Exploring Postharvest Metabolic Shifts and NOX2 Inhibitory Potential in Strawberry Fruits and Leaves via Untargeted LC-MS/MS and Chemometric Analysis

Abstract

Background/Objectives: Strawberries are highly appreciated for their rich phytochemical composition, but rapid postharvest deterioration limits their shelf life and nutritional quality. This study aimed to investigate the metabolic changes occurring in both strawberry fruits and leaves during storage and to evaluate the NADPH oxidase 2 (NOX2) inhibitory potential of strawberry-derived metabolites. Methods: Untargeted LC-MS/MS analysis was conducted on fruit and leaf tissues stored at 8 ± 0.5 °C. A total of 37 metabolites were identified, including organic acids, phenolic acids, flavonoids, and hydroxycinnamic acid derivatives. Multivariate statistical analyses (ANOVA, PLS-DA, and volcano plots) were used to assess temporal and tissue-specific metabolic shifts. Additionally, a machine learning-based predictive model was applied to evaluate the NOX2 inhibitory potential of 24 structurally characterized metabolites. Results: Storage induced significant and tissue-specific metabolic changes. In fruits, malic acid, caffeic acid, and quercetin-3-glucuronide showed notable variations, while ellagic acid aglycone and galloylquinic acid emerged as prominent markers in leaves. The predictive model identified 21 out of 24 metabolites as likely NOX2 inhibitors, suggesting potential antioxidant and anti-inflammatory bioactivity. Conclusions: These findings provide new insights into postharvest biochemical dynamics in both strawberry fruits and leaves. The results highlight the value of leaves as a source of bioactive compounds and support their potential valorization in functional food and nutraceutical applications.

Keywords: LC-MS/MS; NOX2 enzyme; chemometric analysis; flavonoids; organic acids; phenolic compounds; plant secondary metabolites; postharvest metabolism; strawberry.

Figure 1

Flowchart of machine learning procedure.

Figure 2

Partial Least Squares Discriminant Analysis (PLS-DA) scatter plots showing metabolic differentiation in strawberry fruits during storage: (a) a comparison across all storage days and (b) a focused comparison between day 1 and day 11. While Component 1 and Component 2 individually explain a relatively small percentage of the total variance—an expected outcome in complex datasets—together they capture a meaningful separation between time points. The model focusing on early- and late-stage changes (b) was validated through permutation testing, confirming its reliability in identifying storage-related metabolic shifts.

Figure 3

VIP scores of metabolites contributing to storage-related metabolic discrimination (day 1 vs. day 11).

Figure 4

Volcano plot showing key upregulated and downregulated metabolites in strawberries. The numbers in square brackets in the legend indicate the number of significantly upregulated, and downregulated metabolites, respectively.

Figure 5

PLS-DA scatter plots illustrating the metabolic changes in strawberry leaves during storage: (a) across all sampling days; (b) a comparison between day 1 and day 8. The low variance explained by Component 1 and Component 2 reflects the high dimensionality and subtle variability inherent in metabolomics data. Despite this, the model summarizing the metabolic alterations between early and late storage stages (b) exhibited good classification accuracy and passed permutation testing, supporting its robustness in distinguishing between early and late storage stages.

Figure 6

VIP scores of metabolites contributing to storage-related metabolic discrimination (day 1 vs. day 8).

Figure 7

Volcano plot showing key upregulated and downregulated metabolites. The numbers in square brackets in the legend indicate the number of significantly upregulated, and downregulated metabolites, respectively.

Figure 8

XGBoost classifier 5-epoch results. (a) 5-epoch ROC-curves; (b) importance of descriptors in classification.