Postharvest treatments with MnCl2 and ZnCl2 reduce enzymatic browning and enhance antioxidant accumulation in soya bean sprout
Bibliographic record
Abstract
Abstract Soya bean sprout is a nutrient-abundant vegetable. However, enzymatic browning of soya bean sprouts during storage remains a challenge. In this study, the effects of treatment with MnCl2or ZnCl2on the browning index, antioxidant nutrient accumulation, total antioxidant capacity and enzyme activities of phenylalanine ammonia-lyase (PAL), polyphenol oxidase (PPO), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) were investigated in soya bean sprouts after storage at 4 °C and 90% relative humidity for 0, 7, 14 and 21 days. The results showed that postharvest treatment with 1, 2 and 10 mM MnCl2or ZnCl2profoundly retarded enzymatic browning in soya bean sprouts to different extents. Compared with the control, the 10 mM MnCl2and ZnCl2treatments drastically enhanced ascorbic acid, total thiol and phenolic content, and enhanced FRAP (ferric-reducing ability of plasma) antioxidant capacity in stored soya bean sprouts. Moreover, the MnCl2and ZnCl2treatments enhanced SOD, CAT and PAL but decreased PPO and POD activities compared with the control. In addition, the Mn and Zn content in soya bean sprouts significantly increased, by approximately two- to threefold, compared with the control. This study provides a new method for improving the nutrient quality of soya bean sprouts based on postharvest Mn or Zn supplementation.
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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) | 0.001 | 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".