Comprehensive Nutrient Profiling and Untargeted Metabolomic Assessment of Siraitia grosvenorii from Different Regions and Varying Degrees of Processing

Abstract

The primary objective of this study was to compare the nutrition and metabolite profiles of Siraitia grosvenorii from different regions (namely Yongfu and Longsheng) and processing stages. Our findings showed that fresh Siraitia grosvenorii from Longsheng contained higher levels of total sugars, protein, and crude fat compared with those from Yongfu, though both regions had similar dietary fiber and ash content. Dried Yongfu Siraitia grosvenorii showed increased nutrient levels. A mineral analysis revealed that fresh Siraitia grosvenorii from Yongfu had the highest levels of calcium, magnesium, and potassium along with distinct differences in other mineral concentrations compared with Longsheng. Notably, fresh Yongfu fruits had higher mineral content than dried ones, except for aluminum and selenium. Through an untargeted metabolomics analysis, we identified 470 metabolites, showing significant variation between fresh samples from Yongfu and Longsheng and between fresh and dried Yongfu samples. Key metabolites included carboxylic acids, fatty acyls, and organooxygen compounds. Additionally, we observed significant enrichment in metabolic pathways such as phenylpropanoid biosynthesis, galactose metabolism, and linoleic acid metabolism, with notable differences in metabolite regulation depending on the region and processing stage. These findings highlight the influence of regional environmental factors and drying processes on the nutrient and metabolite composition of Siraitia grosvenorii.

1. Introduction

Siraitia grosvenorii (S. grosvenorii), commonly known as monk fruit, is a member of the Cucurbitaceae family and has garnered considerable attention for its potential health benefits due to its unique metabolite profile, which makes it an important plant in both food science and medicinal research. S. grosvenorii is primarily grown in southern China and mainly distributed in Guangxi and Hunan province [1]. It has traditionally been valued for its various nutrients and essential minerals [2]. Due to its rich nutritional content and dual nature as both food and medicine, it has the potential to be used as an ingredient in functional foods for purposes such as moistening the lungs and relieving cough, regulating blood sugar, and clearing heat and reducing inflammation. Moreover, it also has effects such as hemostasis and expectoration, gastric mucosa protection, blood sugar reduction, and antitumor and anti-oxidant effects [3]. Foreign research on S. grosvenorii began in 1975 when Lee ChiHong from the United States extracted a 50% ethanol solution from S. grosvenorii and separated a non-sugar substance with a sweetness equivalent to 150 times that of sucrose using column chromatography and thin-layer chromatography, suggesting it to be a triterpene glycoside. In the food and pharmaceutical industries, dried fruits of S. grosvenorii are commonly used. This is conducted to facilitate storage and transportation, ensure the stability of the content of active ingredients, and make processing operations more convenient. However, the nutritional, mineral, and metabolite composition of S. grosvenorii can vary significantly depending on factors such as the growing region and processing methods, which in turn impact its value and efficacy as a dietary component. Regional variations along with different processing stages influence the concentration of essential nutrients such as sugars, protein, and crude fat [4]. Minerals like calcium, magnesium, and potassium, crucial for physiological health [5], also differ by region, potentially due to environmental factors like soil composition and climate. Furthermore, processing methods, specifically drying, also play a significant role in altering the fruit’s nutritional and mineral profiles, affecting its bioavailability and potential health impacts [6]. In addition to its nutrient and mineral content, S. grosvenorii hosts a diverse array of metabolites, including carboxylic acids, fatty acyls, and organooxygen compounds [7].

Metabolomics, as an emerging scientific discipline, is dedicated to the qualitative and quantitative analysis of all low-molecular weight metabolites present within a biological organism or cell during a specific physiological stage [8]. In accordance with the distinct research objectives, metabolomics can be categorized into untargeted metabolomics and targeted metabolomics. Untargeted metabolomics undertakes the unbiased detection of all detectable metabolite molecules within a sample and subsequently conducts differential and pathway analyses via bioinformatics approaches to discern the disparities in metabolites between samples [9]. It represents a data-driven, expeditious, and high-throughput analytical technique for comprehensively examining all potential metabolites within a given sample set, without the prerequisite of prior knowledge regarding the specific metabolites [10].

