4 and Y15. Similarly, total flavonoids (6.9 mg/g to 9.4 mg/g) were highest at Y10 (9.42 ± 0.45 mg/g) and lowest at Y2 and Y15 (6.89 ± 0.18 mg/g and 6.91 ± 0.31 mg/g). These results indicate that polyphenols and flavonoids accumulated markedly from Y4 to Y10, but declined substantially in Y15, consistent with the patterns observed for morphological traits and dry weight. Research indicates that the yield of LBF (including size and dry weight) and the accumulation of its primary active components (such as polysaccharides, carotenoids, polyphenols, and flavonoids) all exhibit significant age-dependency, with different components peaking at various stages of tree age maturity. Specifically, the accumulation of polysaccharides and carotenoids peaks at Y6 and Y8, whereas the accumulation of polyphenols and flavonoids significantly increases only by Y10, and generally declines by Y15. Therefore, if the production of functional foods primarily targets polysaccharides or carotenoids, Y6-Y8 represents the optimal harvesting window; if the focus is on polyphenols and flavonoids, harvesting should be postponed until Y10. These age-specific accumulation patterns raise important questions about their molecular regulation. While key genes involved in polysaccharide biosynthesis (e.g., glycosyltransferases) and carotenoid metabolism (e.g., PSY, PDS, CCDs) have been identified in Lycium barbarum (Cao et al., 2021), their regulation by tree age remains uncharacterized and warrants further investigation.

Fig. 2.

Fig. 2

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Effects of tree age on LBF properties: (A) Longitudinal fruit diameter; (B) Dry weight per 100 seeds; (C) Total polysaccharide content; (D) Total carotenoid content; (E) Total polyphenol content; (F) Total flavonoid content. Y2 to Y15: LBF from trees aged 2 to 15 years. Data are presented as mean ± SD (n = 3). Different lowercase letters within the same row indicate significant differences between groups (p < 0.05) based on one-way ANOVA followed by Tukey's post hoc test.

3.2. Effect of tree age on the levels of bioactive compounds in LBF

Tree age significantly influences the accumulation of various bioactive compounds in LBF, with distinct age-dependent patterns observed across different classes of compounds (Fig. 3). In particular, ZDP and zeaxanthin, the primary carotenoids in LBF and key contributors to its vision-improving effects, exhibited clear trends with tree age. The content of ZDP increased steadily from Y2 to Y6, peaking at 3.94 ± 0.35 mg/g—a 2.6-fold increase compared to Y2—before declining sharply, with the lowest level observed in Y15 (0.65 ± 0.05 mg/g) (Fig. 3A and Fig. S1). Zeaxanthin was less sensitive to age variation (Fig. 3B and Fig. S1), but followed a broadly similar trend, reaching its highest level at Y6 (0.75 ± 0.03 mg/g) and remaining relatively stable from Y8 to Y15. This indicates that moderate tree age promotes carotenoid biosynthesis, and that ZDP biosynthesis or degradation is more sensitive to tree age than free zeaxanthin. The decline in ZDP in older trees may be attributed to reduced carotenoid esterification efficiency or increased degradation rates. These accumulation patterns in LBF differ from those observed in mango, where total carotenoids increase with tree age (Meena & Asrey, 2018b). Given that ZDP and zeaxanthin contribute to ocular health, skin protection, and antioxidant defense (Pedro et al., 2019; Zhu et al., 2025), mid-aged LBF appears optimal for carotenoid-rich applications.

Fig. 3.

Fig. 3

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Age-dependent accumulation patterns of bioactive compounds in LBF: (A) ZDP, (B) zeaxanthin, (C) betaine, (D) N-trans-feruloyltyrosine (NFT), and (E) 2-O-β-d-glucopyranosyl-l-ascorbic acid (AA-2βG). Y2 to Y15: LBF from trees aged 2 to 15 years. Data are presented as mean ± SD (n = 3). Different lowercase letters within the same row indicate significant differences between groups (p < 0.05) based on one-way ANOVA followed by Tukey's post hoc test.

