Comparative LC-MS/MS Metabolomics of Wild and Cultivated Strawberries Reveals Enhanced Triterpenoid Accumulation and Superior Free Radical Scavenging Activity in Fragaria nilgerrensis

Abstract

Strawberry fruit quality is linked to its phytochemical composition, yet the diversity of non-volatile terpenoids remains largely unexplored. Therefore, a comparative LC-MS/MS-based metabolomic analysis of terpenoid profiles was conducted using three commercial cultivars (Fragaria × ananassa) and a wild accession of Fragaria nilgerrensis (HM). Results from this study showed that the HM cultivar had a total terpenoid abundance 5–6 times higher than the commercial cultivars. The HM cultivar was uniquely enriched in specific triterpenoids, such as 3β,6β,19α,24-Tetrahydroxyurs-12-en-28-oic acid and 13,27-Cyclo-2,3-Dihydroxy-11,19(29)-Ursadien-28-Oic Acid, which was over 450 times higher than the ‘Danxue’ and ‘Fenyu’ commercial strawberry cultivars. Conversely, sesquiterpenoids like Alismol and Pterocarpol were 100 times lower in HM than in the commercial cultivars. This enhanced triterpenoid accumulation strongly correlated with a superior capacity to scavenge free radicals in vitro, with HM showing maximum capacity, as measured by the DPPH assay. These findings highlight the value of wild strawberry germplasm as a reservoir of biochemical diversity for breeding strawberries with enhanced functional quality.

1. Introduction

The cultivated strawberry (Fragaria × ananassa Duch.) stands as one of the most widely consumed fruits of the Rosaceae family, and economically important globally [1]. Its high economic value and commercial success is driven by a compelling combination of attractive sensory properties, including a vibrant red color, juicy texture, and a balanced, sweet-acid flavor profile [2]. Beyond its fresh market appeal, the strawberry is a fundamental raw material for a diverse range of processed foods and a key ingredient in jams, jellies, juices, yogurts, dairy products, ice creams, and confectionery products [3,4]. The strawberry holds significant nutritive importance, being a rich source of essential vitamins (notably vitamin C and folate), minerals, dietary fiber, and a diverse array of bioactive phytochemicals [5]. These compounds contribute not only to the fruit’s quality attributes but also to its recognized health-promoting properties, including antioxidants, anti-inflammatory, and potential cardioprotective effects [6,7]. The economic value derived from this fruit is therefore substantial, sustained by continuous consumer demand and extensive cultivation across varied climates.

The strawberry fruit’s biochemistry is a synergy of primary and secondary metabolism. Primary metabolites, such as soluble sugars (e.g., fructose, glucose) and organic acids (e.g., citric acid), are directly responsible for the characteristic aroma, taste, and flavor, determining consumer acceptability [8,9,10]. In contrast, secondary metabolites, while not involved in primary metabolism, play crucial roles in plant defense and conferring health benefits to consumers. Compounds such as anthocyanins like pelargonidin-3-glucoside, which impart the characteristic red pigmentation, flavonols, and ascorbic acid are the most studied antioxidants in strawberries [4,11,12,13]. They not only contribute to the nutritional value of the fruit but also influence product quality and shelf-life by mitigating oxidative degradation processes that lead to color fading, off-flavor development, and nutrient loss.

Among the diverse classes of plant secondary metabolites, terpenoids represent the largest and most structurally diverse family [14]. Synthesized from isoprene (C5) units, this vast group includes monoterpenoids (C10), sesquiterpenoids (C15), diterpenoids (C20), and triterpenoids (C30) [4,15]. In plants, they serve essential ecological functions as attractants for pollinators, defense against herbivores and pathogens, and signaling molecules [16,17]. From a human health perspective, terpenoids exhibit several biological activities, and demonstrated antioxidant, anti-inflammatory, antimicrobial, antiviral, and anticancer properties [18,19,20,21,22]. For instance, certain monoterpenoids and sesquiterpenoids are key contributors to the aromatic bouquet of fruits and herbs, while pentacyclic triterpenoids, such as those of the ursane and oleanane types, have garnered significant scientific interest for their pharmacological potential [23,24,25].

Despite their established importance, research on terpenoids in strawberries has been mostly focused on the volatile monoterpenes and sesquiterpenes that influence fruit aroma [26]. In contrast, strawberry non-volatile terpenoids, particularly the high-molecular weight triterpenoids and their glycosylated derivatives (triterpenoid saponins), remain under-investigated, creating a critical scientific research gap. The genetic regulation and biosynthetic pathways leading to this complex class of compounds in Fragaria species are poorly elucidated. Moreover, comprehensive comparative analyses of terpenoid diversity across different strawberry cultivars, especially between cultivated cultivars and their wild relatives, are less studied. Wild species, such as Fragaria nilgerrensis, are often reservoirs of unique genetic and biochemical traits that may have been diluted or lost during modern breeding processes focused on agronomic performance, fruit size, aroma, taste, and shelf-life [4,27]. A systematic exploration of the phytochemical diversity of these strawberries could reveal pharmacological active terpenoids, providing a foundation for breeding cultivars with enhanced nutritional value.

