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. 2026 Feb 13;12:101350. doi: 10.1016/j.crfs.2026.101350

Lipidomics combined with amino acids and their derivatives: Targeted metabolomics analysis for the quality deterioration of coconut water

Tao Wang 1, Mengyang Zhang 1, Wenxue Chen 1, Lipin Chen 1, Meizhen Xie 1,⁎, Yong-Huan Yun 1,⁎⁎
PMCID: PMC12927102  PMID: 41737889

Abstract

A systematic analysis of temporal changes in coconut water quality is essential for coconut industry development. This study elucidated deterioration-related metabolic pathways by integrating targeted lipidomics with amino acids and their derivatives. The content of malondialdehyde gradually increases with prolonged storage time, indicating that lipid oxidation occurs during the storage of coconut water. The results showed that the lipid levels increasing especially phosphatidylserine and free amino acids decreasing over time. A total of 75 differential lipids and 41 differential amino acids and their derivatives were identified during the storage of coconut water, with glycerophospholipid metabolism and aminoacyl-tRNA biosynthesis significantly enriched as the metabolic pathways. Glycerophospholipid metabolism primarily yields sn-glycerol-3-phosphate and 1,2-diacyl-sn-glycerol, which are ultimately broken down through glycolytic pathway and TCA cycle. Succinic acid reached the highest level of 1856.81 ± 113.50 μg/mL in coconut water at 24 h of storage. As a TCA cycle–derived metabolite, its accumulation was associated with pH reduction and oxidative rancidity of coconut water, which suggests that trace lipids and amino acids and their derivatives markedly influence coconut water quality during storage. Correlation results indicate that there are significant correlations among differential lipids and differential amino acids and their derivatives, with negative correlations predominating. The findings indicate that the quality deterioration of coconut water is associated with lipid oxidation and the accumulation of succinic acid.

Keywords: Coconut water, Lipidomics, Amino acids, Quality deterioration, Lipid oxidation

Graphical abstract

Image 1

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Highlights

  • •

    Targeted lipidomics and amino acids and their derivatives were used for analysis.

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    The lipid content in coconut water increases with the extended storage time.

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    The metabolites in coconut water accumulate in the form of succinic acid.

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    Differential lipids showed a significant negative correlation with free amino acids.

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    The quality deterioration is significantly correlated with lipid oxidation.

1. Introduction

Coconut water is very popular in the market due to its unique sensory characteristics and richness in electrolytes, micronutrients and bioactive compounds (Ceballos et al., 2025; Li et al., 2024). However, the quality of fresh coconut water deteriorates rapidly during storage due to its rich organic content (Pandiselvam et al., 2022), and the quality deterioration is characterized by loss of flavor, browning and degradation of bioactive components (Prithviraj et al., 2021). In addition to traditional causative factors such as microbial spoilage and enzymatic browning, lipid oxidation has been recognized as a predominant cause of quality loss in fruit and vegetable products (Hasan et al., 2021). The products of lipid degradation (such as free fatty acids and peroxides) can accelerate browning through the Maillard reaction and form precipitates by cross-linking with proteins, which further affects the organoleptic and nutritional qualities (Nawaz et al., 2022). Appaiah et al. (2015) have reported that the lipid content in coconut water is approximately 0.2-1.2%. Although its content is low, it still has an important impact on food quality (Wang et al., 2023). Furthermore, lipid oxidation was found to be associated with the quality deterioration of coconut water, and linoleic acid metabolism was recognized as a major pathway through untargeted metabolomics by Wang et al., 2025a, Wang et al., 2025b. Therefore, in-depth analysis of the action mechanism of lipids in coconut water is important to clarify the deterioration mechanism.

