Low-Temperature Regulates the Cell Structure and Chlorophyll in Addition to Cellulose Metabolism of Postharvest Red Toona sinensis Buds across Different Seasons
Bibliographic record
Abstract
buds significantly affect their quality during storage. This study aimed to clarify the effects of low-temperature storage on postharvest red TSB quality harvested in different seasons. Red TSB samples were collected from Guizhou province, China, 21 days after the beginning of spring (Lichun), summer (Lixia), and autumn (Liqiu), and stored at 4 °C in dark conditions. We compared and analyzed the appearance, microstructure, chlorophyll and cellulose content, and expression levels of related genes across different seasons. The results indicated that TSB harvested in spring had a bright, purple-red color, whereas those harvested in summer and autumn were green. All samples lost water and darkened after 1 day of storage. Severe greening occurred in spring-harvested TSB within 3 days, a phenomenon not observed in summer and autumn samples. Microstructural analysis revealed that the cells in the palisade and spongy tissues of spring and autumn TSB settled closely during storage, while summer TSB cells remained loosely aligned. Xylem cells were smallest in spring-harvested TSB and largest in autumn. Prolonged storage led to thickening of the secondary cell walls and pith cell autolysis in the petioles, enlarging the cavity area. Chlorophyll content was higher in leaves than in petioles, while cellulose content was lower in petioles across all seasons. Both chlorophyll and cellulose content increased with storage time. Gene expression analysis showed season-dependent variations and significant increases in the expression of over half of the chlorophyll-related and cellulose-related genes during refrigeration, correlating with the observed changes in chlorophyll and cellulose content. This research provides valuable insights for improving postharvest storage and freshness preservation strategies for red TSB across different seasons.
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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".