Changes in Fungal Population and Aflatoxin Levels and Assessment of Major Aflatoxin Types in Stored Peanuts (Arachis hypogaea Linnaeus)
Bibliographic record
Abstract
Peanut kernels of Homabay Local, Valencia Red, ICGV-SM 12991 and ICGV-SM 99568 cultivars were stored for six months in jute, polypropylene and polyethylene bags to assess the effect of storage bags, temperature and R.H. on fungal population and aflatoxin contamination. Moisture content (M.C.), fungal population and aflatoxin levels were determined before storage and after every 30 days during storage. Isolates of Aspergillus flavus and A. parasiticus were assayed for production of aflatoxin B1, B2, G1 and G2. The correlation between MC, population of A. flavus and A. parasiticus and aflatoxin levels in peanuts was also determined. Six fungal pathogens were commonly isolated from the peanut samples and occurred as follows in decreasing order: Penicillium spp. (106.6 CFU/g), A. flavus L-strain (4.8 CFU/g), A. flavus S-strain (2.9 CFU/g), A. niger (2.6 CFU/g), A. parasiticus (1.7 CFU/g) and A. tamarii (0.2 CFU/g). The overall population of A. flavus L-strain was 66% higher than that of A. flavus S-strain. Ninety one percent of A. flavus and A. parasiticus isolates produced at least one of the four aflatoxin types assayed, with 36% producing aflatoxin B1. Total aflatoxin levels ranged from 0 - 47.8 µg/kg with samples stored in polyethylene and jute bags being the most and least contaminated, respectively. Eighty nine percent and 97% of the peanut samples met the EU (? 4 µg/kg) and Kenyan (? 10 µg/kg) regulatory standards for total aflatoxin, respectively. Peanuts should be adequately dried to safe moisture level and immediately packaged in a container - preferably jute bags - which will not promote critical increases in fungal population and aflatoxin contamination.
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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".