While previous metabolomic studies in strawberry have extensively characterized other bioactive classes such as anthocyanins, flavonols, phenolic acids, and ascorbic acid, the non-volatile terpenoid such as triterpenoids and their glycosylated derivatives remain a significant and under-explored dimension of the fruit’s phytochemical landscape. This study therefore focuses on terpenoids to address this gap and to evaluate their potential contribution to the functional quality of strawberry germplasm. We conducted a comparative analysis of terpenoid compounds in three commercial strawberry cultivars (Fragaria × ananassa ‘Fenyu’, ‘Red Face 99’, and ‘Danxue’) and a wild accession of Fragaria nilgerrensis (HM). The objectives of this study were as follows: (1) to qualitatively profile the terpenoid metabolome across these four cultivars using LC-MS/MS; (2) to employ multivariate statistical analyses to define terpenoid profiling and identify key distinct metabolites; (3) to correlate the distinct metabolomic signatures with in vitro DPPH radical scavenging. Results from our study would ultimately help future breeding programs aimed at enhancing the phytochemical and functional quality of this important fruit.

2. Materials and Methods

2.1. Growth Conditions and Sample Preparation

This study included four distinct strawberry cultivars: three commercial octoploid cultivars (Fragaria × ananassa), ‘Fenyu’ (FY), ‘Red Face 99’ (RF), and ‘Danxue’ (DX), sourced from Dongji Luyuan Agriculture and Animal Husbandry Co., Ltd. (Dandong, China), and a wild accession of Fragaria nilgerrensis (HM). The HM plants were originally collected from wild fields in Yongshan County, Yunnan Province, China, in October 2023, and then vegetatively propagated and grown under the same controlled conditions as the commercial cultivars. All cultivars were vegetatively propagated via stolon division and subsequently cultivated under controlled environmental conditions within greenhouse facilities at Zhaotong University.

Three independent container replicates were maintained for each cultivar, with each container (19 × 15 × 72 cm) housing five plants. This container-based cultivation system was employed under controlled greenhouse conditions rather than field conditions typical of commercial operations. This replication strategy was considered sufficient to capture core biological variation, as fruits from all five plants within a container were pooled to form a composite biological sample, effectively integrating plant-to-plant variability. The growth substrate consisted of Pindstrup peat moss (10–30 mm granule size; Denmark, purchased from a local agricultural supplies store in Zhaotong City.) amended with 20% (v/v) vermiculite and decomposed sheep manure compost to enhance physical structure and nutrient content. To ensure consistent fruit set, manual cross-pollination was conducted using fine brushes. All cultivars were cultivated simultaneously under these uniform conditions to minimize environmental variation. Mature fruits were harvested in March 2024 upon reaching physiological maturity across all cultivars, ensuring that developmental stage was consistent and would not confound the metabolomic results.

For downstream analyses, representative fruits were selected from the most vigorous container replicate for each cultivar to minimize developmental variability. Berries from all five plants within the selected container were pooled to form a composite biological sample. Approximately 20 fruits were pooled per biological replicate to obtain a representative sample. The fruit samples were meticulously rinsed with deionized water to remove surface contaminants, gently air-dried on sterilized filter paper, and dissected into uniform fragments (approximately 0.5 cm³) using sterile instruments. The tissue fragments were immediately flash-frozen in liquid nitrogen, mechanically ground to a fine powder, and homogenized. The resulting homogenate was aliquoted into three technical replicates, with each technical replicate consisting of 0.1 g of homogenized powder for the DPPH assay and 30 mg for the LC-MS/MS analysis, stored in sterile cryogenic vials, and preserved at −80 °C until subsequent transcriptomic, biochemical, and metabolomic profiling.

2.2. Chemicals and Reagents

All chemicals and solvents used in this study were of analytical or high-performance liquid chromatography (HPLC) grade. Trolox was purchased from Sigma-Aldrich (St. Louis, MO, USA). The stable radical, 2,2-diphenyl-1-picrylhydrazyl (DPPH), was obtained from Aladdin (Shanghai, China). For chromatographic analyses, HPLC-grade acetonitrile, methanol, and ethanol were purchased from Fisher Scientific (Waltham, MA, USA). UPLC-MS grade formic acid was obtained from Merck (Darmstadt, Germany).

