The chemical composition and taste quality of tea fluctuate seasonally. However, the compounds responsible for the seasonal variation of metabolic pattern and taste quality are far from clear. This study compared the metabolite profiles of green teas of nine varieties that were plucked in spring, summer, and autumn by using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) on a reversed phase column. A multivariate analysis indicated distinct differences among the metabolite phenotypes of teas harvested in different seasons. Heat-map analysis and metabolic pathway analysis demonstrated that flavan-3-ols, theasinensins, procyanidins, quercetin-O-glycosides, apigenin-C-glycosides, and amino acids exhibited sharp seasonal fluctuations. An equivalent quantification of tea tastes showed that in summer and autumn teas, the bitterness and astringency were significantly elevated, whereas umami declined. Metabolite content comparisons and partial least-squares analysis suggested that several flavonoids and amino acids are mainly responsible for the seasonal variations in taste quality.

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Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

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