Analysis of metabolomics associated with quality differences between room‐temperature‐ and low‐temperature‐stored litchi pulps
Bibliographic record
Abstract
Studies on how temperature affects the postharvest quality of litchi have focused mainly on pericarp browning but rarely on the metabolites in postharvest litchi pulp. In this study, the differences in respiration rates, total soluble solid content, and titratable acid content demonstrated that room and low temperatures have different effects on the quality of "Feizixiao" litchi pulp. UHPLC-ESI-QTOF-MS/MS analysis was performed to compare the differentially expressed metabolites (DEMs) in litchi pulp after 8 days of storage at room temperature (RT-8 d) with those in litchi pulp after 28 days of storage at low temperature (LT-28 d). Nineteen carbohydrates (phosphohexoses, sorbitol, and mannose), fifteen acids, seven amino acids, nine energy metabolites and nucleotides, and six aliphatic and secondary metabolites were identified as common DEMs in RT-8 d and LT-28 d pulps. These findings indicated active fructose and mannose metabolism and increased catabolism of nicotinate, nicotinamide, alanine, aspartate, and glutamate. Four carbohydrates (mainly phosphohexoses), five acids, ten amino acids, three aliphatic and secondary metabolites, and one hormone were identified as unique DEMs in RT-8 d pulp, the consumption of key metabolites in glycolysis and the tricarboxylic acid cycle, and accumulation of phenylalanine, tyrosine, and tryptophan. Active consumption of nucleotide metabolites and biosynthesis of aliphatics in LT-28 d pulp were indicated by unique DEMs (eleven carbohydrates, four acids, seven amino acids, seven energy metabolites and nucleotides, and six aliphatic and secondary metabolites). These results provided an unambiguous metabolic fingerprint, thereby revealing how room and low temperatures differentially influenced the quality of litchi pulp.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".