Influence of traditional dehulling on mycotoxin reduction and GC-HRTOF-MS metabolites profile of fermented maize products
Bibliographic record
Abstract
Contamination with mycotoxins has been a worldwide food safety concern for several decades, and food processing has been suggested as a potential method to mitigate their presence. In this study, the influence of traditional dehulling (TD) on the mycotoxin reduction and metabolites profile of fermented white maize products obtained via natural and three controlled fermentation methods (involving Lactobacillus fermentum, Lactobacillus plantarum, and their mixed cultures) was examined. Gas chromatography coupled with high resolution time-of-flight mass spectrometry (GC-HRTOF-MS) and ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) were employed. TD brought the levels of fumonisin B1 (FB1) and B2 (FB2) in the white maize below the regulatory limit set by the European Union (EU) for maize consumed by humans. While TD increased the concentration of several mycotoxins in the fermented maize products obtained from other studied fermentation methods, it primarily reduced aflatoxin B1 (AFB1), FB1, deoxynivalenol, and 15-acetyldeoxynivalenol in the L. plantarum-fermented products. By tempering the dehulled maize, a solid-state fermentation process began. This was used in TD to make it easier to remove the pericarp. GC-HR-TOF-MS metabolomics revealed that TD brought about the generation of 12 additional compounds in the dehulled maize though some metabolites in the whole maize were lost/biotransformed. The fermented dehulled maize products obtained from the four studied fermentation procedures contained fewer compounds than the fermented whole maize products. Overall, the analysis showed that all fermented maize (whole and dehulled) produced had varied nutritional metabolites and mycotoxin concentrations below the EU maximum level, except for fermented maize obtained from mixed strains (AFB1 + AFB2 > 4.0 g/kg).
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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 | 0.001 |
| 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".