The Effect of SO2 Fumigation, Acid Dipping, and SO2 Combined with Acid Dipping on Metabolite Profile of ‘Heiye’ Litchi (Litchi chinensis Sonn.) Pericarp
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Bibliographic record
Abstract
Sulfur fumigation (SF), acid dipping (HCl treatment, HAT), and their combination (SF+HAT) are common methods for long-term preservation and color protection of litchi. However, their effects on the metabolic profile of the litchi pericarp have not been investigated. SF resulted in a yellowish-green pericarp by up-regulating lightness (L*), b*, C*, and h° but down-regulating total anthocyanin content (TAC) and a*, while HAT resulted in a reddish coloration by up-regulating a*, b*, and C* but down-regulating L*, h°, and TAC. SF+HAT recovered reddish color with similar L*, C* to SF but a*, b*, h°, and TAC between SF and HAT. Differential accumulated metabolites (DAMs) detected in HAT (vs. control) were more than those in SF (vs. control), but similar to those in SF+HAT (vs. control). SF specifically down-regulated the content of cyanidin-3-O-rutinoside, sinapinaldehyde, salicylic acid, and tyrosol, but up-regulated 6 flavonoids (luteolin, kaempferol-3-O-(6″-malonyl)galactoside, hesperetin-7-O-glucoside, etc.). Five pathways (biosynthesis of phenylpropanoids, flavonoid biosynthesis, biosynthesis of secondary metabolites, glutathione metabolism, and cysteine and methionine metabolism) were commonly enriched among the three treatments, which significantly up-regulated sulfur-containing metabolites (mainly glutathione, methionine, and homocystine) and down-regulated substrates for browning (mainly procyanidin B2, C1, and coniferyl alcohol). These results provide metabolic evidence for the effect of three treatments on coloration and storability of litchi.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it