Ascophyllm nodosum Extracts Improve Shelf Life and Nutritional Quality of Spinach (Spinacia oleracea L.)
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Bibliographic record
Abstract
In order to develop an environmentally friendly seaweed extract treatment which will benefit both pre- and post-harvest qualities of vegetables, the effects of pre-harvest application of the brown algae Ascophyllum nodosum extracts on the nutritional quality and post-harvest storability of spinach (Spinacia oleracea L.) was investigated. Plants treated with A. nodosum extracts accumulated higher concentrations of iron, potassium, total soluble protein, and total phenolics as compared to untreated controls. 1H NMR and LC-MS analysis revealed a roughly 50% enhanced accumulation of the 9 flavonoids identified, which is partially confirmed by the elevated chalcone isomerase activity. A. nodosum extract treatment caused an increase in transcription of the genes related to plant growth, osmolyte accumulation, and antioxidative activities. Post-harvest analysis revealed that A. nodosum extract treatment caused an enhanced storability of spinach leaves in terms of visual quality, weight loss, and senescence. Lipid peroxidation and ascorbate content were correlated with visual quality during storage. Animal experiments using the Caenorhabditis elegans nematode model revealed that spinach extracts prolonged the life span of C. elegans, and A. nodosum extract-enhanced polyphenols exerted improved beneficial effects in C. elegans against oxidative and heat stresses. Taken together, the results suggest that A. nodosum extracts enhance both pre- and post-harvest quality of spinach through stimulation of flavonoid pathways, thus leading to accumulation of flavonoids and promotion of anti-radical capacity in spinach leaves, which may protect the plant tissue against reactive oxygen species and subsequent decay. Furthermore, the increased flavonoid content in spinach exerted beneficial effects in C. elegans against oxidative and heat stresses via different mechanisms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it