2.3. DPPH Radical Scavenging Assay

The free radical scavenging capacity of strawberry fruit extracts was evaluated using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, following the specifications of a commercial kit (G0128W, Grace Biotechnology Co., Ltd., Suzhou, China). Frozen fruit tissue (0.1 g) was homogenized with 1 mL of pre-chilled 80% (v/v) aqueous methanol, and the extraction was facilitated using an ultrasonic cleaner (Js-100A, Jingsheng, Chengdu, China) at 60 °C for 30 min, with vortexing at 5 min intervals. The resultant homogenate was centrifuged at 12,000× g for 10 min, and the supernatant was collected for analysis.

The assay was performed according to the manufacturer’s protocol. Briefly, in separate tubes, 150 µL of the supernatant was mixed with 150 µL of freshly prepared DPPH working solution (Test Sample), while control samples consisted of 150 µL supernatant with 150 µL of 80% methanol (Sample Control, to correct for sample color) and 150 µL of 80% methanol with 150 µL DPPH working solution (Blank Control, to determine initial DPPH absorbance). All mixtures were incubated in darkness at 25 °C for 30 min, then centrifuged at 12,000× g for 5 min. The absorbance of the supernatants was measured at 517 nm using a microplate reader. The DPPH radical scavenging activity (%) was calculated as

Scavenging Activity (%) = [1 − (A_Test − A_Sample_Control)/A_Blank] × 100,

where A_Test, A_Sample_Control, and A_Blank represent the absorbance of the Test, Sample Control, and Blank Control, respectively.

A standard curve was generated using Trolox (0–25 µg/mL) under identical conditions (y = 2.8486x + 0.7084), and the results were expressed as micrograms of Trolox equivalents per gram of fresh weight (µg TE/g FW).

2.4. Terpenoid Profiling by LC-MS/MS

2.4.1. Sample Preparation and Extraction

A composite sample of approximately 20–30 fresh strawberry fruits per cultivar was lyophilized using a freeze-dryer (Scientz-100F, Ningbo Scientz Biotechnology Co., Ltd., Ningbo, China) and pulverized into a fine powder using a high-frequency ball mill (MM 400, Retsch GmbH, Haan, Germany). Precisely weighed portions of the powder (30 mg) were measured using an analytical balance (MS105DU, Mettler Toledo, Greifensee, Switzerland, readability 0.01 mg) and transferred into centrifuge tubes. Metabolite extraction was performed with 1.5 mL of pre-cooled 70% aqueous methanol containing 1 ppm 2-chlorophenylalanine as an internal standard. The mixture was vortexed thoroughly and then extracted by shaking at 30 min intervals, followed by centrifugation at 12,000× g for 3 min. The resulting supernatant was carefully collected and filtered through a 0.22 μm nylon membrane filter (Jinteng Laboratory Equipment Co., Ltd., Tianjin, China) prior to LC-MS/MS analysis. The LC-MS/MS analysis was conducted by Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China).

2.4.2. Chromatographic and Mass Spectrometric Conditions

Chromatographic separation was performed on an ExionLC™ AD system (SCIEX, Framingham, MA, USA) equipped with an Agilent SB-C18 column (1.8 μm, 2.1 × 100 mm) maintained at 40 °C. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), using a gradient elution from 5% to 95% B over 9 min at a flow rate of 0.35 mL/min [28].

Mass spectrometric detection was carried out using a QTRAP^® 6500+ hybrid triple quadrupole linear ion trap mass spectrometer (SCIEX, Framingham, MA, USA). The system was equipped with an ElectroSpray Ionization (ESI) Turbo V™ Ion Source. Analysis was conducted in both positive and negative ionization modes. Instrument parameters were set as follows: ion source temperature, 500 °C; ion spray voltage, ±5500 V; curtain gas, 25 psi; and collision gas, high. Terpenoids were quantified using scheduled multiple reaction monitoring (MRM), with declustering potential and collision energies optimized for each compound [28].

2.5. Multivariate Statistical Analysis

Both metabolomic and transcriptomic datasets were standardized using Z-score normalization prior to analysis. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were performed using the OmicShare tools platform (https://www.omicshare.com/tools, accessed on 2 March 2025) to delineate group separations and minimize intra-group variance [29]. For univariate analyses, biochemical parameters, including antioxidant activities and phytochemical contents, were compared among strawberry cultivars using a one-way ANOVA followed by a Least Significant Difference (LSD) post hoc test (p < 0.05) in Statistix 8.1 software, with data representing three experimental replicates.

