Multianalyte Serum Assays from Mid-IR Spectra of Dry Films on Glass Slides
Bibliographic record
Abstract
An analytical method based upon mid-infrared spectroscopy is proposed, and the advantages of this approach are discussed. The method involves drying a liquid specimen to a film, and deriving analyte levels from the infrared spectrum of that film. The specific aim of this study was to determine whether glass might serve as a suitable substrate for the simultaneous determination of several analytes in complex mixtures. Using human serum as a “proof-of-concept” example, we show here that six commonly measured analytes may be determined from spectra originally measured by employing barium fluoride substrates, but restricting the analytical models to absorptions within the region 2000–4000 cm −1 —i.e., making use of only those absorptions that are accessible with glass substrates. With the use of partial least-squares calibration models, it is shown that albumin, cholesterol, glucose, total protein, triglycerides, and urea may be determined with standard errors that approach or meet the criteria required for routine clinical analysis. The practical advantages of such an approach are discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".