In contrast, the alkaloid betaine—a key marker component for LBF quality in the Chinese Pharmacopoeia—showed a distinct peak at Y10, where its concentration was 34.7 % higher than that at Y2. After this peak, the content declined at Y15. Intermediate-age samples (Y4, Y6, and Y8) contained 37.6 %, 29.1 %, and 34.6 % less betaine, respectively, than the Y10 peak (Fig. 3C and Fig. S2). Another alkaloid, NFT—a neuroprotective compound (Khan et al., 2021)—reached its highest level at Y15, showing a 67 % increase over Y4. The content was lower at Y8 than at Y2 and Y10, and lowest at Y4 and Y6 (Fig. 3D and Fig. S3), suggesting that alkaloid accumulation may be enhanced with advanced tree maturity.

AA-2βG is a stable ascorbic acid derivative with reported anti-aging effects in Caenorhabditis elegans (Fang et al., 2025) and the ability to modulate gut microbiota and reduce neuroinflammation (Dong et al., 2024). The age of the tree also significantly influenced the levels of AA-2βG (Fig. 3E and Fig. S4). The highest levels occurred at Y8 and Y10 (p < 0.05), followed by Y6, which exhibited significantly higher levels than Y4 and Y15 (p < 0.05). Conversely, Y2, Y4, and Y15 exhibited similarly low AA-2βG contents with no significant differences among them. The mid-age peak aligns with patterns observed for LBPs, carotenoids, and flavonoids, suggesting coordinated regulation of antioxidant-related metabolites during this developmental stage.

Collectively, these results demonstrate that the biosynthesis and accumulation of different classes of bioactive compounds respond to tree age in distinct ways, with carotenoids favoring moderate age (Y6), alkaloids favoring later maturity (Y10), and certain antioxidants (AA-2βG) showing optimal levels in mid-to-late stages (Y8–Y10). Although previous studies have identified key enzymes involved in the biosynthesis of these compounds—notably fatty acyl-CoA synthetase for ZDP esterification (Cao et al., 2021), specific UDP-glycosyltransferases for AA-2βG glycosylation (Huang et al., 2025), and betaine aldehyde dehydrogenase (BADH) for betaine synthesis (Tian et al., 2024), the specific regulatory mechanisms through which tree age governs the temporal patterns of these biosynthetic pathways remain a critical knowledge gap requiring further investigation.

3.3. Effect of tree age on the levels of metabolites in LBF

To further investigate the influence of tree age on the accumulation of bioactive constituents in LBF, a comprehensive metabolomic analysis was conducted for samples from different-aged trees. Compared with Y2, substantial metabolic reprogramming was observed at all age stages, with the number of differential metabolites (upregulated vs. downregulated) being 549 (153 vs. 396) at Y4, 590 (152 vs. 438) at Y6, 568 (208 vs. 360) at Y8, 548 (152 vs. 396) at Y10, and 605 (240 vs. 365) at Y15 (Fig. 4A). In all comparisons, downregulated metabolites markedly outnumbered upregulated ones, suggesting that many biosynthetic activities were attenuated relative to Y2. Cluster analysis revealed that 155 metabolites were shared among all age comparisons, indicating a core set of age-responsive compounds. Unique metabolites were most abundant at Y15 vs Y2 (204 metabolites), reflecting the most pronounced metabolic divergence in older trees, while Y6 vs Y2 had the fewest unique metabolites (35), suggesting greater similarity in metabolic composition to young trees (Fig. 4B).

Fig. 4.

Fig. 4

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Metabolomic analysis of age-dependent variation in LBF: (A) Differential accumulation heatmap, (B) Venn diagram of shared/unique metabolites, (C) KEGG pathway enrichment.

KEGG pathway enrichment analysis showed that these differential metabolites were mainly associated with secondary metabolic pathways, particularly phenolic metabolism (flavone and flavonol biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis), as well as the biosynthesis of various alkaloids and zeatin. Several amino acid metabolism pathways (e.g., phenylalanine, tyrosine, and tryptophan metabolism; glycine, serine, and threonine metabolism; and arginine and proline metabolism) were also significantly enriched, alongside glycerophospholipid and glutathione metabolism (Fig. 4C). These results indicate that tree age not only alters the abundance of individual bioactive molecules but also reshapes the broader metabolic network, with a strong shift toward secondary metabolite biosynthesis during mid to late growth stages.