To identify key discriminatory metabolites, orthogonal partial least squares-discriminant analysis (OPLS-DA) was implemented via the MetaboAnalystR (v4.0) package (https://doi.org/10.1038/s41467-024-48009-6, accessed on 24 February 2025) in R [30]. Metabolites with a Variable Importance in Projection (VIP) score > 1.0 were considered significant for group discrimination [31]. Significantly altered metabolites were defined by a dual threshold: a false discovery rate (FDR)-adjusted p-value < 0.05 (Student’s t-test) [32] and an absolute log2 fold-change (|log2FC|) ≥ 1 [33]. Correlation analysis between cultivars was conducted using Pearson’s method on the OmicShare platform). Venn diagrams were constructed using the EVenn web tool (http://www.ehbio.com/test/venn/, accessed on 10 August 2025) to illustrate overlaps in metabolite sets [29].

3. Results

LC-MS/MS-based metabolomic profiling of the four strawberry cultivars revealed diverse terpenoid compounds. A total of 200 terpenoid compounds were found to be differentially accumulated (VIP score > 1.0), underscoring obvious biochemical diversity. The terpenoids were classified into sub-categories such as 85 triterpenes, 51 monoterpenoids, 42 sesquiterpenoids, 10 triterpene saponins, 9 diterpenoids, and 3 terpenes (Supplementary Table S1). The scale of this differential terpenoid profile highlights profound terpenoids specialization among the cultivars.

3.1. Hierarchical Clustering Analysis of Terpenoid Profiles Among Strawberry Cultivars

The z-score normalized heatmap of terpenoid compounds, which met the significance thresholds of a VIP score > 1.0 and a log2 fold-change > 1.0, revealed pronounced and statistically significant differential accumulation across the four strawberry cultivars (Figure 1). Hierarchical clustering analysis (HCA) delineated a clear segregation of the samples into two primary clusters, effectively distinguishing the three commercial cultivars (Fragaria × ananassa ‘RF’, ‘FY’, and ‘DX’) from the wild cultivar HM. Within the commercial cultivar cluster, further sub-clustering was observed, suggesting more subtle, yet distinct, chemotypic variations among RF, FY, and DX. The heatmap further illustrates that 174 terpenoid metabolites were substantially more abundant in the HM cultivar, forming a prominent block of high-intensity signals (red), while a concurrent set of 26 compounds was enriched in the commercial cultivars.

3.2. Principal Component Analysis Reveals Distinct Genetic Variation Among Strawberry Cultivars

The Principal Component Analysis (PCA) results, visualized in Figure 2A, demonstrate a clear separation of cultivars along the principal components. The first principal component (PC1) accounts for most of the observed variance, explaining 90.68% of the total genetic variation. This indicates that a single, dominant source of genetic divergence is the primary driver distinguishing the strawberry cultivars in this study. The second principal component (PC2) explains a substantially smaller, yet notable, portion of the variance at 3.08%. The cumulative variance explained by these first two components is 93.76%. In compound-wise PCA, the first two principal components effectively captured most of the variance, with PC1 and PC2 explaining 71.33% and 24.6% of the total variance, respectively, cumulatively accounting for 95.93% of the observed variation (Figure 2B). The plot revealed a distinct separation of compounds along the primary axes. A large, dense cluster of terpenoids was observed near the intersection of both axes, indicating a group of compounds with highly similar compositions. In contrast, several individual terpenoids were positioned as clear outliers, spatially distant from the main cluster along both PC1 and PC2 (Figure 2B). This pronounced distribution suggests significant qualitative and/or quantitative differences in their terpenoid synthesis and accumulation, highlighting unique chemotypes within the strawberry terpenoids.

3.3. Comparative Analysis of Terpenoid Distribution Across Cultivars

A Venn diagram was constructed to elucidate the shared and unique terpenoids among the three strawberry cultivars (FY, HM, and RF). The analysis revealed a substantial core set of 191 terpenoids common to all three cultivars, indicating a conserved biochemical foundation for terpenoid synthesis (Figure 3). The FY vs. HM group contained 5 whereas the RF vs. HM group had 3 unique terpenoids. The pairwise comparison showed that 196 terpenoids were significantly altered among FY vs. HM, and 194 between RF vs. HM, further underscoring the distinct chemical signature of the HM cultivar compared to the other two (Figure 3). A comparative analysis of terpenoid composition among different comparison groups of the cultivated cultivars FY, RF, and DX, using a Venn diagram. The analysis identified a core set of 13 terpenoids common to all three cultivars comparisons (Figure 3). Pairwise comparisons revealed substantial overlap, with 16 compounds shared between two groups FY vs. DX and RF vs. DX (Figure 3), whereas 3 and 4 terpenoids were unique to these comparison groups, respectively. Notably, the RF vs. FY comparison group showed 6 unique compounds (Figure 3). This distribution underscores a more distinct terpenoid profile for the DX cultivar.

