Determination of α-Tocopherol in Vegetable Oils Using a Molecularly Imprinted Polymers–Surface-Enhanced Raman Spectroscopic Biosensor
Bibliographic record
Abstract
We report the development of a novel hybrid "capture-detection" molecularly imprinted polymers-surface-enhanced Raman spectroscopic (MIPs-SERS) biosensor for the detection and quantification of α-tocopherol (α-Toc) in vegetable oils. α-Toc served as the template for MIPs synthesis. Methacrylic acid formed as the functional monomer. Ethylene glycol dimethacrylate was the cross-linking agent, and 2,2'-azobisisobutyronitrile was used as the initiator. The synthesized MIPs functioned to rapidly and selectively adsorb and separate α-Toc from oil components. We validated a dendritic silver nanostructure synthesized by a displacement reaction to be a suitable SERS substrate for the enhancement of Raman signals. Second-derivative transformations and chemometric models based upon SERS spectral features confirmed the possibility of a rapid and precise detection and quantification of different spiking levels of α-Toc in four different sources of vegetable oils (Mahalanobis distance from 15.93 to 34.01 for PCA model; R > 0.92, RMSE < 0.41 for PLSR model). The MIPs-SERS biosensor had a high sensitivity as well as a good recovery for α-Toc analysis in vegetable oils. The entire analysis required 15 min or less to complete with limited sample preparation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".