Base peak ion current check
We firstly conducted a visual inspection of the base peak ion current of all samples and found that signal peak capacity and retention time repeatability were good in all samples. Figures 1 and 2 showed the base peak chromatogram of the quality control samples in TIC+ and TIC- mode, respectively.
Establish a data matrix
Metabolomics analysis was performed using the pulp of peeled eggplant after 0 (CK), 3 and 5 min. To carry out pattern recognition, the raw data were preprocessed by the metabolomics processing software progenesis QI, including peak recognition, peak alignment, integration, baseline filtering, retention time correction, normalization. In the end, a data matrix was constructed, which include the sample name, mass-to-charge ratio (m/z), retention time (RT), ion model, and metabolites. There are 15133 m/z features in this matrix, including 7038 m/z in TIC+ and 8095 m/z in TIC- (Additional file 1: Table S1). This matrix was gone to the subsequent analysis.
In the data matrix, the data were disposed by multivariate analysis tools (PCA, PLS-DA and OPLS-DA). PCA, PLS-DA and OPLS-DA scoring plots and validation plots of the OPLS-DA models were built for the three contrastive groups: 3min/CK, 5min/CK and 3min/5min (Fig. 3). Table 1 showed all the parameters of these models. A confidence interval is a measure of confidence, usually the bigger the better. R2X represents the cumulative interpretation rate of the multivariate statistical analysis modeling, which generally requires R2X > 0.4, indicating that the model is credible. R2 and Q2 are the parameters of response sequencing test, used to measure whether the model is overfitted. External validation generally requires Q2 < 0 to avoid over-fitting. Internal validation generally requires R2, > 0.5; The closer R2 is to 1, the better the model. In our results, all values for Hotelling’s T2 were 95% , Q2 were < 0 (Q2 = −0.849 to −0.741), R2X were >0.4, and R2 were > 0.5, indicating that the model is reliable.
According to the VIP > 1 for the first principal component in the OPLS-DA, and p-value < 0.05 were the criteria for screening differential metabolites, we identified the differential metabolites among the three contrastive groups (Additional file 2: Table S2). 256 differential metabolites were detected in the 3min/CK comparative group, 357 in the 5min/CK comparative group and 333 in the 3min/5min comparative group. Among these metabolites, 49 were common to all three groups and 119 were common between 3min/CK and 5min/CK. However, 253 metabolites were the non-repeated differential metabolites between any two groups; 114, 58 and 81 metabolites were exclusive to the 3min/CK, 5 min/CK and 3min/5min comparative groups, respectively (Fig. 4).
The 119 common differential metabolites from the 3min/CK and 5min/CK groups were further divided into three sorts represented by A, B and C, as indicated in Additional File 3: Table S3 based on STC. Furthermore, the metabolites in these three categories were grouped into eight types of compounds (Table 2). The relative expression of 33 metabolites selected from the three categories (A, B, C) is shown in the heatmap (Fig.5 ).
Category A included 68 metabolites. These were mainly lipids, fatty acid and carbohydrates (Table 2). The contents and expression levels of these metabolites are increased gradually. The metabolites of category B comprised 40 compounds, most of which were fatty acids and lipids (Table 2). The contents and expression levels of these metabolites decreased with increase of the peeled time. There were 11 components in category C, mainly lipids (Table 2). Interestingly, their contents and expression levels decreased at 3 min, but are increased at 5 min. However, they were lower than those at 0 min (CK) as a whole.
Metabolite content changes
Eight phenols associated with browning were selected from the data matrix. The content levels of the selected metabolites at different treatment intervals shown in Fig. 6. The contents of chlorogenic acid and E-10-Hydroxyamitriptyline increased significantly with longer time from peeling. By contrast, octyl 4-methoxycinnamic acid, trimethobenzamide, (S)--Gingerol and cis--Shogaol contents decreased significantly. However, there was no remarkable alteration in the content of some metabolites , such as ethylvanillin glucoside and glucocaffeic acid. Furthermore, the changes in the contents of the selected metabolites (Fig. 6) were similar to the expression level changes shown in the heatmap (Fig. 5).
KEGG pathway analysis.
The metabolites in categories A, B and C were mapped, respectively, using the KEGG database onto the KEGG pathways with the following results (Fig. 7).
The metabolites of category A were mainly enriched in 18 metabolic pathways: Linoleic acid metabolism, Pentose phosphate pathway, Nitrogen metabolism, Glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Autophagy-other, Taurine and hypotaurine metabolism, Galactose metabolism, Aminoacyl-tRNA biosynthesis, Arginine biosynthesis, Arginine and proline metabolism, Alanine, aspartate and glutamate metabolism, C5-Branched dibasic acid metabolism, Glutathione metabolism, Butanoate metabolism, Histidine metabolism, ABC transporters, Glyoxylate and dicarboxylate metabolism, and Glycerophospholipid metabolism. Of these, Glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Linoleic acid metabolism, Autophagy-other and Nitrogen metabolism showed extremely significant differences in the 3min/CK and 5min/CK two comparative groups at the p < 0.01 level of signiﬁcance, while the others revealed a significant difference (p < 0.05). There was a remarkable difference (p < 0.05) in the metabolites of category B that were only enriched in one metabolic pathway, Tropane, piperidine and pyridine alkaloid biosynthesis. Finally, the metabolites of category C were enriched in Tyrosine metabolism and had a remarkable difference (p < 0.05) similarly.