Lipids are a class of hydrophobic or amphiphilic biomolecules, mainly composed of hydrocarbon chains, including triglycerides, phospholipids, steroids and many other types, which are widely existed in all kinds of food (Maiti and Bhattacharya, 2022). Lipids not only affect the flavor of food, but also the main target of oxidation reactions. The dynamic changes in their composition and metabolites directly affect the nutritional value, sensory properties and storage stability of food (Shahidi and Hossain, 2022). Under the continuous effect of the external environment (such as oxygen or temperature), lipids will gradually enter an oxidized stage from the stable state. Lipid oxidation not only affects the flavor profile and nutritional function of food through the generation of small molecule products such as aldehydes, ketones and hydroperoxides, but may also trigger secondary reactions such as protein cross-linking and vitamin degradation (Dragoev, 2024; Jiang et al., 2021). Traditional physicochemical indicators such as peroxide value and acid value can reflect the degree of oxidation, but it is difficult to identify early oxidative properties or to analyze the dynamic changes of specific lipid molecule (Flores et al., 2021). Lipidomics, as an important branch of emerging metabolomics, realizes the global analysis of lipid molecules in food by integrating high-resolution mass spectrometry and chromatographic separation techniques, which provides a key tool for revealing lipids metabolic network and their association with food quality (Han and Gross, 2022; Zhang et al., 2023). Qu et al. (2023) showed that brown rice stored at 15 °C could better maintain its quality and reduce the catabolism of glycerophospholipids (GP) by using non-targeted lipidomics. According to the lipidomics research by Lv et al. (2023), the transformation of lipids in chicken breast meat was likely associated with an elevated lysophospholipid-to-phospholipid ratio and enhanced lipid oxidation. These findings suggest that the use of lipidomics can provide an in-depth insight into the trends and action mechanisms of trace lipids during the quality deterioration of coconut water.

In addition, amino acids and their derivatives, as the core functional components in the food system, are not only involved in protein synthesis, but also contribute substantially to the development of characteristic food flavors (Zhan et al., 2022). The study revealed that amino acids and their derivatives accounted for approximately 1% of the total constituents in coconut water, with glutamate, alanine, and aspartate present at relatively higher levels (Zhang et al., 2024). These active molecules are not only the chemical basis of its fresh and sweet flavor, but also key factors in the deterioration pathways such as the Maillard reaction and enzymatic browning (Han et al., 2024). Essential amino acids (such as lysine and methionine) contribute directly to the nutritional quality of foods, whereas non-essential amino acids such as glutamate and proline participate in the generation of color, flavor, and umami attributes through the Maillard reaction and Strecker degradation pathways (Galili and Amir, 2013). Through untargeted and targeted metabolomics, Shen et al. (2022) found that amino acids play a crucial role in determining tea flavor. Their levels and related derivatives were significantly associated with storage duration, and the observed reduction in free amino acids was likely caused by Maillard and Strecker reactions. Most of the current research focuses on microbial spoilage of coconut water, but the role of changing amino acid and their derivatives contents in quality deterioration is not addressed. Although the content of amino acids and their derivatives in coconut water is relatively low, Xu et al. (2025) have reported that they have an important influence on its quality. Therefore, analysis of the alterations in amino acid and their derivatives content and metabolic pathways in coconut water during storage is essential for uncovering the mechanisms underlying its quality deterioration.

In recent years, foodomics technology has provided a new tool for systematically analyzing the quality changes of foods (Balkir et al., 2021). Current researches mostly focus on the changes of single quality indicators (such as the abundance of volatiles), and little attention has been directed towards the role of lipid-amino acid interactions in influencing food quality within multicomponent systems. Coconut water is popular as an electrolyte-rich beverage, but its short storage time and perishable nature seriously affect its production value. In view of the fact that the present study does not focus on the effect of trace lipids, amino acids and their derivatives in coconut water on its quality during the storage. In this study, the metabolic pattern of lipids, amino acids and their derivatives during coconut water storage was systematically investigated by combining targeted lipidomics and targeted amino acids and their derivatives. The molecular mechanism of coconut water quality deterioration elucidated in this study not only clarifies the key metabolites and their interaction networks, but also provides a stable theoretical foundation and practical guidance for optimizing preservation strategies, screening early molecular markers for quality deterioration, and subsequent integrated multi-omics analysis.

2. Materials and methods

2.1. Materials and sample preparation

Wenye No.2 coconuts of uniform size at 8-month maturity were provided by the Coconut Research Institute (CRI) in Wenchang, China. This study harvested fifty coconuts sourced from five distinct plantation bases. The coconut water was pooled and homogenized after shelling to reduce inter-sample variability and improve the generalizability of the results. The mixed coconut water was aliquoted into 100 mL portions within aseptic bags and maintained at 25 °C for 24 h.

2.2. Measurement of quality indicators

In order to exhibit the degree of lipid oxidation in coconut water, pH, total plate count, the content of malondialdehyde (MDA), and acid value of coconut water were measured. Total plate count, MDA, and acid value were determined according to Chinese national standards GB 4789.2–2022, GB 5009.181–2016, and GB 5009.229–2025, respectively.

The method for determining acid value is as follows: the samples were serially diluted with sterile physiological saline, and 1 mL of each dilution was transferred into sterile petri dishes. Subsequently, 15-20 mL of plate count agar was added and thoroughly mixed. Plates were incubated at 36 ± 1 °C for 48 ± 2 h, after which colonies were enumerated.