3.4. Differential Terpenoid Abundance and Total Content Across Cultivars

The correlation analysis of biological replicates within each cultivar (HM, DX, FY, RF) showed exceptionally high positive correlations (r = 0.98 to 1.00), indicating strong reproducibility and a distinct, conserved terpenoid signature for each cultivar (Figure 4A). In contrast, correlations between different cultivars were consistently negative or close to zero (r ≈ −0.05), demonstrating that the terpenoid composition is highly specific to each cultivar and fundamentally different between them. The analysis of differentially altered terpenoids revealed a striking contrast between comparisons involving the HM cultivar and all others (Figure 4B). In the HM vs. FY and HM vs. RF comparisons, the vast majority of terpenoids were significantly up-regulated (172 and 171 compounds, respectively), with very few down-regulated or unchanged. Conversely, comparisons between FY, RF, and DX showed a more balanced distribution, with a moderate number of up- and down-regulated terpenoids (26–35 up, 23–26 down) and a large proportion of compounds with non-significant changes or that were not detected in one cultivar (Figure 4B). The HM strawberry samples exhibited a markedly higher total terpenoid abundance (394,158,565.9), which was approximately 5 to 6 times greater than that of the other three cultivars (Figure 4C). The cultivars FY, RF, and DX showed comparatively similar and lower total abundances.

3.5. Comparative Metabolomic Profiling Reveals Significant Differential Accumulation Across Strawberry Cultivars

A comprehensive comparative metabolomic analysis, visualized through a clustered heatmap of log2 fold-change (Log2FC) values and associated statistical significance (Figure 5), revealed substantial and statistically significant differential accumulation of terpenoids. A total of 200 terpenoids were identified and subjected to pairwise comparisons between cultivars (HM_vs._DX, HM_vs._FY, HM_vs._RF, DX_vs._FY, DX_vs._RF, FY_vs._RF). In multiple pairwise comparisons (HM_vs._DX, HM_vs._FY, HM_vs._RF), a significant number of metabolites exhibited pronounced up-regulation in HM. Conversely, specific comparisons between commercial cultivars, such as DX_vs._FY and FY_vs._RF, showed more moderate and variable fold-change patterns. The presence of the significance (p-value) level (indicated by the dark yellow and pale yellow) across numerous metabolite-cultivar pairings confirms that the differential accumulation patterns in multiple pairwise comparisons (Figure 5). Notably, several terpenoid and glycosidic compounds, such as various forms of Elaeocarpucin and Oleanane-type triterpenoids, were among the most differentially accumulated, pointing to specific biochemical pathways that are differentially regulated across the cultivars.

3.6. Key Metabolites Differentially Accumulated in the HM Cultivar vs. Commercial Cultivars

A targeted statistical analysis, including VIP scores, fold-change, and p-value calculations, was performed to identify the specific metabolites driving the differentiation between the HM cultivar and the three commercial cultivars (DX, FY, RF). In the comparison of HM vs. DX, HM vs. FY, and HM vs. RF, the most dramatically up-regulated compounds (Fold Change > 300, Log2FC > 8.5) were consistently complex triterpenoid acids and their hydroxylated derivatives (

Table 1. Key differentially (VIP score > 1.1) accumulated metabolites in HM versus commercial strawberry cultivars. Each pairwise comparison includes the compound name, chemical class, VIP score, p-value, Fold Change, and Log2-transformed Fold Change (Log2FC).

