Exploring the potential of applying infrared vibrational (micro)spectroscopy in ergot alkaloids determination: Techniques, current status, and challenges
Bibliographic record
Abstract
Ergot alkaloids (EA) are toxins produced mainly by Claviceps fungi and are considered as one of the most important groups of mycotoxins. Rapid and reliable detection techniques are urgently required by producers, importers and market regulators. As a promising alternative to conventional wet chemistry, infrared (IR)-based techniques are non-destructive, rapid and cost-effective. However, very limited studies on the qualitative or quantitative analysis of ergot or EA in food or feed based on IR vibrational spectroscopy have been reported so far. Being a secondary technique, the accuracy of IR method heavily depends on the robustness of chemometrics models. This paper aims to offer a brief overview of the EA issue in food and feed, conventional detection methods, theoretical principles of IR-based techniques and commonly used chemometrics for spectral data processing. In addition, the current application status of IR spectroscopy in ergot research is also considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".