Postharvest Treatment of ‘Florida Prince’ Peaches with a Calcium Nanoparticle–Ascorbic Acid Mixture during Cold Storage and Its Effect on Antioxidant Enzyme Activities
Bibliographic record
Abstract
Chilling injury (CI) is a physiological disorder resulting from low storage temperatures that affects the fruit quality and marketing of the ‘Florida Prince’ peach. In this study, the exogenous application of a mixture of calcium nanoparticles (CaNPs) and ascorbic acid was found to significantly alleviate the symptoms of CI in peaches during cold storage. Fruits were treated with CaNPs plus different concentrations of ascorbic acid (AA; 0, 3, 6, and 9 mM). Peaches were immersed in CaNP–AA for 15 min before being stored at 4 ± 1 °C and 95 ± 1% RH for 30 days. We observed that the 9 mM CaNP–AA treatment lowered the values for the CI index, ion leakage, and malondialdehyde (MDA) content and increased antioxidant enzyme activities (AEAs), such as for ascorbate oxidase (APX), catalase (CAT), superoxide dismutase (SOD), and glutathione reductase (GR). Furthermore, the treatment reduced the accumulation of both H2O2 and O2•− and increased the level of DPPH reduction throughout the duration of cold storage. Our results suggest that 9 mM CaNP–AA treatment suppresses the incidence of CI in peach fruit throughout cold storage, possibly because 9 mM CaNP–AA is at least partly involved in enhancing the antioxidant system via its effect on antioxidant substances. The results indicate that applying the 9 mM CaNP–AA treatment afforded peaches with enhanced tolerance against cold storage stress.
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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 | 0.001 |
| 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".