Serial No.	Compounds	Class	HM vs. DX
			VIP	p-Value	Fold Change	Log2FC	Type
1	3β,6β,19α,24-Tetrahydroxyurs-12-en-28-oic acid	Triterpene	1.12	0.001	3973.84	11.96	up
2	Rhodiolol A	Monoterpenoids	1.12	0.007	1832.43	10.84	up
3	13,27-Cyclo-2,3-Dihydroxy-11,19(29)-Ursadien-28-Oic Acid	Triterpene	1.12	0.005	1308.87	10.35	up
4	Jasminoside E	Monoterpenoids	1.12	0.000	716.00	9.48	up
5	Elaeocarpucin C	Triterpene	1.12	0.007	609.10	9.25	up
6	2,7-Dimethylocta-2,4-Dienal-8-ol 8-O-(beta-D-Xylopyranosyl)-beta-D-glucopyranoside	Monoterpenoids	1.12	0.003	580.95	9.18	up
7	(1R,4S,5R,8S)-8-(hydroxymethyl)-4-isopropylbicyclo[3.2.1]oct-6-ene-6-carbaldehyde	Sesquiterpenoids	1.12	0.000	436.05	8.77	up
8	28-Hydroxy-1,20(29)-Lupadien-3-one	Triterpene	1.12	0.013	434.59	8.76	up
9	Trachelosperonide B-1	Triterpene Saponin	1.12	0.001	391.67	8.61	up
10	Humulene epoxide II	Sesquiterpenoids	1.10	0.093	0.03	−4.99	down
11	Icariside F *	Sesquiterpenoids	1.12	0.016	0.03	−5.06	down
12	Deacetyl asperulosidic acid methyl ester	Monoterpenoids	1.12	0.016	0.03	−5.30	down
13	3,7,11-trimethyldodeca-3,7-diene-1,10,11-triol 10-O-(beta-D-Xylopyranosyl)-beta-D-glucopyranoside	Sesquiterpenoids	1.11	0.036	0.02	−5.33	down
14	2-[2-(5-hydroxy-4a,8-dimethyl-2,3,4,5,6,8a-hexahydro-1H-naphthalen-2-yl)prop-2-enoxy]-6-(hydroxymethyl)oxane-3,4,5-triol *	Sesquiterpenoids	1.12	0.025	0.01	−6.23	down
15	6″-O-Caffeoylharpagide	Terpene	1.12	0.019	0.01	−6.37	down
16	Pterocarpol	Sesquiterpenoids	1.12	0.008	0.01	−7.48	down
17	Alismol	Sesquiterpenoids	1.12	0.001	0.00	−7.98	down
	Compounds	Class	HM vs. FY
			VIP	p-value	Fold Change	Log2FC	Type
18	1β-Hydroxy-2-oxopomolic acid	Triterpene	1.11	0.008	411.89	8.69	up
19	28-Hydroxy-1,20(29)-Lupadien-3-one	Triterpene	1.11	0.013	434.59	8.76	up
20	3β,6β,19α,24-Tetrahydroxyurs-12-en-28-oic acid	Triterpene	1.11	0.001	463.75	8.86	up
21	2,3,19,23-Tetrahydroxyolean-12-en-28-oic acid	Triterpene	1.11	0.000	466.18	8.86	up
22	1,2,3,19-Tetrahydroxyurs-12-en-28-oic acid	Triterpene	1.11	0.000	531.91	9.06	up
23	3,11-Dihydroxy-23-Oxo-20(29)-Lupen-28-Oic Acid	Triterpene	1.11	0.002	570.82	9.16	up
24	Guavenoic Acid	Triterpene	1.11	0.001	708.29	9.47	up
25	Viburolide	Triterpene	1.11	0.003	782.64	9.61	up
26	13,27-Cyclo-2,3-Dihydroxy-11,19(29)-Ursadien-28-Oic Acid	Triterpene	1.11	0.005	1308.87	10.35	up
27	Alismol	Sesquiterpenoids	1.11	0.001	0.00	−8.67	down
28	Pterocarpol	Sesquiterpenoids	1.11	0.004	0.00	−8.16	down
29	3,7,11-trimethyldodeca-3,7-diene-1,10,11-triol 10-O-(beta-D-Xylopyranosyl)-beta-D-glucopyranoside	Sesquiterpenoids	1.11	0.023	0.01	−6.09	down
30	Humulene epoxide II	Sesquiterpenoids	1.11	0.001	0.02	−5.36	down
31	pterodontoside E *	Sesquiterpenoids	1.09	0.004	0.03	−5.17	down
32	6″-O-Caffeoylharpagide	Terpene	1.11	0.009	0.03	−5.16	down
	Compounds	Class	HM vs. RF
			VIP	p-value	Fold Change	Log2FC	Type
33	3,23-Dihydroxy-30-noroleana-12,20(29)-dien-28-oic acid (30-Norhederagenin)	Triterpene	1.12	0.001	373.13	8.54	up
34	Trachelosperonide B-1	Triterpene Saponin	1.12	0.001	391.67	8.61	up
35	1β-Hydroxy-2-oxopomolic acid	Triterpene	1.12	0.008	411.89	8.69	up
36	3,13,15-Trihydroxyoleanane-12-one	Triterpene	1.12	0.001	530.13	9.05	up
37	Elaeocarpucin C	Triterpene	1.12	0.007	609.10	9.25	up
38	Jasminoside E	Monoterpenoids	1.12	0.000	716.00	9.48	up
39	16,23:16,30-Diepoxydammar-24-ene-3,20-diol (Jujubogenin) *	Triterpene	1.12	0.003	1678.95	10.71	up
40	Rhodiolol A	Monoterpenoids	1.12	0.007	1832.43	10.84	up
41	Isothankunic acid	Triterpene	1.12	0.000	3191.85	11.64	up
42	Alismol	Sesquiterpenoids	1.12	0.000	0.01	−7.45	down
43	Pterocarpol	Sesquiterpenoids	1.11	0.003	0.01	−6.93	down
44	Humulene epoxide II	Sesquiterpenoids	1.11	0.002	0.01	−6.28	down
45	pterodontoside E *	Sesquiterpenoids	1.10	0.007	0.02	−5.97	down
46	Icariside C1 *	Sesquiterpenoids	1.11	0.006	0.02	−5.78	down
47	6-Eudesmene-1,4-diol 1-O-β-D-Glucopyranoside *	Sesquiterpenoids	1.11	0.035	0.03	−5.18	down

