Influence of Drought and Wounding Stress on Soluble Phenols and Proteins in Potato Tubers
Bibliographic record
Abstract
Potato is a valuable source for phytochemicals like vitamins, minerals, plant phenols, amino acids and proteins. However, environmental stress may affect the level of all these components. In this study, two purple breeding clones and one yellow fleshed cultivar (cv.) were assessed in their response to drought and wounding stress in two consecutive years. The plants were grown in the greenhouse under control (sufficient water supply) and with drought stress conditions. After harvest the tubers were analyzed for the content of soluble phenols and proteins in fresh tissue and after wounding. In addition, free amino acids (AAS) and activity of lipid acyl hydrolases (LAH) were assayed in control and drought stressed tubers. The results revealed significant differences in soluble phenols and proteins between genotypes, and that drought stress significantly increased the level of soluble proteins (P < 0.0001) and LAH activity (P < 0.001), but had no significant effect on the concentration of phenols. Moreover, total amounts of free AAS were higher in the drought stress variant. Wounding stress caused a significant increase of soluble phenols in cv. Agave. But, this was less prominent in purple clones which in general had higher contents of phenols. Proteins were also enhanced as a result of wounding, although, the effect of wounding stress on their level was smaller than that of drought 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 | 0.000 |
| 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 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".