Label‐free optical detection of type II diabetes based on surface‐enhanced Raman spectroscopy and multivariate analysis
Bibliographic record
Abstract
Surface‐enhanced Raman scattering (SERS) spectroscopy was first employed to detect oxyhemoglobin (OxyHb, the common type of hemoglobin) variation in type II diabetic development without using exogenous reagents. Using silver nanoparticles as SERS‐active substrate, high‐quality SERS spectra are obtained from blood OxyHb samples of 49 diabetic patients and 40 healthy volunteers. Tentative assignment of the observed SERS bands indicates specific structural changes of OxyHb molecule in diabetes, including heme transformation and globin variation. Furthermore, partial least squares and principal component analysis combined with linear discriminate analysis diagnostic algorithms are employed to analyze and classify the SERS spectra acquired from diabetic and healthy OxyHb, yielding the diagnostic accuracies of 90.0% and 95.5%, respectively. This exploratory work suggests that the silver nanoparticles‐based OxyHb SERS method in combination with multivariate statistical analysis has great potential for the label‐free detection of type II diabetes. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".