Mycotoxins in fuel ethanol co‐products derived from maize: a mass balance for deoxynivalenol
Bibliographic record
Abstract
Abstract BACKGROUND: Three matrices—corn (maize) meal, distiller's dried grains with solubles (DDGS) and condensed distiller's solubles (CDS)—were sampled in sequence from a continuous dry‐milling process plant for the determination of mass balance of deoxynivalenol (DON). Four commercially available enzyme‐linked immunosorbent assay (ELISA) kits were evaluated for their ability to measure the presence of DON. Liquid chromatography/tandem mass spectrometry (LC/MS/MS) was used as standard method to detect DON and other Fusarium toxins. RESULTS: The concentrations of DON in DDGS and especially CDS were overestimated or underestimated by ELISA. However, for both matrices, all ELISA methods were not significantly different in their mean results from the LC/MS/MS standard, although the variability in results was much higher. DON concentrations in the CDS and the final DDGS co‐product were significantly higher ( P ≤ 0.01) than in the starting material (corn grain). Toxin concentration increased by a factor of 3 on a dry weight basis in DDGS compared with the starting corn and by a factor of 4 in CDS. Mean concentration of DON in CDS was four times higher (7.11 mg kg −1 ) than in corn grains (1.80 mg kg −1 ) and 1.4 times higher than in DDGS (5.24 mg kg −1 ). Mass balance calculations showed that CDS was the main source of contamination of DON, comprising ca 70% of the toxin found in the final product (DDGS). Most DON (87%) was accounted for by this analysis. CONCLUSION: Concentrations in the grain corn entering ethanol plants should be close to the dietary values recommended for swine in Canada and the USA for DON (1 mg kg −1 ). Small amounts of acetyldeoxynivalenol and DON glucoside were also found in the three matrices along with a small amount of zearalenone. Unlike the situation for DON, the DON glucoside was not concentrated into DDGS and CDS, indicating that it was hydrolysed during the fermentation process. Copyright © 2009 Society of Chemical Industry
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".