To investigate the differences in LBF across various age groups based on metabolomics, we conducted dimensionality reduction on the metabolomics data. In Principal Component Analysis (PCA), the distances between sample points reflected the similarities and differences in metabolite composition among LBF of different tree ages. As shown in Fig. 5A, Y4, Y8, and Y10 were closely clustered, with Y6 also located nearby, indicating that the secondary metabolite compositions of Y4, Y8, and Y10 were highly similar. Although some differences were observed between Y6 and these three groups, the overall distinction was not significant. All four sample groups were located to the left of the quality control (QC) samples. In contrast, Y2 and Y15 were positioned far from the other samples, distributed to the right of the QC samples, suggesting that their metabolite compositions are substantially different from those of the other groups. Furthermore, Y2 and Y15 were located on the positive and negative sides of the second principal component, respectively, indicating that their metabolic profiles were also distinctly different. This phenomenon might arise from shifts in metabolic strategies associated with tree age: metabolites in the fruits of younger trees (e.g., Y2) were primarily directed toward supporting basic plant growth and development, whereas fruits of older trees (e.g., Y15) might accumulate more secondary metabolites related to stress resistance, owing to long-term adaptation to environmental stresses. In summary, based on similarities in metabolite composition, the samples could be divided into three categories: one group comprising Y4, Y6, Y8, and Y10; Y2 as a separate group; and Y15 as another distinct group. The supervised partial least squares-discriminant analysis (PLS-DA) score plot further supported this classification pattern (Fig. 5B).

Fig. 5.

Fig. 5

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Metabolic profiling of LBF. (A) PCA score plot, (B) PLS-DA score plot, and (C) KEGG phytochemical compound classification.

Through KEGG annotation and phytochemical classification of metabolites from LBF, nine major classes of compounds were identified (Fig. 5C). These comprised 30 flavonoids (including 7 isoflavones), 31 phenolic compounds (including 4 lignans, 12 coumarins, and 15 monomeric lignans), 39 terpenoids, and 29 alkaloids. The detailed information on phytochemical compounds of LBF is shown in Table S2.

Among these metabolites identified, polyphenolic and alkaloid metabolites compounds were particularly noteworthy due to their established roles in enhancing the nutritional quality, antioxidant activity, and pharmacological potential of wolfberry. Additionally, many of the detected compounds—such as ferulic acid, caffeic acid, 4-caffeoylquinic acid, rutin, quercetin, and dihydroferulic acid—have been widely reported in previous phytochemical studies of wolfberry fruits (Sun, Jia, et al., 2025; Zhou et al., 2017). Given their bioactive relevance and literature support, these phenolic acids and flavonoids were selected for further targeted pattern analysis. Hierarchical clustering and heatmap analysis (Fig. S5A) revealed distinct age-related accumulation patterns. The metabolites were grouped into two subclusters with opposing trends: Subcluster 1 (Fig. S5B) comprised mainly phenolic acids and flavonoids, which increased steadily from Y2 to Y8 (peaking at Y8) before declining sharply at Y10 and Y15 to levels even lower than those at Y2. Subcluster 2 (Fig. S5C) contained other phenolic acids, which decreased rapidly from Y2 to Y6, reached their lowest at Y6, and then increased and stabilized between Y8 and Y15. Many of the compounds in Subcluster 1 are widely recognized for their antioxidant properties (Chen et al., 2025; Jeelani et al., 2025). Interestingly, Subcluster 2 included some phenolic acids and flavonoids that decreased during early tree age but increased again in older trees. This biphasic pattern may reflect age-related shifts in phenylpropanoid metabolism, possibly linked to changes in enzymatic activity or resource allocation between growth and defense (Fig. S5D). Compared with other fruit species, such as mango—where phenolic content is highest in younger fruits (Meena & Asrey, 2018a)—LBF demonstrated a delayed peak, suggesting species-specific regulation of secondary metabolism.

In a similar manner, alkaloid metabolites were categorized into two subclusters exhibiting contrasting trends. Subcluster 1 (Fig. S6B), enriched in nitrogen-containing compounds such as kukoamines and polyamines (e.g., kukoamine A/B, spermidine, spermine, and nicotinamide riboside) (Fig. S6A), peaked at Y6 before experiencing a decline, although levels at Y8 and Y10 remained elevated compared to Y4. These polyamine derivatives are recognized for their antioxidant, anti-inflammatory, and anti-diabetic properties (Lin et al., 2025; Mann et al., 2025). Conversely, Subcluster 2 (Fig. S6C), characterized by nucleotide derivatives and tyramine conjugates (such as choline, adenine, hypoxanthine, and N-feruloyltyramine) (Fig. S6A), displayed the highest abundance at Y2 and the lowest at Y10, indicating that certain nitrogen-containing metabolites are more abundant in younger trees. Collectively, these results indicate that alkaloid metabolites in LBF exhibit dynamic, age-dependent patterns. Middle-aged trees (approximately 6–10 years) generally displayed the most favorable bioactive profiles, which may inform the optimization of harvest timing for functional and nutritional purposes.

