Current Immunoassay Methods for the Rapid Detection of Aflatoxin in Milk and Dairy Products
Bibliographic record
Abstract
Abstract The presence of mycotoxins in foodstuff causes serious health problems to consumers and economically affects the food industry. Among the mycotoxins, aflatoxins are very toxic and highly carcinogenic contaminants which affect the safety of many foods, and therefore endanger human health. Aflatoxin M 1 (AFM 1 ) found in milk results from the biotransformation of aflatoxin B 1 . Many efforts have been made to control the source of AFM 1 from farmers to dairy product companies. However, AFM 1 escapes ordinary methods of food treatment such as cooking, sterilization, and freezing, hence it appears in milk and dairy products. The presence of high levels of AFM 1 constitutes an alarming threat as milk and dairy products contain essential nutrients for human health, especially for infants and children. For this reason, there is a pressing need for developing a fast and reliable screening method for detecting trace aflatoxins in food. Several analytical methods based on high‐performance liquid chromatography (HPLC) and mass spectroscopy have been used for aflatoxin detection; however, they are expensive, time‐consuming, and require many skills. Recently, immunoassay methods, including enzyme‐linked immunosorbent assay (ELISA), immunosensors, and lateral flow immunoassay (LFIA), have been preferred for food analysis because of their improved qualities such as high sensitivity, simplicity, and capability of onsite monitoring. This paper reviews the new developments and applications of immunoassays for the rapid detection of AFM 1 in milk.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".