The effect of desulfurization on the postharvest quality and sulfite metabolism in pulp of sulfitated “Feizixiao” Litchi (<i>Litchi chinensis</i> Sonn.) fruits
Bibliographic record
Abstract
Abstract The residual sulfite caused by sulfur fumigation (SF) is a hazard to health and influenced the export trade of litchi. Desulfurization (DS) is a valid chemical method to reduce the residual sulfite. However, the effect of DS on fumigated litchi has not been studied at physiological and molecular level. This study was aimed to evaluate the effect of DS (SF plus 3% desulfurizer) on the postharvest quality, sulfite residue, and the sulfite metabolism in sulfitated “Feizixiao” litchi during the 4°C storage. Results indicated that the DS promoted the color recovery of sulfitated litchi and achieved an effect similar to SF on controlling rot and browning. DS recovered the water content and respiration rate of sulfitated litchi pericarp. Thus, DS improves commodity properties of sulfitated litchi. Moreover, DS greatly reduced sulfite residue especially in pulp and ensured the edible safety of sulfitated litchi. The activities of sulfite oxidase, sulfite reductase, serine acetyltransferase, and O ‐acetylserine(thiol) lyase in pulp increased after SF but fell down after DS while the expressions of their encoding genes decreased after SF but then rallied after DS. These results indicated the key role of these enzymes in sulfite metabolism after SF and DS changed the sulfite metabolism at both enzymatic and transcriptional level. It could be concluded that DS used in this study was an effective method for improving the color recovery and ensuring the edible safety of sulfitated litchi by not only chemical reaction but also both of enzymatic and transcriptional regulation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".