Beyond polyphenols and alkaloids, the analysis of organic acids in the metabolomics of LBF across different tree ages revealed significant variations in their content with changes in tree age (Fig. S7). The organic acids were categorized into two distinct subclusters: the content of subcluster 1 increased until Y4 and then gradually declined (Fig. S7B), while subcluster 2 decreased markedly from Y2 to Y6 before rising again thereafter (Fig. S7C). Specifically, from Y2 to Y6, the content of succinic acid changed significantly (Fig. S7A), reflecting notable enrichments in energy metabolism-related pathways such as oxidative phosphorylation, pyruvate metabolism, and propanoate metabolism (Fig. S7D). During Y8 to Y10, pronounced changes in D-(+)-malic acid and citric acid were observed, indicating a metabolic shift toward supplying precursors for biosynthesis, including glyoxylate and dicarboxylate metabolism. Overall, the metabolic profile of organic acids exhibited a dynamic age-dependent trend: energy metabolism predominated in younger trees, carbon skeleton provision gained emphasis in middle-aged trees, and the regulation of coenzymes and hormone signaling became prominent in older trees.

Likewise, the metabolomic analysis of amino acids and saccharides across the Y2, Y4, Y6, Y8, Y10, and Y15 groups revealed substantial changes in abundance and pathway enrichment, with metabolites clearly separating into two subclusters (Fig. S8 and Fig. S9), suggesting two distinct accumulation or regulatory modes. The corresponding KEGG enrichment network is presented in Fig. S8D and Fig. S9D.

3.4. Effect of tree age on the structure and antioxidant activity of LBPs in LBF

To explore the influence of tree age on bio-macromolecules, we analyzed age-dependent changes in the structural characteristics and bioactivities of LBPs in LBF (Table 1, Fig. 6 and Fig. S10). Table 1 shows that the carbohydrate content of LBPs was lowest in LBPs-Y2 (46.23 ± 1.96 %), peaked (>52 %) from LBPs-Y4–LBPs-Y10, and declined to 42.05 ± 1.51 % in LBPs-Y15. Protein levels were constant across ages, whereas uronic acids varied markedly, with LBPs-Y2 and LBPs-Y15 containing approximately 6.5 % and LBPs-Y4–LBPs-Y10 exceeding 7.60 %, though LBPs-Y8 (6.75 ± 0.43 %) showed a transient decrease. All LBPs contained eight monosaccharides—Man, Rha, GlcA, GalA, Glc, Gal, Xyl, Ara—with Glc (22.59–45.07 %), Ara (23.71–36.56 %), and Gal (18.95–31.22 %) predominating. Glc content was enriched in LBPs-Y4–LBPs-Y10, whereas Ara and Gal were highest in LBPs-Y2 and lowest in LBPs-Y10. Molecular weight profiling identified three fractions (MW1: 2.099–3.644 × 106 Da; MW2: 1.439–1.802 × 105 Da; MW3: 4.537–8.942 × 104 Da). LBPs-Y6 and LBPs-Y8 were enriched in MW1 and MW2 with the lowest MW3 proportion, while LBPs-Y4, LBPs-Y10, and LBPs-Y15 showed similar MW patterns. FT-IR spectra exhibited consistent functional groups across all ages, including O—H (3403 cm−1), C—H (2925 cm−1), C Created by potrace 1.16, written by Peter Selinger 2001-2019 O (1643, 1420 cm−1), C—H bending (1078 cm−1), and β-glycosidic linkages (895 cm−1); the intensity of several peaks varied with tree age, which may be attributed to the disruption of intermolecular hydrogen bonds in the polysaccharides during extraction (Wu et al., 2024). UV spectra showed a faint 280 nm absorption, confirming minor protein presence. Collectively, LBPs-Y4–LBPs-Y10 exhibited optimal LBPs carbohydrate and uronic acid content, favorable monosaccharide composition, and high-MW enrichment, supporting their use as superior raw material for functional ingredient production.