Through metabolomic analysis, distinct patterns in metabolite concentrations have been identified, revealing the complex interaction between growth environment and post-harvest processing [11]. Despite the increasingly extensive applications of S. grosvenorii in the food and pharmaceutical fields, there remains a significant gap in the systematic research on the overall changes in nutrition and metabolomics in relation to its diverse origins and various processing procedures.

This study is designed to fill this knowledge void, and we investigated the comprehensive nutrients and conducted untargeted metabolomics analyses of both fresh and dried S. grosvenorii samples from Yongfu and Longsheng, contributing to a refined understanding of how regional and processing factors influence its composition. The insights gained are expected to enhance the strategic utilization of monk fruit in nutraceuticals and functional food industries by optimizing its nutritional and bioactive potential. It will help to reveal the transformation of metabolites during the processing of S. grosvenorii and provide guidance for optimizing the processing techniques.

2. Materials and Methods

2.1. Materials

Fresh and dried fruits of S. grosvenorii from Yongfu and Longsheng from Guangxi, China, were used. The reagents used in this study included nitric acid (≥70%) and 2-chloro-L-phenylalanine (internal standard substance, 98%), which were purchased from Aladdin (Shanghai, China). Anthrone was procured from Macklin Biochemical Co., Ltd. (Shanghai, China) Sulfuric acid (≥98%) was purchased from Xilong Science Co. (Guangzhou, China). A mixture of 28 metal standards (barium, bismuth, vanadium, calcium, cadmium, chromium, mercury, cobalt, potassium, lithium, aluminum, magnesium, manganese, molybdenum) and a mixture of 6 metal internal standards (germanium, indium, rhenium, rhodium, scandium, yttrium), 100 μg/mL, were provided by the National Nonferrous Metals and Electronic Materials Analysis and Testing Center. Methanol (≥99.0%) and acetonitrile (≥99.9%) were purchased from Thermo (Waltham, MA, USA), and ammonium formate (≥99.9%) was obtained from Sigma (St. Louis, MI, USA). Formic acid (LC-MS grade) was purchased from TCl (Tokyo, Japan).

2.2. Methods

2.2.1. Protein Content

In the present experiment, the protein content was quantified via the Thomas Brilliant Blue methodology [12]. Bovine serum albumin was selected to formulate a spectrum of concentration gradients. The samples were solubilized in an appropriate buffer solution. Subsequently, the Caulmers Brilliant Blue dye solution was introduced to both the standard and sample aliquots. Following thorough mixing, the reaction was permitted to progress, ensuring sufficient protein–dye binding for colorimetric manifestation. Ultimately, the absorbance of each sample was measured at 595 nm, and the protein concentration was calculated employing the standard curve.

2.2.2. Total Sugar Content

The determination of total sugar content was carried out by referring to the anthrone colorimetric method for the measurement of water-soluble total sugar content in cotton as stipulated in GB/T 9695.31-2008 [13]. Meanwhile, the specific procedures were in line with the methodology proposed by Shang [14]. To create a standard curve, varying concentrations of glucose standard solution were prepared in several test tubes. Anthrone reagent was then added to each test tube, followed by a heating process. A blank tube served as the reference. The absorbance was measured at 620 nm using a spectrophotometer, and the data were used to plot the standard curve. For the sugar content analysis, 1.0 mL of a 10 mg/mL sample solution was prepared in a test tube and processed using the same method as for the standard curve. The sugar content was then determined by comparing the sample’s absorbance with the standard curve.

