Pre-harvest treatment of spinach with Ascophyllum nodosum extract improves post-harvest storage and quality
Bibliographic record
Abstract
Fresh spinach (Spinacia oleracea L.) leaves contain high concentrations of beneficial phytochemicals. However, spinach does not store well and is highly perishable especially during handling and post-harvest storage. In this study, we investigated the effect of pre-harvest root-treatment of spinach with Ascophyllum nodosum extract (ANE) at different concentrations (0, 0.1, 1.0, or 5.0 g L−1) on post-harvest quality of fresh-cut spinach over a 35-day storage period. At the time of harvest there was no significant difference in dry weight, chlorophyll, ascorbate and lipid peroxidation between ANE-treated and non-treated plants. However, the loss in fresh weight and visual quality (color and turgor) of spinach leaves during storage was reduced by pre-harvest application of ANE. Lipid peroxidation was significantly reduced in ANE-treated leaves. The total chlorophyll content and ascorbate content in the control and treated leaves was however identical over the storage period and decreased at a similar rate. A negative correlation was observed between visual quality and lipid peroxidation. The results show that pre-harvest ANE application through root drench, especially at 1.0 g L−1, enhanced post-harvest storage quality of spinach leaves.
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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) | 0.002 | 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".