Nanomaterial‐Based Biosensors for Aflatoxin B1 Detection: Current Advances, Challenges, and Prospects
Bibliographic record
Abstract
Aflatoxins (AFTs) are mycotoxins recognized as the key contributors to environmental and food contamination. AFTs can contaminate food and raw materials at different phases of manufacturing, transformation, processing, distribution, and marketing. Aflatoxin B1 (AFB1) is the most harmful one among AFTs, and it exhibits potent toxic and carcinogenic effects in animals and humans. Currently, the efficient, fast, and sensitive methods for the early detection and monitoring of AFB1 in processed food and raw materials are urgently required. Nanomaterials, which possess unique physical, chemical, and optical properties, have gained increased traction in the manufacturing and improvement of biosensors for the detection sensitivity to AFB1. Herein, a comprehensive review of the development of nanomaterials-based biosensors for the detection of AFB1 in food and raw materials and their potential applications is provided. Additionally, the sensitivity, selectivity, and effectiveness in the detection of real samples are discussed. The biosensors aimed at AFB1 screening are highlighted using novel nanomaterials such as noble metal nanomaterials, quantum dots, and carbon-based nanomaterials. This review provides new information and insightful commentary, representing an important starting point for the future development and innovation of further innovative detection methods for AFB1 in food and raw materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".