The method for determining MDA content is as follows: standard MDA solutions were prepared at concentrations of 0.01, 0.05, 0.10, 0.15, and 0.25 μg/mL. Briefly, 5.0 g of sample was accurately weighed into a 100 mL stoppered conical flask, followed by the addition of 50 mL trichloroacetic acid (TCA) mixed solution. The mixture was shaken at 50 °C for 30 min in a thermostatic shaker, cooled to room temperature, and filtered through double-layer quantitative filter paper. The initial filtrate was discarded. Then, 5 mL of the subsequent filtrate and standard solution was transferred into a 25 mL stoppered colorimetric tube, while 5 mL of TCA solution served as the blank. Each tube was reacted with 5 mL thiobarbituric acid aqueous solution at 90 °C for 30 min, then cooled to room temperature. The absorbance was measured at 532 nm using a 1 cm optical path length, with the blank used for zero adjustment. The MDA content in samples was calculated based on the standard calibration curve.

The method for determining acid value is as follows: oleic acid working solutions (0.0, 0.1, 0.5, 1.0, 3.0, and 5.0 μg/mL) were prepared in cyclohexane. For calibration, 5 mL of each standard solution was mixed with 2 mL of copper acetate solution in a 50 mL centrifuge tube, vortexed for 30 s, and allowed to stand for phase separation. The upper phase was filtered, and absorbance was measured at 710 nm using a 1 cm cuvette, with the 0.0 μg/mL standard as the blank. A standard curve was constructed accordingly. For sample preparation, 1.0 g of coconut water was accurately weighed, dissolved in cyclohexane, and diluted to 5 mL. The solution was transferred to a 50 mL centrifuge tube, mixed with 3-5 mL of deionized water, vortexed for 40-60 s, and allowed to separate. Subsequently, 4 mL of the upper organic phase was reacted with 2 mL of copper acetate solution under the same conditions as the standards. The absorbance of the filtered supernatant was measured at 710 nm. The total free fatty acid content was quantified from the calibration curve and expressed as oleic acid equivalents, which were then converted to the acid value of the sample.

2.3. Targeted lipidomics analysis

Analytical separation was achieved using an ultra-performance liquid chromatography system (UPLC, ExionL AD) coupled with QTRAP® 6500+ mass spectrometer (MS/MS). Chromatographic separation employed a Thermo Accucore™ C30 column (2.6 μm, 2.1 mm × 100 mm i.d.) with mobile phase conditions and gradient program adapted from Liu et al. (2023). The mobile phase consisted of solvent A, acetonitrile/water (60/40, v/v) containing 0.1% formic acid and 10 mmol/L ammonium formate, and solvent B, acetonitrile/isopropanol (10/90, v/v) containing 0.1% formic acid and 10 mmol/L ammonium formate. The gradient elution program was as follows: the A/B ratio was set at 80:20 (v/v) at 0 min, adjusted to 70:30 at 2 min, 40:60 at 4 min, 15:85 at 9 min, 10:90 at 14 min, maintained at 5:95 from 15.5 to 17.3 min, and finally returned to 80:20 at 17.5 min and held until 20 min. The flow rate was set at 0.35 mL/min, the column temperature was maintained at 45 °C, and the injection volume was 2 μL.

2.4. Analysis of targeted amino acids and their derivatives

A 2 mL sample was lyophilized, followed by the addition of 300 μL of 70% (v/v) aqueous methanol containing 12 μL of an internal standard solution (L-threonine-2,3-d2, 250 ng/mL). The mixture was vortex-mixed for 5 min and centrifuged at 12,000 r/min for 10 min at 4 °C. Subsequently, 250 μL of the supernatant was transferred to a new centrifuge tube, re-centrifuged, and 180 μL of the resulting supernatant was collected for analysis. Analytical standards (>99% purity) sourced from Sigma-Aldrich and Shanghai Zhenzhun Biotechnology were used to generate calibration curves at seventeen concentrations (0.01-2000 μg/mL). The concentration levels were set at 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000 and 2000 μg/mL. Mass spectral responses at each level were recorded for quantitative analysis. Standard curves were generated with the concentration ratio (external/internal standard) as the abscissa and peak area ratio (external/internal standard) as the vertical ordinate. By substituting the ratio of peak areas obtained from all detected samples into the linear equation of the standard curves for calculation, the final concentrations of the substances in the actual samples can be determined. Chromatographic separation and detection were performed using an ExionL AD™ UPLC system coupled to a QTRAP® 6500+ triple-quadrupole mass spectrometer. The chromatographic and mass spectrometry conditions were referred to the research of (Guo et al., 2013), with specific details presented in the supplementary materials.

