Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy
Bibliographic record
Abstract
We present a rapid, portable optical system for label-free detection of COVID-19. Raman spectra from an entire liquid drop of saliva supernatant can be obtained within 6 minutes, and the sample is classified as COVID-19 positive or negative using artificial intelligence (AI). 293 COVID negative and 49 COVID positive saliva supernatant samples were analyzed. Positive samples were from hospitalized patients (non-critical and critical) and non-hospitalized testing clinic volunteers (symptomatic and asymptomatic). Our Raman/AI system has an 82% accuracy detecting people with COVID-19 of any severity with any symptom presentation, and 89% accuracy when detecting COVID-19 in hospitalized patients alone. Rapid label-free analysis of biofluids for viruses could provide a low-cost screening solution that could be adapted to respond to viral mutations. This could be invaluable for future pandemics and for reducing infections in hospitals, care homes and workplaces.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".