Changes in the Phenolic Acid Content and Antioxidant Activity During Kernel Development of Corn (<i>Zea mays</i> L.) and Relationship with Mycotoxin Contamination
Bibliographic record
Abstract
Corn grain production could be affected by several fungal pathogens responsible for the production of mycotoxins. The aims of this study were to determine the evolution of phenolic acids and total antioxidant activity (TAA) during kernel development and to evaluate their potential protective role in minimizing mycotoxin contamination in six corn genotypes (four open‐pollinated varieties and two hybrids) characterized by a wide array of kernel traits. TAA and free and cell wall‐bound phenolics showed significant differences among corn genotypes at different stages of development, with the highest values found at the beginning of kernel development. Ferulic, p‐coumaric, and caffeic acids were the main cell wall‐bound phenolic acids during kernel development, whereas chlorogenic acid was the main free phenolic acid. A significant negative correlation was observed between deoxynivalenol contamination at harvest maturity and free phenolic acids and TAA at the beginning of kernel development, whereas no significant correlation was observed with fumonisin contamination. In conclusion, free phenolic acids are evidently involved in the resistance mechanism toward deoxynivalenol contamination, whereas their role toward fumonisin contamination was not elucidated under field conditions, implying that components other than phenolic acids may be responsible for this latter type of resistance.
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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 | 0.001 | 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".