Table 1.

Chemical composition and monosaccharide constituents of LBPs extracted from LBF of different tree ages.

Sample LBPs-Y2 LBPs-Y4 LBPs-Y6 LBPs-Y8 LBPs-Y10 LBPs-Y15
Carbohydrate (%) 46.23 ± 1.96b 53.29 ± 1.04a 53.01 ± 2.13a 52.82 ± 1.87a 52.09 ± 0.98a 42.05 ± 1.51c
Protein (%) 5.04 ± 0.32a 4.71 ± 0.49a 4.69 ± 0.61a 5.00 ± 0.35a 4.50 ± 0.55a 4.51 ± 0.43a
Uronic acid (%) 6.49 ± 0.36b 7.74 ± 0.41a 7.60 ± 0.38a 6.75 ± 0.43b 7.67 ± 0.29a 6.46 ± 0.22b
Monosaccharide constituents (molar ratios) (mol%)
Man 2.92 2.60 2.29 2.17 3.01 1.84
Rha 3.06 3.38 3.50 3.06 3.10 3.22
Glc 22.59 37.75 37.59 39.94 45.07 34.83
Gal 31.22 20.54 21.76 21.72 18.95 26.61
Xyl 1.87 1.85 2.27 2.02 1.42 1.54
Ara 36.56 30.84 29.48 28.85 23.71 29.50
GlcA 1.05 1.30 1.33 1.19 1.39 1.49
GalA 0.73 1.74 1.79 1.06 3.34 0.97
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Note: Different lowercase letters within the same row indicate significant differences between groups (p < 0.05) based on one-way ANOVA followed by Tukey's post hoc test.

Fig. 6.

Fig. 6

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Characterization of LBPs extracted from LBF of different tree ages: (A) Monosaccharide composition chromatogram of LBPs, (B) Molecular weight distribution of LBPs, (C) FT-IR spectral features of LBPs, and (D) UV absorption characteristics of LBPs. Y2–Y15: LBF from trees aged 2–15 years. LBPs-Y2–LBPs-Y15: Polysaccharides extracted from LBF of trees aged between 2 and 15 years.

Recently, plant polysaccharides have gained attention as promising adjunct hepatoprotective agents. For example, polysaccharides from old stalks of Asparagus officinalis L. can alleviate nonalcoholic fatty liver disease (NAFLD) by modulating the gut microbiota and regulating the AMPK/SREBP and LPS/TLR4/NF-κB pathways, highlighting their hepatoprotective potential (Sun, Luo, et al., 2025). Previous studies have demonstrated that LBPs possess hepatoprotective activity (Wang et al., 2020). This study utilized HepG2 cells for in vitro experiments to investigate the recovery effects following liver injury and the administration of LBPs. As shown in Fig. S11, when the concentration of LBPs was within the range of 50–400 μg/mL, there was no significant impact on cell proliferation and cytotoxicity. Therefore, the effect of LBPs on cell viability after ethanol-induced damage was further studied (Fig. S12). After treatment with 4 % ethanol, the cell survival rate was 62.8 %. When protected with 10 μg/mL silybin, the cell survival rate was restored to 92.8 %, which indicated the successful establishment of the model. Compared to the MC, the viability of cells treated with different concentrations of LBPs after injury showed a concentration-dependent increase (Fig. S12). Specifically, when the concentration of LBPs was 200 μg/mL, the survival rates increased by 14.01 %, 22.27 %, 37.79 %, 42.05 %, 27.86 %, and 16.49 %, respectively, and compared to the MC, LBPs-Y4 to LBPs-Y10 exhibited significant restorative effects (p < 0.05) (Fig. S12). Considering the relatively limited protective effects observed at the concentration of 50 μg/mL, this study selected three concentrations of 100, 200, and 400 μg/mL for subsequent experiments to further evaluate the hepatoprotective activity of LBPs. As shown in Fig. 7, compared with the NC, the MC exhibited significantly increased levels of AST, ALT, ROS, MDA, and NO, while the GSH level was markedly reduced, indicating severe liver injury in the MC. The positive control group (PC), which was a reference for drug intervention, showed significant improvement in all indicators compared to the MC, demonstrating the effectiveness of the intervention. Compared to the MC, the extent of reduction in AST, ALT, ROS, MDA, and NO levels, as well as the increase in GSH level, initially rose and then declined with increasing tree age, with LBPs from Y6–Y8 exhibiting the optimal hepatoprotective activity. Under the same LBPs conditions, the high-dose group outperformed the medium-dose group, and the medium-dose group outperformed the low-dose group, demonstrating a clear dose-response relationship. The most representative was the high-dose group with LBPs-Y6, which showed improvement rates of 23.1 %, 39.8 %, 39.3 %, 49.2 %, 39.8 %, and 32.3 % for ROS, MDA, NO, GSH, AST, and ALT, respectively, significantly outperforming the same-dose groups with LBPs-Y2 and LBPs-Y15.