* Isomers.

). Notable examples include 3β,6β,19α,24-Tetrahydroxyurs-12-en-28-oicacid, 13,27-Cyclo-2,3-Dihydroxy-11,19(29)-Ursadien-28-Oic acid, rhodiolol A, and isothankunic acid, all of which exhibited significant fold-change increases and highly significant p-values (p < 0.01).

Conversely, the HM cultivar showed a significant down-regulation of several sesquiterpenoids across all comparisons (Table 1). Compounds such as alismol, pterocarpol, and humulene epoxide II were consistently present at markedly lower levels in HM (Fold Change < 0.03, Log2FC < −4.99). All identified compounds possessed high VIP scores (VIP > 1.0), confirming their importance as key biomarkers of bioactive triterpenoids to discriminate the wild-HM metabolome from the commercial cultivars.

3.7. Relative Composition of Major Terpenoid Subclasses

Relative percentage analysis of major terpenoids confirmed a fundamental metabolic divergence between the HM cultivar and the commercial cultivars (DX, FY, RF). As shown in Figure 6, the HM cultivar was markedly enriched in the percentage of pentacyclic triterpenoids, consistently exhibiting the highest relative abundance of compounds such as 2,3,19,23-tetrahydroxyurs-12-en-28-oic acid, cordianol B, and 2,19-dihydroxy-3-oxours-12-en-28-oic acid (Figure 6). This pattern indicates a specialized and highly active triterpenoid biosynthetic pathway in HM. Conversely, the commercial cultivars, particularly DX and RF, accumulated significantly higher levels of specific sesquiterpenoids. Notably, santalol A, awabukinol, and alismol were present at lower concentrations in HM. This inverse relationship (HM’s dominance in triterpenoids vs. the cultivars’ dominance in sesquiterpenoids), defines a major shift in metabolic flux within the terpenoid backbone biosynthesis pathway between the cultivars.

3.8. HM Cultivar Demonstrates Superior Antioxidant Activity

As shown in Figure 7, the HM cultivar exhibited significantly superior in DPPH radical scavenging activity compared to the commercial cultivars. HM achieved maximum radical scavenging (97%) and the highest DPPH radical scavenging (320.40 µg Trolox/g FW) (Figure 7). Among the commercial cultivars, FY and DX showed intermediate values, while RF consistently displayed the lowest radical scavenging activity. These results functionally link the distinct, triterpenoid-rich metabolome of the HM cultivar with a significantly enhanced antioxidant profile.

4. Discussion

This study provides a terpenoid diversity across strawberry cultivars, revealing a profound biochemical divergence between a wild accession and modern commercial cultivars. The most obvious finding is the high abundance of triterpenoids in the fruits of F. nilgerrensis (HM) cultivar (Figure 1). The noticeably high total terpenoid abundance in HM, approximately 5 to 6 times greater than in the commercial cultivars (Figure 4C), suggests a fundamental difference in its isoprenoid biosynthetic network. This shift is characterized by the significant up-regulation of specific pentacyclic triterpenoid acids and their hydroxylated derivatives, such as 3β,6β,19α,24-tetrahydroxyurs-12-en-28-oic acid and isothankunic acid (Table 1), these pentacyclic triterpenoids are well-known for their potent therapeutic potential [19,21]. From a plant physiology perspective, this heightened triterpenoid production likely establishes a constitutive defense mechanism in the wild cultivar [34,35], affording protection against biotic and abiotic stresses in its native habitat trait [36,37] that may have been inadvertently selected against during the cultivated strawberries (F. × ananassa), which prioritized traits such as aroma, fruit size, yield, taste, sweetness, and visual appeal.