2.2.3. Fat Content

Fat content was determined using the Soxhlet extraction method [15]. The sample was crushed, dried, and accurately weighed. The Soxhlet extractor was set up, and anhydrous ethyl ether, petroleum ether, or other organic solvents were added to the flask. The processed sample was wrapped in filter paper and placed in the extraction tube. The mixture was heated and refluxed until no oil traces remained in the siphon reflux liquid. The flask was then removed, and the solvent was evaporated. After drying and weighing, the fat content in the sample was calculated based on the difference in sample weight before and after extraction.

2.2.4. Ash Content

The ash content was determined using the slow ashing method [16]. An appropriate amount of the sample was weighed and placed in a crucible. The sample was then slowly heated on an electric furnace until it became carbonized and smokeless. Afterward, the crucible was transferred to a high-temperature furnace and heated at 550–600 °C until a constant weight was achieved. The crucible was then cooled and weighed. The ash content was calculated based on the mass difference between the crucible and the sample before and after burning.

2.2.5. Mineral Analysis

Calibration curve preparation: A 5 mL aliquot of a mixture comprising 28 metals was transferred into a 100 mL volumetric flask. The flask was then filled with 1% nitric acid and shaken vigorously to obtain an intermediate solution with a concentration of 5 mg/L. Subsequently, this intermediate solution was serially diluted with 1% nitric acid to generate 10 calibration curve points. For the preparation of the internal standard solution, a solution containing 6 metal internal standards was diluted from 100 μg/mL to 50 μg/L using 1% nitric acid.

Sample Extraction [17]: A 0.5 g sample was precisely weighed and placed into a microwave digestion vessel. Subsequently, 5 mL of nitric acid was added, and the vessel was tightly sealed. Digestion was carried out in accordance with the standard operating protocol of the microwave digestion instrument. The digestion reference conditions are presented in

Table 1. Sample digestion reference conditions.

Steps	Temperature	Digestion Time
1	120 °C	3 min
2	160 °C	3 min
3	190 °C	20 min

. Upon cooling, the vessel was carefully removed, and the lid was slowly opened to vent any residual gas. The vessel was then placed on an acid evaporator, and the acid was evaporated until the sample was nearly dry. The inner lid was rinsed with a small amount of water, followed by the addition of 1% nitric acid to adjust the volume to 10 mL. The solution was thoroughly mixed and reserved for further analysis. A blank test was conducted simultaneously.

Testing: The instrument employs CCT (collision/reaction cell) analysis in the He/O₂ gas mode. The principal working parameters and acquisition conditions of the apparatus are detailed as follows [18,19]: The tuning mode was configured to STD/KED with a residence time of 0.1 s. The sample introduction speed was 40 rpm for a duration of 40 s. The RF (radio frequency) power was set at 1550 W, and the sampling depth was 5.0 mm. The nebulizer gas flow rate was 0.98 L/min, the cooling gas flow rate was 14.0 L/min, and the auxiliary gas flow rate was 0.8 L/min. The nebulizer chamber temperature was maintained at 2.7 °C. The horizontal and vertical alignments were 0.16 mm and −0.53 mm, respectively. The He flow rate was 4.55 mL/min, and the O₂ flow rate was 0.3125 mL/min. The voltage of lens D₁ was −350 V, and that of lens D₂ was −154 V. This entire procedure was replicated three times. The content of each mineral was computed utilizing the subsequent formula:

Content = (C − C₀) × V × D/Sampling quantity

Content unit: μg/g; C, concentration of ions in the test sample, unit: μg/L; V, fixed volume, unit: mL; C₀, concentration of ions in the blank sample, unit: μg/L; Sampling quantity unit: mg; D: dilution factor