2.5. Data analysis

All quality indicators for samples in this study were measured with three technical replicates, and the results were presented as mean ± standard deviation (SD). Statistical differences were evaluated using SPSS Statistics v26.0 (IBM Corp., USA) with Tukey's post hoc test (p < 0.05 criteria). Multivariate analysis employed OPLS-DA via the MetaboAnalyst web platform (https://www.metaboanalyst.ca). The significance analysis of metabolites in coconut water at different storage times was conducted using the t-test. Correlation analysis was performed using the Metware Cloud, a free online platform for data analysis (https://cloud.metware.cn).

3. Results and discussion

3.1. Analysis of quality indicators

Fig. 1 presents the results of quality indicators including pH (Fig. 1A), total plate count (Fig. 1B), MDA (Fig. 1C), and acid value (Fig. 1D) of coconut water at different storage times. The results of quality indicators indicate that the pH of coconut water falls below 5 when stored at 24 h. According to Detudom et al. (2023), coconut water deterioration initiates when pH declines below 5.0. Coconut water demonstrated that total plate count >104 CFU/mL at 24 h, surpassing both Chinese regulatory thresholds (GB 7101-2022: 104 CFU/mL) and FAO quality guidelines (>5000 CFU/mL). These parameters constitute definitive deterioration criteria for coconut water. MDA content and acid value in coconut water exhibited a duration-dependent increase during storage, which indicates that its trace lipid content has undergone severe oxidation and hydrolytic rancidity. This directly contributes to product quality deterioration and these quality indicators indicate that lipid oxidation significantly influences the coconut water quality.

Fig. 1.

Fig. 1

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The results of quality indicators of coconut water during the storage. (A) pH; (B) total plate count; (C) malondialdehyde (MDA); (D) acid value.

3.2. Determination results of targeted lipidomics

3.2.1. Lipid composition analysis

A total of 517 lipid components were identified in this study, with 276 and 241 lipids detected in negative and positive ion modes, respectively. These lipids contain five major classes, fatty acyls (FA), glycerolipids (GL), GP, prenolipids (PR), and sphingolipdis (SP), of which 24 are FA, 295 are GL, 130 are GP, 2 are PR, and 66 are SP. There are 25 subclasses contained in these 5 classes and the lipid composition is presented in Fig. 2A (0 h) and Fig. 2B (24 h), respectively. The number of triacylglycerol (TG) is the highest with 222 as well as 232 species in coconut water stored for 0 and 24 h, which accounted for 45.68% and 45.14% of the number of lipids, respectively. The total lipid content and the content of each subclass in coconut water at different storage times are shown in Fig. 2C and D, respectively. The lipid content increased from 23.54 ± 0.18 μg/mL at 0 h to 170.11 ± 16.91 μg/mL at 24 h. This indicates that storage time correlates positively with rising lipid content. Appaiah et al. (2015) also showed that lipid content exhibits progressive elevation during maturation. Among the 25 subclasses, phosphatidylglycerol (PG), lyso-phosphatidylethanolamine (LPE), phosphatidic acid (PA), phosphatidylethanolamine (PE) and phosphatidylserine (PS) showed the most significant changes with storage time, and their contents changed from 0.08 ± 0.00 μg/mL, 0.10 ± 0.01 μg/mL, 0.17 ± 0.00 μg/mL, 0.81 ± 0.01 μg/mL, and 0.25 ± 0.02 μg/mL at 0 h to 1.50 ± 0.05 μg/mL, 4.46 ± 0.54 μg/mL, 4.86 ± 1.51 μg/mL, 13.96 ± 1.51 μg/mL, and 120.27 ± 20.88 μg/mL at 24 h, respectively. PE, PG, and PS represent the predominant phospholipids in Escherichia coli (E. coli) membranes and are ubiquitous across bacterial taxa (Bittman, 2013). It is also noteworthy that the contents of free fatty acid (FFA) and TG were higher at both storage times and their contents were not significantly changed during the storage period. This indicates that the type and content of FFA as well as TG in coconut water did not change significantly with storage time.

Fig. 2.

Fig. 2

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The composition of lipids in coconut water at 0 h (A) and 24 h (B) of storage. The total lipid content (C) and the content of each subclass (D) in coconut water.