Fig. 7.

Fig. 7

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Evaluation of the hepatoprotective activity of LBPs extracted from LBF of different tree ages: (A) AST levels, (B) ALT levels, (C) ROS levels, (D) MDA content, (E) NO levels, and (F) GSH levels. LBPs-Y2 to LBPs-Y15: Polysaccharides extracted from LBF of trees aged between 2 and 15 years. Three doses were tested: low (L, 100 μg/mL), medium (M, 200 μg/mL), and high (H, 400 μg/mL). Data are presented as mean ± SD. Statistical significance is indicated as #p < 0.05, ##p < 0.01 vs. the normal control (NC); and ⁎p < 0.05, ⁎⁎p < 0.01 vs. the model control (MC), as determined by one-way ANOVA followed by Tukey's post hoc test.

4. Conclusion

This study systematically demonstrates that tree age significantly influences the accumulation of bioactive compounds in LBF. The results reveal that total polysaccharides and carotenoids peak at Y6 and Y8, whereas total polyphenols and flavonoids reach their highest levels at Y10. Key bioactive compounds, including ZDP, AA-2βG, and betaine, also exhibit distinct age-dependent accumulation patterns, with peaks at Y6, Y8, and Y10, respectively. Metabolomic analysis further indicates highly similar metabolic profiles among trees aged Y4 to Y10. Importantly, polysaccharides extracted from Y6 to Y8 exhibit the strongest bioactivity, significantly improving oxidative stress markers and liver injury indicators. These findings confirm that LBF from trees aged Y6 to Y10 possess superior nutraceutical properties, offering clear guidance for selecting optimal harvest timing in Lycium barbarum cultivation.

Despite these promising results, this study has several limitations that should be acknowledged. All fruit samples were collected from a single cultivation base within an authentic production region, which effectively minimized geographical and cultivation variability but limited the generalizability of our findings. Future studies should include samples from multiple major Lycium barbarum production areas to comprehensively evaluate the combined effects of tree age, geographical environment, and cultivation practices on metabolite accumulation. Furthermore, although this study demonstrated that tree age markedly affects the contents of major bioactive compounds, it did not elucidate the underlying biochemical or molecular mechanisms. Future research integrating transcriptomic, proteomic, and metabolic pathway analyses is warranted to reveal how tree age regulates metabolic processes that determine the accumulation of bioactive compounds.

CRediT authorship contribution statement

Ningli Wang: Writing – review & editing, Writing – original draft, Software, Resources, Methodology, Investigation, Formal analysis, Data curation. Hao Meng: Writing – review & editing, Writing – original draft, Software, Data curation. Tingting Zhao: Writing – review & editing, Methodology, Formal analysis, Data curation. Dong Pei: Writing – review & editing, Funding acquisition. Duolong Di: Writing – review & editing, Validation. Yingli Yang: Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. Jianfei Liu: Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization.

Ethical approval

Ethics approval was not required for this research.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by Key Research and Development Program of Ningxia (2024BBF02008), Gansu Science Fund for Basic Creative Research Groups (25JRRA469), State-owned capital budget supports scientific research projects of provincial enterprises (2024GZ021), Science and Technology Program of Gansu Province (25YFFA033), and Lanzhou youth science and technology talent project (2023-QN-89).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2025.103462.

Contributor Information

Yingli Yang, Email: xbsfxbsdyang@163.com.

Jianfei Liu, Email: jfliu@licp.cas.cn.

Appendix A. Supplementary data

Supplementary material

mmc1.docx (3.4MB, docx)

Data availability

Data will be made available on request.

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Associated Data

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

Supplementary Materials

Supplementary material

mmc1.docx (3.4MB, docx)

Data Availability Statement

Data will be made available on request.

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