The inverse relationship observed between triterpenoid enrichment and sesquiterpenoid depletion in HM (Table 1) points to a potential metabolic trade-off within the terpenoid backbone biosynthesis pathway. This pattern is consistent with a hypothesized redirection of metabolic flux in the cytosolic Mevalonic Acid (MVA) pathway [38]. The data suggests that in the HM cultivar, flux may be preferentially directed toward the triterpenoid branch, a phenomenon documented in other plant species where the competition for the common precursor farnesyl pyrophosphate (FPP) can lead to such inverse relationships [34,38]. This could be facilitated by the relative up-regulation of key enzymes such as squalene synthase, which condenses FPP to form squalene (C30), the universal triterpenoid precursor [14], potentially at the expense of sesquiterpene synthase activity. This proposed mechanism provides a logical explanation for the observed metabolite profiles and highlights a key target for future transcriptomic and enzymatic validation.

For the food industry, the triterpenoid-rich profile of the HM cultivar is of considerable functional importance, as it was directly correlated with a superior DPPH radical scavenging capacity (Figure 7). While the use of a single assay provides a specific rather than a comprehensive antioxidant profile, the DPPH method is a well-validated measure of hydrogen-donating activity and serves as a strong indicator of free radical scavenging potential, which is a key mechanism of antioxidant action. Besides this, the obvious up-regulation of ursane, oleanane, and lupane-type triterpenoid acids in HM (more-than 400-fold) (Table 1). This aligns with research highlighting that plants can be rich, species-specific accumulation of these bioactive molecules [39]. The potent bioactivities reported for these triterpenoids (ursane, oleanane, and lupane-type), including anti-inflammatory, anticancer, and antiviral properties [18,19,20,22], suggest that the HM cultivar offers far more than just enhanced DPPH radical scavenging capacity. Our findings position it as a promising genetic resource for the development of “functional” or “nutraceutical” strawberries. From a breeding perspective, harnessing this wild germplasm could allow for the introduction of novel compounds, potentially leading to cultivars with enhanced health-promoting properties that align with the growing market for clean-label, food-as-medicine products.

The clear terpenoids separation revealed by HCA and PCA, and the distinct clusters of shared and unique metabolites in the Venn diagrams underscore that the genetic background is a primary determinant of terpenoid composition (Figure 1, Figure 2 and Figure 3). This metabolic divergence between the wild and the cultivated strawberries suggests a significant shift in the terpenoid biosynthetic network during modern breeding that mostly prioritized yield and esthetic traits over specialized phytochemistry, a phenomenon observed in other plant species [21]. This validates the targeted metabolomics approach for the authentication of botanical origin and for quality control in the industrial processing of strawberry-derived ingredients. Purees, concentrates, or functional extracts claiming specific health benefits could be fingerprinted against such terpenoid profiles to ensure authenticity and influence [40]. Furthermore, few but unique terpenoids identified in the commercial cultivars, may be critical for their specific aroma and flavor profiles, which are essential for consumer acceptance [4].

Future work should integrate volatile terpenoid data with sensory analysis to provide a deeper understanding of how these non-volatile precursors influence final product quality. It is important to acknowledge the limitations of this study. While we identified significant differences in terpenoid accumulation, the underlying transcriptional regulation of the associated biosynthetic genes remains to be elucidated. Furthermore, the in vitro antioxidant activity demonstrated here requires validation through more complex food models and in vivo studies to confirm its bioactivity in a food matrix and its bioavailability. Despite these limitations, our findings clearly demonstrate that wild F. nilgerrensis strawberry germplasm represents a largely unexploited reservoir of bioactive triterpenoids, specifically the oleanane-, ursane-, and lupane-type pentacyclic triterpenoids renowned for their wide-ranging pharmacological activities [19]. Harnessing this diversity through conventional or molecular breeding holds significant promise for developing the strawberry cultivars with enhanced functional properties for the health-conscious consumer and the innovative food industry.

5. Conclusions

This study shows that the wild strawberry cultivar Fragaria nilgerrensis (HM) possesses a unique and highly divergent terpenoid profile, characterized by an obvious enrichment of bioactive triterpenoids and total terpenoid content than that of commercial cultivars. The significant up-regulation of specific pentacyclic triterpenoid acids (more than 400-fold), as well as their strong correlation with superior antioxidant activity, reveals a previously unexplored biochemical dimension of strawberry fruit quality. These findings underscore a critical metabolic specialization that distinguishes wild germplasm from domesticated cultivars. For the food industry, this work positions F. nilgerrensis as a high-value genetic resource for future breeding programs aimed at biofortification. The incorporation of these triterpenoid-rich traits into commercial lines offers a direct, natural strategy to enhance the nutraceutical value and intrinsic oxidative stability of strawberry-based products, aligning with consumer demand and superior health-promoting properties.