The result of principal components analysis (PCA) of the lipids identified at the two storage times is shown in Fig. 3A. It reveals pronounced within-group clustering of coconut water lipids at both storage times, with clear separation confirming significant lipidomic difference. PC1 and PC2 explained 45.52% and 20.58% of the total variation, respectively. In addition, it can be seen that the difference between different storage times of coconut water is mainly in PC1, where the coconut water stored for 0 h is mainly concentrated in the first and fourth quadrants, while the coconut water stored for 24 h is mainly concentrated in the second and third quadrants.

Fig. 3.

Fig. 3

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(A) The results of PCA of lipid content in coconut water at different storage times. The OPLS-DA model score plot (B) and permutation test results (C) of lipids. Volcano plot of differential lipids in coconut water at different storage times (D).

OPLS-DA employs supervised multivariate dimensionality reduction to filter orthogonal variance, enabling high-specificity screening of differential metabolites (Blasco et al., 2015). Therefore, in order to analyze the differential lipids in coconut water during storage, OPLS-DA was further used and the result is shown in Fig. 3B. The separation effect is more obvious for two storage times after excluding the irrelevant information. Model validity was assessed through 200 response permutation testing and the result is shown in Fig. 3C. The intercept of the Q2 regression line with the vertical axis in the figure is less than 0. The results showed that no overfitting occurred in the established OPLS-DA model, indicating that the model is stable, reliable and interpretative (Chen et al., 2023).

Differential lipids in coconut water were identified using thresholds of p < 0.05, VIP >1, and FC > 2 or FC < 0.5, with results detailed in Table S1. A total of 74 differential lipids, including 2 FA, 9 GL, 60 GP, and 3 SP, were screened between two storage times. It can be seen that the content of GP species in coconut water changed significantly with the increase of storage time. A volcano plot showed in Fig. 3D was used to show the variability and significance between coconut water samples, where each point represents a substance, with down-regulated and up-regulated metabolites denoted as green and red points, respectively. The gray color indicates that there is no significant difference in the corresponding metabolites detected. The most significantly different up-regulated and down-regulated lipids were PE (16:1_18:2) and PA (16:0_18:2), respectively. In order to further observe the change pattern of lipid content, the raw content of differential lipids was normalized and plotted in a cluster heatmap, and the result is depicted in Fig. 4A. The specific quantitative information of the differential lipids is shown in Table S2. Both coconut water groups clustered in a good trend and the content of most of the lipids increased with storage time. The correlation analysis of the top 50 differential lipids was performed and the result is shown in Fig. 4B. A significant positive correlation can be seen between all differential lipids except for lyso-phosphatidic acid (LPA) (18:1), lyso-phosphatidylglycerol (LPG) (16:3), and PA (16:0_18:2). The largest positive correlation was found between PE (18:1_18:1) and PE (16:0_18:1) (correlation coefficient = 0.9999), while the largest negative correlation was found between PG (18:1_16:1) and PA (16:0_18:2) (correlation coefficient = −0.9992).

Fig. 4.

Fig. 4

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Clustered heatmap (A) and correlation analysis (B) of differential lipids of coconut water at different storage times. The annotation results (C) and enrichment analysis (D) of differential lipids using KEGG.

3.2.2. Metabolic pathway analysis

Lipid interactions can result in the formation of different pathways (Anderson, 2006). The differential lipids were annotated using the KEGG database, and the annotation results are shown in Fig. 4C. A total of 52 KEGG pathway types were annotated, with the most differential lipids annotated to glycerophospholipid metabolism and metabolic pathways. The enrichment of differential lipids yielded a total of 18 metabolic pathways with significant differences (hypergeometric distribution p-value <0.05) (Table S3 and Fig. 4D), of which the metabolic pathway with the smallest p-value was glycerophospholipid metabolism. This pathway contained 154 metabolites detected, of which 60 were KEGG-annotated differential metabolites.

Fig. 5 depicts the metabolic pathway of GP, with substances in red font indicating the lipids showing significant differences during coconut water storage. The corresponding heat map illustrates the trend of their content changes throughout the storage process and their specific contents were presented in Table S4. Glycerone phosphate, generated from glycerol lipid metabolism, enters glycerophospholipid metabolism and leads to the formation of products primarily consisting of sn-glycerol-3-phosphate and 1,2-diacyl-sn-glycerol. Among these, sn-glycerol-3-phosphate can be converted into dihydroxyacetone phosphate via the action of 3-phosphoglycerate dehydrogenase, entering the glycolytic pathway to ultimately produce pyruvate. Pyruvate undergoes decarboxylation, dehydrogenation, and coenzyme A conjugation to form acetyl-CoA, which enters the TCA cycle for further breakdown.

