Mycotoxin reduction and metabolite profiles of ogi produced using traditional fermentation methods
Bibliographic record
Abstract
Mycotoxins are widely present in maize, a favourite staple food in sub-Saharan Africa. Food processing methods, like fermentation, have been suggested as potential ways to reduce mycotoxin contamination levels in the grain and, as a result, limit the exposure of crop consumers to the harmful effects of the toxins. The influence of four traditional fermentation processes [cold (with changed steeping liquor (CSL) and unchanged steeping liquor (USL), Fon and Goun procedures] on the mycotoxin reduction and metabolites profile of ogi, a fermented maize product, was studied. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and gas chromatography linked to high resolution time-of-flight mass spectrometry (GC-HR-TOF-MS) were respectively employed for the mycotoxin and metabolite profiles analyses of the samples. Among the nine mycotoxins detected in the raw maize samples, aflatoxin B1 (AFB1) and fumonisin B1 (FB1) + fumonisin B2 (FB2) concentrations were found to exceed the European Union (EU) maximum limit. Both cold (containing USL and CSL) and Goun fermentation techniques were able to lower the AFB1 concentration below this threshold. The metabolomics result revealed that ogi produced using the cold (USL) and Fon fermentation processes had the highest number of most of the detected important compounds, whereas the Goun fermentation process produced the fewest compounds in total. There was no statistically significant difference in the ability of the specified natural fermentation processes to lower FB1, FB2, FB3, deoxynivalenol (DON), sterigmatocystin (STERIG), and zearalenone concentrations in maize (ZEN). In addition, the results demonstrated that the four natural fermentation processes evaluated had varying effects.
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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 | 0.001 | 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 | 0.000 | 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".