2.2.6. Untargeted Metabolomics Analysis

Metabolite extraction: An appropriate amount of sample was accurately weighed and placed into a 2 mL centrifuge tube. Subsequently, 600 µL of methanol containing 2-chloro-L-phenylalanine (4 ppm) was added, and the mixture was vortexed for 30 s. Then, steel beads were added, and the sample was ground at 55 Hz for 60 s in a tissue grinder, followed by ultrasonication at room temperature for 15 min. After that, it was centrifuged at 12,000 rpm at 4 °C for 10 min. The supernatant was collected and filtered through a 0.22 μm membrane, and the filtrate was transferred into a detection vial for LC-MS analysis. The data were acquired in the MS/MS mode using the data-dependent acquisition (DDA) method. For the research and analysis of mass spectrometry/mass spectrometry (MS/MS) spectra, accurate mass, and isotope patterns, in silico fragmentation was employed. The mass spectrometry analysis was performed by comparing with standard spectral libraries such as the NIST mass spectral library.

Chromatographic conditions: An ultra-high liquid chromatography system (manufactured by Thermo Fisher Scientific, USA) was utilized. The ACQUITY UPLC^® HSS T3 column (2.1 × 100 mm, 1.8 µm, produced by Waters, Milford, MA, USA) was employed, operating at a flow rate of 0.3 mL/min, a column temperature of 40 °C, and an injection volume of 2 μL. In the positive ion mode, the mobile phase comprised 0.1% formic acid in acetonitrile (B₂) and 0.1% formic acid in water (A₂). A gradient elution program was applied: 8% B₂ from 0 to 1 min, 8–98% B₂ from 1 to 8 min, 98% B₂ from 8 to 10 min, 98–8% B₂ from 10 to 10.1 min, and 8% B₂ from 10.1 to 12 min. In the negative ion mode, the mobile phase consisted of acetonitrile (B₃) and 5 mM ammonium formate in water (A₃). The gradient elution program was as follows: 8% B₃ from 0 to 1 min, 8–98% B₃ from 1 to 8 min, 98% B₃ from 8 to 10 min, 98–8% B₃ from 10 to 10.1 min, and 8% B₃ from 10.1 to 12 min [20].

Mass spectrometry conditions: The Thermo Q Exactive mass spectrometer (manufactured by Thermo Fisher Scientific, USA) equipped with an electrospray ionization (ESI) source was used to collect data in both positive and negative ion modes. The spray voltages were set at 3.50 kV and −2.50 kV, respectively. The sheath gas flow rate was 40 arb, the auxiliary gas flow rate was 10 arb, and the capillary temperature was 325 °C. The first-level full scan resolution was 70,000 over m/z 100–1000. The tandem was carried out using HCD at 30 eV with a resolution of 17,500. Unnecessary MS/MS information was dynamically excluded, while the top 10 ions were selected for fragmentation [21]. The isolation window is 2.0 m/z, and the collision energy is 30 eV. These parameters are fixed.

2.3. Statistical Analysis

In the analysis of metabolomic data, it is necessary to use multivariate statistical methods to maximize the retention of the original information on the basis of data degradation and regression analysis and then perform the screening of differential metabolites and the subsequent analysis; in this analysis, we used the R language Ropls package to perform multivariate statistical analysis and screened the differential metabolites under the condition of p < 0.05. Non-targeted metabolic charts were drawn through the WeiShengXin platform “https://www.bioinformatics.com.cn/ (accessed on 15 August 2024)”. During the identification process, adduct forms, cosine similarity, and spectral entropy similarity will be taken into consideration. KEGG pathway enrichment analysis of the list of differential metabolites was performed using MetaboAnalyst: “www.metaboanalyst.ca (accessed on 15 September 2023)”. The enrichment method was based on the hypergeometric distribution test, and the topological analysis was performed using the pointwise centrality degree method.

For nutrient content analysis, a one-way ANOVA test was performed using SPSS 22.0 software. Different letters within the same row indicate significant differences (p < 0.05), while the same letter indicates no significant difference.

3. Results

3.1. Nutritional Component Variability Analysis

The results are presented in

Table 2. Nutrient content of S. grosvenorii in different groups (g/100 g).

