Neoteric approach for peanuts biofilm using the merits of Moringa extracts to control aflatoxin contamination
Bibliographic record
Abstract
Aflatoxigenic fungi and aflatoxins are still a principal challenge that threatened peanut production, marketing, and handling. This study aimed to face the problem using bioactive materials, which reduce fungi and mycotoxin contamination, Moringa extracts may be suitable for solving this challenge. Also, the study was compared the extracts of leaves and oil-free seeds. Fresh leaves and seeds were collected, dried, and milled, while oil was collected by cold pressing. The extracts were evaluated for total phenols, flavonoids, and antioxidants, the oil contents of fatty acids, tocopherol, and sterols were determined. An emulsion for protecting peanuts compositing of leaves extract carried by Moringa oil, and commercial emulsifier. Leaves extract evaluation reflected distinct properties of its fibers, total phenols, and flavonoids. It was recorded a microbial inhibition of bacteria and fungi. The values for both minimal inhibition and fungicidal concentrations were recorded at 3.2 mg/mL and 490 μg/L, respectively. For oil, it showed a unique content, as oleic acid was the main fatty acid, with an affinity between palmitic and behenic in their ratios. Also, oil was recorded by high contents of alpha-tocopherol and Δ7-Campesterol, with 1.166 mg/kg oil as total sterols content. The leaves extract has also a unique capacity to inhibit toxigenic fungi. By applying the composite emulsion for peanut coating, results expressed a high CFU-count inhibition when it was inoculated by A. flavus strain compared to the control.
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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) | 0.001 | 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".