Metabolomics Analysis of the Deterioration Mechanism and Storage Time Limit of Tender Coconut Water during Storage

Abstract

Tender coconut water tastes sweet and is enjoyed by consumers, but its commercial development is restricted by an extremely short shelf life, which cannot be explained by existing research. UPLC-MS/MS-based metabolomics methods were used to identify and statistically analyze metabolites in coconut water under refrigerated storage. A multivariate statistical analysis method was used to analyze the UPLC-MS/MS datasets from 35 tender coconut water samples stored for 0–6 weeks. In addition, we identified other differentially expressed metabolites by selecting p-values and fold changes. Hierarchical cluster analysis and association analysis were performed with the differentially expressed metabolites. Metabolic pathways were analyzed using the KEGG database and the MetPA module of MetaboAnalyst. A total of 72 differentially expressed metabolites were identified in all groups. The OPLS-DA score chart showed that all samples were well grouped. Thirty-one metabolic pathways were enriched in the week 0–1 samples. The results showed that after a tender coconut is peeled, the maximum storage time at 4 °C is 1 week. Analysis of metabolic pathways related to coconut water storage using the KEGG and MetPA databases revealed that amino acid metabolism is one of the main causes of coconut water quality deterioration.

Characterization of Volatile Profiles and Marker Substances by HS-SPME/GC-MS during the Concentration of Coconut Jam

“…In fresh coconut water, the main esters are ethyl hexanoate, ethyl octanoate, ethyl decanoate and ethyl dodecanoate. In particular, ethyl decanoate and ethyl dodecanoate are present at the highest concentrations and represent approximately half of all the esters present [23,31]. Ethyl decanoate, ethyl dodecanoate and hexyl…”

Multi-omics Analysis Reveals Whether and how Naphthylacetic Acid Affects the Quality of Rehmannia Glutinosa

“…Metabolites extraction [69][70][71] Accurately weigh 50mg (±1%) of each sample in 2 mL EP tube, and add 0.6 mL 2-chlorophenylalanine (4 ppm) methanol (-20 ℃), vortex for 30 seconds; Add 100 mg glass beads and grind the samples by a high-throughput tissue grinder for 90 seconds at 60 Hz; Ultrasonic treatment for 15 minutes at room temperature; Centrifuge at 4 ℃ for 10 min at 14000 rpm, and the supernatant was ltered through 0.22 µm membrane to obtain the prepared samples for LC-MS; Take 30 µL from each sample to the quality control (QC) samples (These QC samples were used to monitor deviations of the analytical results from these pool mixtures and compare them to the errors caused by the analytical instrument itself) [72];…”

“…The exact masses of metabolites were con rmed according to mass error < 15ppm. Then, Fragment information from MS / MS mode in data matix were further matched in the databases such as HumanMetabolome Database (HMDB) ( http://www.hmdb.ca), Metlin (http://metlin.scripps.edu), massbank (http://www.massbank.jp/), LipidMaps (http://www.lipidmaps.org), mzclound (https://www.mzcloud.org) and Self-built standard product database [72].…”

“…The calculation of Z-score is based on the mean and standard deviation of reference data set (control group). The speci c formula is Z = (xμ) / σ, in which x is a speci c fraction, μ is the average, σ is the standard deviation [72].…”

“…MetPA database can be used to analyze the metabolic pathways related to two groups of different metabolites. MetPA database identi es the metabolic pathways that may be disturbed by organisms through metabolic pathway concentration and topological analysis, and then analyzes the metabolic pathways of metabolites [72].…”