Composition	Fresh Yongfu Fruit	Fresh Longsheng Fruit	Dried Yongfu Fruit
Total sugar	2.88 ± 40.37 a	3.16 ± 3.04 b	4.28 ± 3.95 c
Total dietary fiber	34.97 ± 1.25 a	34.12 ± 1.11 a	41.99 ± 0.93 b
Protein	7.04 ± 0.01 a	7.59 ± 0.038 b	10.98 ± 0.029 c
Soluble dietary fiber	3.48 ± 0.19 a	3.64 ± 0.23 a	3.26 ± 0.24 a
Ash	2.42 ± 0.01 a	2.43 ± 0.01 a	3.99 ± 0.02 b
Crude fat	0.80 ± 0.003 a	0.90 ± 0.003 b	5.19 ± 0.02 c

Note: Different letters indicate significant differences, while the same letters indicate non—sig nificant differences.

. Among the three types of S. grosvenorii, total sugar was the most abundant nutrient, followed by total dietary fiber. Nutrient composition of fresh S. grosvenorii varies between the Yongfu and Longsheng regions. Specifically, the Longsheng group exhibited significantly higher levels of total sugar, protein, and crude fat. However, no significant differences were observed in total dietary fiber, soluble dietary fiber, or ash content between the two regions. These differences in nutrient profiles may be influenced by regional environmental factors such as soil conditions, climate, and light exposure, which differ between Yongfu and Longsheng. In contrast to the fresh fruits, the dried S. grosvenorii from Yongfu exhibited higher levels of all nutrients except for soluble dietary fiber. The protein content shows a significant increase. This increase may be due to water loss during drying, which could concentrate the nutrients, or be the result of chemical reactions occurring during the drying process that enhance nutrient content.

3.2. Mineral Analysis

Previous studies have shown that S. grosvenorii is rich in minerals, especially calcium (Ca), magnesium (Mg), and potassium (K) [22]. These minerals play important roles in preventing osteoporosis and fractures, supporting cardiovascular health, and regulating heart rate and blood pressure [23]. In this study, we analyzed the mineral content of the different varieties of S. grosvenorii, with the results being presented in

Table 3. Mineral Content of S. grosvenorii in different groups (μg/g).

Mineral	Fresh Yongfu Fruit	Fresh Longsheng Fruit	Dried Yongfu Fruit
Ca	1203.5 ± 84.64 a	971.25 ± 48.62 b	627.25 ± 45.09 c
Mg	2254.25 ± 134.95 a	1746.25 ± 114.27 c	1242 ± 95.48 b
K	13,656.5 ± 760.06 a	10,675.25 ± 820.34 b	8528.00 ± 689.76 c
Zn	22.1 ± 0.34 a	22.83 ± 0.59 a	17.20 ± 0.18 b
Na	14.83 ± 0.63 a	20.46 ± 0.26 b	11.03 ± 0.13 c
Al	4.75 ± 0.177 a	9.41 ± 0.13 b	6.07 ± 0.09 c
Fe	54.83 ± 1.10 a	59.33 ± 1.40 b	34.63 ± 0.22 c
Cr	0.34 ± 0.01 a	0.22 ± 0.004 b	0.12 ± 0.002 c
Cu	13.45 ± 0.31 a	10.25 ± 0.24 b	11.40 ± 0.08 c
Se	0.0088 ± 0.0005 a	0.0073 ± 0.0005 b	0.02 ± 0.00096 c

Note: Different letters indicate significant differences, while the same letters indicate non—signif icant differences.

. We observed that mineral levels varied across the samples. The fresh Yongfu S. grosvenorii group had the highest content of Ca, Mg, and K, followed by the fresh Longsheng S. grosvenorii group. Notably, there was no significant difference in zinc (Zn) content between these groups. However, the fresh Yongfu S. grosvenorii group had lower levels of sodium (Na), aluminum (Al), and iron (Fe) compared with the fresh Longsheng variety while showing significantly higher levels of chromium (Cr), copper (Cu), and selenium (Se). These results indicate that the mineral content of S. grosvenorii varies by region. Furthermore, with the exception of aluminum (Al) and selenium (Se), all minerals were found in higher concentrations in the fresh fruits compared with the dried ones.

