Molecular Detection of Mycotoxigenic Fungi in Foods: The Case for Using PCR-DGGE
Bibliographic record
Abstract
Among the toxin-producing microbes, those that produce mycotoxins are especially problematic due to their broad distribution in the environments and in foods. Several species of Aspergillus, Penicillium, and Fusarium are sources of potent mycotoxins such as aflatoxins, ochratoxins, patulin, deoxynivalenol, and fumonisins. It is, therefore, vital that mycotoxigenic fungi contaminants in food are rapidly and accurately identified for ensuring the safety of consumers. Most of the current methods are based on PCR using gene-specific or species-specific primers. However, contaminating microbes often compose a complex community and PCR-DGGE may provide a better approach than traditional single-gene and/or single-species based methods. It provides “fingerprints” for each microbial flora and has been widely used to analyze environmental and food-associated microbial communities. This review shows the advantages and disadvantages of different molecular methods for the detection of mycotoxigenic fungi including PCR-DGGE as a potent and applicable method that could overcome the difficulties associated with other methods.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".