Effects of Foliar Application of Elicitors on Red Clover Isoflavone Content
Bibliographic record
Abstract
Abstract Red clover (Trifolium pratense L.) contains high concentrations of isoflavones, compounds that have received much interest lately due to their presumed benefits for human health. In this experiment we tested the possibility to induce isoflavone production in the foliage of two greenhouse‐grown red clover cultivars (‘Azur’ and ‘Start’) through the application of elicitor compounds. Foliar applications of different concentrations of acetic acid (50, 100, 250 and 500 mm), yeast extract (1, 2, 3 and 4 g l−1), and chitosan (125, 250, 500, and 1000 mg l−1) were carried out on plants at the late vegetative stage, which were harvested 2 or 8 days after spraying. Concentrations of genistein, daidzein, formononetin and biochanin A were determined by high performance liquid chromatography. The two cultivars differed in isoflavone concentrations, ‘Azur’ having on average 36 % higher biochanin A, formononetin and total isoflavone concentrations than ‘Start’ (P < 0.05). A cultivar × sampling date interaction (P < 0.1) reflected a 20 % increase over time in total isoflavone concentration with ‘Azur’, which was not observed with ‘Start’. Effects of elicitors were limited, contrasts indicating overall, 12, 14 and 15 % greater total isoflavone concentration in yeast extract (P < 0.1), chitosan (P < 0.05) and acetic acid (P < 0.05)‐treated plants, respectively, than in untreated control plants. There were few differences between the various elicitors and none between concentrations of each elicitor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".