3.3. Untargeted Metabolomics Analysis

3.3.1. Orthogonal Partial Least Squares Discriminant Analysis OPLS-DA

The orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) is a multivariate statistical method with supervised pattern recognition. It effectively eliminates effects unrelated to the study, thus aiding in the screening of differential metabolites. The replacement test plot effectively assesses whether the OPLS-DA model is overfitting. According to Figure 1A–D, the points of Q² and R² represent the model evaluation parameters of the actual grouping. When the predicted Q² and R² points are both lower than the original Q² and R² points in the upper right (the rightmost blue Q² point may coincide with the green R² point at the uppermost right corner in the figure), it indicates that the model is reliable.

According to Figure 1E–H, the point clouds of the sample groups are distributed in distinct regions, showing clear clustering and categorical differences. Additionally, the samples from different groups are more distinctly separated with no overlap, indicating a significant change in metabolome information.

3.3.2. Differential Metabolite Screening

To identify the metabolites responsible for clustering and class differences among different S. grosvenorii groups, the three groups were screened for differential metabolites using criteria of VIP ≥ 1 and p < 0.05. This screening identified a total of 470 differential metabolites, as shown in

Table 4. Metabolite distribution of S. grosvenorii in different groups.

Metabolite Class	Percentage (%)
Carboxylic acids and derivatives	15.31
Fatty acyls	9.8
Organooxygen compounds	9.3
Benzene and substituted derivatives	7.6
Prenol lipids	4.0
Organonitrogen compounds	2.5
Phenols	2.5
Indoles and derivatives	2.1
others	45

. The primary metabolites identified were carboxylic acids and their derivatives, with fatty acyls, organooxygen compounds, and benzene derivatives following in abundance. In our study, we observed significant differences in metabolite content between various groups of S. grosvenorii (Figure 2). Specifically, when comparing fresh S. grosvenorii from Yongfu with that from Longsheng, we identified 193 differential metabolites. Of these, 86 were down-regulated and 107 were up-regulated. Further, when comparing fresh and dried S. grosvenorii from Yongfu, 234 differential metabolites were identified, with 93 being down-regulated and 141 being up-regulated. Notably, 131 of these differential metabolites were common across the comparisons between the Yongfu and Longsheng fresh samples as well as between the fresh and dried samples from Yongfu. These findings are illustrated in the heatmap.

In comparing the fresh Yongfu and Longsheng varieties of S. grosvenorii (Figure 3A), significant differences were observed in 31 carboxylic acids and their derivatives, most of which were amino acids, comprising 97% of the content. Among these, 13 metabolites, including proline, beta-leucine, phenylalanine, and N6-acetyl-L-lysine, were up-regulated, while 19 others were down-regulated. Fatty acyls, which play a critical role in energy production and maintaining cell membrane integrity [24] showed differences in 20 metabolites. Of these, 11 metabolites, such as pantothenol and methyl jasmonate, were up-regulated, while 9, including L-2-hydroxyglutaric acid and 9-OxoODE, were down-regulated. Further analysis revealed differences in 16 organooxygen compounds, predominantly carbohydrates. In this category, 11 metabolites, such as cellopentaose and trehalose, were up-regulated, whereas 5 metabolites, including arabinose and muramic acid, were down-regulated. Benzene and its derivatives, known for their roles in growth regulation and anti-inflammatory effects, also showed significant variation. A total of 16 such compounds were detected, with 12, including 2-hydroxybenzaldehyde and 4-hydroxyphenylacetaldehyde, being up-regulated, while 4, such as phenylacetic acid and p-aminobenzoic acid, were down-regulated. Additionally, among the five prenol lipids identified, two were up-regulated and three were down-regulated, highlighting the nuanced metabolic differences between the two varieties of S. grosvenorii.

