Postharvest Application of Organic and Inorganic Salts To Control Potato (<i>Solanum tuberosum</i>L.) Storage Soft Rot: Plant Tissue–Salt Physicochemical Interactions
Bibliographic record
Abstract
Soft rot caused by Pectobacterium sp. is a devastating disease affecting stored potato tubers, and there is a lack of effective means of controlling this disease. In this study, 21 organic and inorganic salts were tested for their ability to control soft rot in potato tubers. In the preventive treatment, significant control of soft rot was observed with AlCl3 (≥66%) and Na2S2O3 (≥57%) and to a lesser extent with Al lactate and Na benzoate (≥34%) and K sorbate and Na propionate (≥27%). However, only a moderate control was achieved by curative treatment with AlCl3 and Na2S2O3 (42%) and sodium benzoate (≥33%). Overall, the in vitro inhibitory activity of salts was attenuated in the presence of plant tissue (in vivo) to different degrees. The inhibitory action of the salts in the preventive treatment, whether effective or otherwise, showed an inverse linear relationship with water ionization capacity (pK') of the salt ions, whereas in the curative treatment, only the effective salts showed this inverse linear relationship. Salt-plant tissue interactions appear to play a central role in the attenuated inhibitory activity of salts in potato tuber through reduction in the availability of the inhibitory ions for salt-bacteria interactions. This study demonstrates that AlCl3, Na2S2O3, and Na benzoate have potential in controlling potato tuber soft rot and provides a general basis for understanding of specific salt-tissue interactions.
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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) | 0.001 | 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".