Individual and Combined Occurrence of Mycotoxins in Feed Ingredients and Complete Feeds in China
Bibliographic record
Abstract
The objective of this study was to investigate the individual and combined contamination of aflatoxin B₁ (AFB₁), zearalenone (ZEN) and deoxynivalenol (DON) in feedstuffs from different Provinces of China between 2016 and 2017. A total of 1569 samples, including 742 feed ingredients and 827 complete pig feed samples, were collected from various regions of China for mycotoxins analysis. The results showed that individual occurrence rates of AFB₁, ZEN, and DON were more than 83.3%, 88%, and 74.5%, respectively, in all the tested samples. DON was the most prevalent contaminant, followed by ZEN and AFB₁, with the average concentrations ranging from 450.0-4381.5 μg/kg, 2.3-729.2 μg/kg, and 1.3-10.0 μg/kg, respectively. Notable, 38.2%, 10.8%, and 0.6% of complete pig feeds were contaminated with DON, ZEN, and AFB₁ over China's regulatory limits, respectively. Moreover, over 75.0% analyzed samples were co-contaminated with two or three mycotoxins. In conclusion, the current study revealed that the feedstuffs in China were severely contaminated with DON, followed by ZEN and AFB₁ during the past two years. These findings highlight the importance of monitoring mycotoxins in livestock feed and implementing feed management and bioremediation strategies to reduce mycotoxin exposure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".