Fig. 5.

Fig. 5

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The pathway of glycerophospholipid metabolism.

From the perspective of the overall metabolic network, glycerophospholipid metabolism serves as a pivotal link between phospholipid synthesis and degradation, with PA and diacylglycerol acting as central hubs. The heatmap reveals that PC, PE, and PS related metabolites undergo significant changes during storage. The change of the PC/PE ratio is commonly regarded as a key indicator of alterations in membrane fluidity and integrity, with its imbalance suggesting a decline in membrane structural stability during storage.

Notably, LPC, LPE and LPG exhibited an upward trend during storage, reflecting the activation of hydrolytic reactions such as those catalyzed by phospholipase A2 and phospholipase C. This phenomenon is typically closely associated with the release of endogenous enzymes or enhanced oxidative stress. The accumulation of lysophosphalipids not only indicates intensified membrane lipid degradation but also acts as a surfactant further disrupting membrane structure. These metabolic pathways are associated with the release and oxidation of polyunsaturated fatty acids, providing substrates for lipid oxidation and the formation of volatile aldehydes and ketones—flavor compounds that constitute a major contributor to off-flavors and quality deterioration in coconut water. In biological perspective, the metabolism of GP reflects the disruption of cell membrane stability and the intensification of endogenous and exogenous enzymatic reactions, serving as a key metabolic characteristic marking the transition from freshness to deterioration of coconut water. GP are one of the main components of cell membranes, and their structure consists of three main parts: glycerol skeleton, fatty acid chains, and phosphate groups (Hishikawa et al., 2014). The results of the study showed that the content of GP tended to increase with storage time.

In addition, some of the GP are decomposed by phospholipases to produce glycerol, fatty acids, phosphoric acid, and organic groups. Among them, glycerol is activated to glycerol 3-phosphate by glycerol kinase, and then catalyzed by dehydrogenase to produce dihydroxyacetone phosphate, which enters the carbohydrate metabolism pathway. Fatty acids are first activated to fatty acyl-CoA, which is then progressively decomposed to acetyl-CoA through β-oxidation processes into the carbohydrate metabolism pathway (Panov et al., 2024). While some unsaturated fatty acids will be oxidized by oxygen in the air, generating aldehydes, ketones and low molecular organic acids with bad odor, further promoting the oxidative rancidity (Wang et al., 2025a, Wang et al., 2025b).

3.3. Determination results of targeted amino acids and their derivatives

3.3.1. Compositional analysis of amino acids and their derivatives in coconut water

In this study, 61 amino acids and their derivatives were quantified. Among these, 58 were detected in positive ion mode, while the remaining 3 were identified using negative ion mode. Table S5 presents the quantitative profile of amino acids and their derivatives in coconut water under different storage durations, in which the highest content of glutamine (91.57 ± 7.18 μg/mL) was found in coconut water stored at 0 h, and the highest content of succinic acid (1856.81 ± 113.50 μg/mL) was found in coconut water stored at 24 h.

PCA analysis of two storage times was carried out and the result is shown in Fig. S1A. It can be seen that there is good aggregation within the groups and no crossover between the two storage times, indicating that the types and contents of amino acids and their derivatives in coconut water varied significantly across the storage periods. PC1 and PC2 explained 87.19% and 6.57% of the total variance, respectively, with their cumulative contribution exceeding 90%. Notably, the separation between samples was mainly driven by PC1: samples at 0 h were predominantly distributed in the first and fourth quadrants, whereas those at 24 h were mainly located in the second and third quadrants.

In order to analyze the differential amino acids and their derivatives in coconut water during storage, the OPLS-DA model was further developed to analyze the amino acids and their derivatives in coconut water and the result is shown in Fig. S1B. The coconut water of two storage times can be effectively separated, indicating that the metabolite profile components are significantly different. The R2X, R2Y and Q2 of the OPLS-DA model were 0.923, 0.997 and 0.982, respectively, which are close to 1, indicating that the model is effective in explaining and predicting between the two groups. Fig. S1C illustrates the results of the permutation test. The intercept of the Q2 regression line with the vertical axis in the figure is less than 0. The results showed that no overfitting occurred in the established OPLS-DA model, which indicates that the model is reliable.

