Effect of Temperature and Solvent Composition on Extraction of Fumonisins B1 and B2 from Corn Products
Bibliographic record
Abstract
Fumonisins B1 and B2 were extracted from naturally contaminated corn products by using different extraction solvent compositions (methanol-water, acetonitrile-methanol-water, ethanol-water, and 100% water) and a range of temperatures from ambient to 150 degrees C. Ground samples of several corn products and 1 rice sample were mixed with an adsorbent material (Hydromatrix), and the fumonisins were extracted in 2 sequential 5 min static extractions at various temperatures. The combined extracts were cleaned up and analyzed by reversed-phase liquid chromatography with fluorescence detection after o-phthaldialdehyde-mercaptoethanol derivatization. The results showed a clear influence of temperature and solvent composition on recovery of fumonisins from some matrixes. With acetonitrile-methanol-water (1 + 1 + 2) the quantity of fumonisins extracted from naturally contaminated taco shells almost tripled in going from 23 degrees to 80 degrees C, and increased by another 30% when ethanol-water (3 + 7) was used as extraction solvent at 80 degrees C. Similar results were obtained with nacho chips. These effects were less pronounced with cornmeal, and small differences due to temperature and solvent composition were observed for corn flakes and rice. The ethanol-water extraction solvent combinations were specifically evaluated in an effort to use the cheapest, least toxic, and most environmentally friendly solvents for organic residue analysis. At 80 degrees C, ethanol-water combinations performed equally or better than methanol-water (8 + 2) or acetonitrile-methanol-water (1 + 1 + 2), combinations which are commonly used for fumonisin extractions. Even 100% water was successful for extracting fumonisins from the products, except for rice. However, increased amounts of water created technical problems and required an increased amount of Hydromatrix in the samples prior to extraction.
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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 | 0.000 | 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) | 0.000 | 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".