In the comparison between the fresh and dried Yongfu varieties of S. grosvenorii (Figure 3B), we identified 40 carboxylic acids, and their derivatives were significantly different. Of these, 21 differential metabolites were up-regulated, including proline, beta-leucine, gamma-aminobutyric acid, and N6-acetyl-L-lysine. Conversely, 19 differential metabolites, such as limonoate, were down-regulated. In the category of fatty acyls, 21 metabolites were identified. Among them, 14, including kojibiose, were up-regulated, while 7, such as 5-acetamidovalerate and dodecanoic acid, were down-regulated. Additionally, within the organooxygen metabolites, 7 were up-regulated, including nicotinamide riboside and ribulose, whereas 11, including N-acetyl-D-glucosamine and fructose 1,6-bisphosphate, were down-regulated. In the category of fatty acyls, 21 metabolites were identified. Among them, 14, including kojibiose, were up-regulated, while 7, such as 5-acetamidovalerate and dodecanoic acid, were down-regulated. Additionally, within the organooxygen metabolites, 7 were up-regulated, including nicotinamide riboside and ribulose, whereas 11, including N-acetyl-D-glucosamine and fructose 1,6-bisphosphate, were down-regulated. For benzene and substituted derivatives, we identified 13 metabolites, of which 8, including 2-methylbenzoic acid and dibutyl phthalate, were up-regulated. In contrast, five metabolites, including 4-methylbenzaldehyde and 2-methylbenzoic acid, were down-regulated. In the category of prenol lipids, seven metabolites were up-regulated, while two were down-regulated.

Phenols contribute to fruit color and flavor while also acting as anti-oxidants. Here, we identified 12 phenols. No significant difference was observed in phenol levels between the fresh S. grosvenorii from Yongfu and Longsheng, while a significant difference was observed between the fresh and dried S. grosvenorii from Yongfu. Moreover, significant differences were observed in the levels of organonitrogen compounds, phenols, indoles, and their derivatives when comparing the Yongfu and Longsheng varieties of S. grosvenorii, as well as when comparing the fresh and dried forms of the Yongfu variety. These results emphasize the complex metabolic changes that occur between the fresh and dried states of S. grosvenorii and between the Yongfu and Longsheng varieties, indicating significant alterations in various biochemical pathways.

3.3.3. Enrichment Pathway Analysis

Through enrichment analysis, the top twenty significantly enriched metabolic pathways were identified. These findings are detailed in Figure 4. In the comparison between the fresh Yongfu and Longsheng varieties of S. grosvenorii, the three most significantly enriched pathways were the biosynthesis of phenylpropanoids, galactose metabolism, and linoleic acid metabolism. Within the phenylpropanoid biosynthesis pathway, seven metabolites were up-regulated, while eight metabolites were down-regulated. In the galactose metabolism pathway, seven metabolites showed up-regulation, and two were down-regulated. For the linoleic acid metabolism pathway, four metabolites were up-regulated, and three were down-regulated. Table S1 provides a detailed KEGG enrichment pathway analysis of the differential metabolites.

4. Discussion

This study investigates the nutritional value and metabolic profiles of S. grosvenorii from different regions and processing stages, employing nutrient composition analysis and untargeted metabolomics. We observed significant differences in nutrient content across various groups, except for the total and soluble dietary fiber in fresh S. grosvenorii from Yongfu and Longsheng. This indicates that regional variations and processing levels influence the fruit’s nutritional composition. Notably, the three varieties studied contained higher levels of Mg, K, Ca, and Fe, suggesting their potential role in supporting metabolism, muscle function, and heart health [25]. Fresh S. grosvenorii from Yongfu contained significantly higher levels of most minerals compared with its dried counterpart, indicating that processing may lead to mineral loss in the fruit. Metabolomics analysis revealed variations in metabolite types and quantities across the different groups. The most abundant metabolites across all groups were carboxylic acids and derivatives, fatty acyls, organooxygen compounds, and benzene derivatives. These compounds may account for the unique flavor, nutritional profile, and various biological activities of S. grosvenorii from different regions and processing stages.

