Current PCR-based methods for the detection of mycotoxigenic fungi in complex food and feed matrices
Bibliographic record
Abstract
Mycotoxins are toxic secondary fungal metabolites produced by certain types of filamentous fungi, such asAspergillus, Fusarium, andPenicillium spp. Mycotoxigenic fungi and their produced mycotoxins are considered to be an important issue in food and feed safety due to their toxic effects like carcinogenicity, immunosuppression, neurotoxicity, nephrotoxicity, and hepatotoxicity on humans and animals. To boost the safety level of food and feedstuff, detection and identification of toxins are essential at critical control points across food and feed chains. Zero-tolerance policies by the European Union and other organizations about the extreme low level of tolerance of mycotoxins contamination in food and feed matrices have led to an increasing interest to design more sensitive, specific, rapid, cost-effective, and safer to use mycotoxigenic fungi detection technologies. Hence, many mycotoxigenic fungi detection technologies have been applied to measure and control toxins contamination in food and feed substrates. PCR-based mycotoxigenic fungi detection technologies, such as conventional PCR, real-time PCR, nested PCR, reverse transcriptase (RT)-PCR, loop-mediated isothermal amplification (LAMP),in situ PCR, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR DGGE), co-operational PCR, multiplex PCR, DNA arrays, magnetic capture-hybridization (MCH)-PCR and restriction fragment length polymorphism (RFLP), would contribute to our understanding about different mycotoxigenic fungi detection approaches and will enhance our capability about mycotoxigenic fungi identification, isolation and characterization at critical control points across food and feed chains. We have assessed the principles, results, the limit of detection, and application of these PCR-based detection technologies to alleviate mycotoxins contamination problem in complex food and feed substrates. The potential application of these detection technologies can reduce mycotoxins in complex food and feed matrices.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".