Molecularly imprinted polymers integrated with surface enhanced Raman spectroscopy: Innovative chemosensors in food science
Bibliographic record
Abstract
Determination of trace levels of compounds in agri‐foods are challenging due to the complexity of the agricultural and food matrices. A specific and complete separation and enrichment of the target compound is sometimes more important than the development of detection tools. Raman spectroscopy and its derivative, surface enhanced Raman spectroscopy (SERS), have been widely used for the detection of specific food components due to their unique ability to record “fingerprinting” features of each molecule. However, Raman spectroscopy/SERS records the spectral signatures of all the food components, demonstrating that a pre‐separation of the target compound is critical. Molecularly imprinted polymers (MIPs), defined as “artificial antibodies”, have been constructed and integrated with Raman spectroscopy/SERS for an accurate and reliable separation and detection of target compounds in agri‐foods with minimum interference from food matrices. Compared to other separation elements (e.g., antibody, aptamer etc.) that can be integrated with Raman spectroscopy/SERS for sensing, MIPs do not contribute to spectral signature, can be reused multiple times and are more resistant to environmental factors, demonstrating the potential to be used for in‐field and on‐line screening of food safety and quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".