The fresh Yongfu S. grosvenorii group exhibited higher levels of proline, β-leucine, 2-hydroxybenzaldehyde, and 4-hydroxyphenylacetaldehyde compared with both the fresh Longsheng and dried Yongfu S. grosvenorii groups. These elevated levels suggest that the distinct aroma of Yongfu S. grosvenorii may be more pronounced than that of the other varieties [26]. Additionally, the fresh Yongfu S. grosvenorii contained higher concentrations of pantothenate, methyl jasmonate, and kojibiose, suggesting that it may possess superior anti-oxidant properties and overall quality [27]. Furthermore, the increased levels of cellopentaose and trehalose in the fresh Yongfu S. grosvenorii group compared with the fresh Longsheng group suggest that Yongfu S. grosvenorii may be better equipped to maintain cellular structure and function, thereby preserving its quality under adverse conditions [28].

The fresh Longsheng variety of S. grosvenorii contains higher levels of L-2-hydroxyglutaric acid compared with the Yongfu variety. This suggests that Longsheng S. grosvenorii may ripen earlier than fruits from Yongfu [29]. Additionally, the concentrations of phenylalanine, arabinose, muramic acid, and phenylacetic acid are higher in fresh Longsheng S. grosvenorii. This indicates that Longsheng S. grosvenorii may have more effective regulation of growth and metabolic processes than the Yongfu variety [30]. These metabolite accumulations are likely influenced by the plant’s growth environment, natural adaptations, and genetic variations. The observed differences in the metabolic pathways between S. grosvenorii from Yongfu and Longsheng indicate distinct variations in amino acid biosynthesis. These variations likely stem from genetic factors or environmental influences. This finding supports the notion that cultivation in different regions leads to significant differences in metabolite profiles.

The dried Yongfu S. grosvenorii group exhibited higher levels of citric acid, dodecanoic acid, and phenolic compounds compared with the fresh fruit. This indicates that the drying process enhances the flavor profile of S. grosvenorii from Yongfu, potentially making the dried fruits more flavorful and distinctive [31]. These findings could explain why the aroma of processed S. grosvenorii is more pronounced when brewed into tea, as opposed to the milder scent of the raw fruit [32].

This study used nutrient composition analysis and untargeted metabolomics to explore the nutritional value and metabolic profiles of S. grosvenorii from different regions and processing stages. Significant differences in nutrient content among various groups were found, showing the impacts of regional variations and processing levels. The studied varieties are rich in certain minerals beneficial for health. Metabolomics analysis revealed variations in metabolite types and quantities. Different groups had distinct metabolite characteristics, like the fresh Yongfu variety having higher levels of specific compounds related to aroma, anti-oxidant properties, and cellular function. The fresh Longsheng variety showed higher levels of certain acids and other compounds, suggesting earlier ripening and better regulation of growth and metabolism. The dried Yongfu variety had higher levels of some compounds, indicating that the drying process enhances its flavor profile, which may explain the aroma difference between processed and fresh fruits. The differences in metabolic pathways are likely due to genetic or environmental factors, supporting that cultivation in different regions leads to variations in metabolite profiles.

To gain a more comprehensive understanding of the nutrient composition and metabolic profile of S. grosvenorii, Future research could utilize techniques such as genomics and transcriptomics to further elucidate the genetic framework, gene regulatory networks, and key functional genes of S. grosvenorii. This would lay the foundation for breeding improved varieties and enhancing the overall quality, thus enabling a more comprehensive and in-depth exploration of its nutritional value and commercial potential. It would also promote the precise development and efficient application of S. grosvenorii in the fields of nutritional health products and functional foods.