Differential amino acids and their derivatives in coconut water were identified using thresholds of p < 0.05, VIP >1, and FC > 2 or FC < 0.5, with results detailed in Table S6. Comparative analysis identified 41 differential metabolites in coconut water between the two storage times, comprising 14 up-regulated and 27 down-regulated compounds. With prolonged storage, most amino acids and their derivatives in coconut water declined in content. A volcano plot was used to demonstrate the variability and significance among the coconut water samples and the result is shown in Fig. S1D. It can be seen that the up-regulated and down-regulated with the most significant differences were D-alanyl-D-alanine and L-tyrosine, respectively.

In order to observe the change pattern of the content of amino acids and their derivatives, the raw content of differential amino acids and their derivatives were normalized and plotted in a cluster heatmap, and the result is shown in Fig. 6A. The specific quantitative information of the differential amino acids and their derivatives is shown in Table S5. It is noted that the two groups of coconut water samples showed a good clustering trend, and a near-universal reduction in free amino acid levels was observed with prolonged storage. The differential metabolites were performed by correlation analysis and the correlation heatmap is shown in Fig. 6B. The metabolite pairs such as L-glutamine and L-serine, L-tyrosine and L-phenylalanine show a strong positive correlation, suggesting that these amino acids may be involved in similar metabolic pathways or affected by the same regulatory mechanisms. Some metabolite pairs (such as L-theanine and L-serine) were negatively correlated, indicating that they showed opposite trends during storage. The largest positive correlation was found between L-serine and L-carnosine (r = 0.9999), while the largest negative correlation was found between Nα-acetyl-L-arginine and L-tyrosine (r = −0.9972). In addition, it can be noted that substances like 5-hydroxy-tryptophan and N-acetylneuraminic acid are negatively correlated with various free amino acids.

Fig. 6.

Fig. 6

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Clustered heatmap (A) and correlation analysis (B) of differential amino acids and their derivatives of coconut water at different storage times. The annotation results (C) and enrichment analysis (D) of differential amino acids and their derivatives using KEGG.

3.3.2. Metabolic pathway analysis

The differential amino acids and their derivatives were annotated and the result is shown in Fig. 6C. Among the 63 annotated KEGG pathway types, the majority of differential amino acids and their derivatives were predominantly enriched in metabolic pathways, biosynthesis of secondary metabolites, and amino acid biosynthesis. The enrichment of differential amino acids and their derivatives yielded only one metabolic pathway with significant differences (hypergeometric distribution p-value <0.05) (Fig. 6D), which is aminoacyl-tRNA biosynthesis. The number of differential metabolites in this pathway that were annotated by KEGG was 16, and the number of metabolites detected that belonged to the pathway was 18.

The aminoacyl-tRNA biosynthesis pathway is a key precursor step in protein synthesis, and the main function of the pathway is to attach specific amino acids to their corresponding tRNA via aminoacyl-tRNA synthetase (AARS) to provide raw materials for protein synthesis by the ribosome (Pang et al., 2014). This process is highly dependent on the concentration of free amino acids and the metabolic state. With the prolonged storage time, rapid microbial multiplication and increased protein synthesis requirements drive an active aminoacyl-tRNA biosynthesis pathway (Parker et al., 2020). These alterations are likely linked to the decline in flavor and nutritional value, as well as the overall quality of coconut water, providing key insights into its deterioration mechanism (Yan et al., 2023; Zhao et al., 2016). Therefore, subsequent studies can combine proteomics, microbiomics, and transcriptomics to deeply analyze the specific active drivers of the pathway.

Notably, the content of succinic acid rose significantly during storage and consequently became the predominant metabolite in the 24 h coconut water samples. Succinic acid is an important substance in glycometabolism (Sun et al., 2013), combining the previous study of Wang et al., 2025a, Wang et al., 2025b and the results of lipidomics in this study, the mechanism of quality deterioration and metabolic process of coconut water can be obtained as shown in Fig. 7. The accumulation of lipids promotes the generation of oxidized products such as 12,13-DHOME and the formation of MDA. MDA further crosslinks cell membrane phospholipids, altering membrane permeability, and reacts with carbohydrates via the Maillard reaction, leading to color darkening. GP are decomposed by phospholipases into glycerol, free fatty acids, and phosphate groups. Glycerol can be converted into pyruvate via dihydroxyacetone phosphate, which is subsequently oxidatively decarboxylated to form acetyl-coenzyme A (CoA) entering the tricarboxylic acid (TCA) cycle. Free fatty acids are catabolized via β-oxidation, generating acetyl-CoA that is further oxidized in the TCA cycle. Simultaneously, the oxidation of unsaturated fatty acids (such as linoleic and linolenic acid) produces volatile compounds like (E, E)-2,4-heptadienal and (E)-2-nonenal, which promote the flavor deterioration of coconut water (Wang et al., 2025a, Wang et al., 2025b). In parallel, amino acids such as glutamine are deaminated to form α-ketoglutarate, which enters the TCA cycle. As a result of TCA cycle metabolism, metabolites in coconut water accumulate in the form of succinic acid, leading to a decrease in pH and causes the oxidative rancidity of coconut water.

Fig. 7.

Fig. 7

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The deterioration mechanism of coconut water quality during the storage.

3.4. Correlation analysis

To elucidate the action mechanisms of these metabolites during the storage of coconut water, the correlation analysis among the differential lipids and amino acids and their derivatives were performed. The result is presented in Fig. S2, where ∗∗∗ indicates a significant difference (p less than 0.001); ∗∗ indicates a significant difference (p less than 0.01); ∗ indicates a significant difference (p less than 0.05). In general, differential lipids exhibit more negative correlations than positive correlations with differential amino acids and their derivatives, particularly free amino acids. Among the correlations between 74 differential lipids and 41 differential amino acids and their derivatives, except for L-theanine with TG (14:0_16:0_18:3), PE (18:0_18:0), FFA (22:5), TG (14:0_18:1_18:3), and L-α-aspartyl-L-phenylalanine with MGDG (14:0_16:0), SQDG (14:0_16:0), LPE (22:6) were less than 0.8 with p > 0.05, while significant correlations were observed between all other groups. Among them, LPA (18:1) exhibited the strongest positive correlation with L-asparagine anhydrous (r = 0.9999, p = 2.79E-09), while PA (16:0_18:2) showed the strongest negative correlation with N-α-acetyl-L-arginine (r = −0.9987, p = 2.72E-06). Overall, among the 74 differential lipids, PE (16:1_18:2), PE (16:1_16:1), PA (16:0_18:2), LPA (18:1), PG (16:0_18:1), and PG (18:1_16:1) exhibited highly significant correlations with the differential amino acids and their derivatives. This indicates that the quality deterioration of coconut water is primarily associated with the lipids containing C16:0, C16:1, C18:1, and C18:2. Regarding differential amino acids and their derivatives, free amino acids exhibit highly significant correlations with most lipids, with their content gradually decreasing over extended storage times.

4. Conclusion

This study employed an integrated approach of high-throughput targeted lipidomics and analysis of amino acids and derivatives to investigate the quality deterioration mechanism of coconut water. The result showed that lipids gradually accumulated in the coconut water as storage progressed, which could reach 170.11 ± 16.91 μg/mL at 24 h. The analysis of targeted amino acids and their derivatives showed that glutamine (91.57 ± 7.18 μg/mL) and succinic acid (1856.81 ± 113.50 μg/mL) are the most abundant substances in coconut water stored at 0 h and 24 h, respectively. A total of 75 lipids and 41 amino acids and their derivatives were detected as differential metabolites. Correlation analysis indicates that there is a significant correlation between amino acid and lipid metabolism during the storage of coconut water, with the content of free amino acids decreasing as storage time increases. Correlation results indicate that the quality deterioration of coconut water is primarily associated with lipids containing C16:0, C16:1, C18:1, and C18:2. The findings of this study not only clarify the role of trace compounds in the quality deterioration of coconut water but also provide a crucial theoretical benchmark for assessing the quality of other fruit juice beverages.

CRediT authorship contribution statement

Tao Wang: Writing-original draft, Data curation, Methodology, Writing-review & editing. Mengyang Zhang: Investigation, Visualization. Wenxue Chen: Project administration, Funding acquisition. Lipin Chen: Resources, Conceptualization. Meizhen Xie: Resources, Conceptualization. Yong-Huan Yun: Writing-review & editing, Resources, Funding acquisition, Project administration.

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.

Acknowledgements

This work was supported by the Key Research and Development Project of Hainan Province (No.ZDYF2025GXJS186), the National Natural Science Foundation of China (No. 22164008), Hainan Provincial Natural Science Foundation of China (323QN202), specific research fund of The Innovation Platform for Academicians of Hainan Province (No. SPTZX202318), Key Laboratory of Tropical Fruits and Vegetables Quality and Safety for State Market Regulation (No. ZX-2024001), and the Innovation Platform for Academicians of Hainan Province.

Handling Editor: Professor Aiqian Ye

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crfs.2026.101350.

Appendix A. Supplementary data

The following is the Supplementary data to this article.

Multimedia component 1
mmc1.docx (69.8MB, docx)

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