Receiver operating characteristics analysis of Surface Enhanced Raman Spectroscopy (SERS) sensors
Bibliographic record
Abstract
Inkjet-printed surface enhanced Raman spectroscopy (SERS) sensors are fabricated on cellulose based paper or fabric substrates. These flexible sensors provides basic point-of-sampling advantages that is particularly useful in field applications. Due to the heterogeneous loading of nanoparticles on the substrate, SERS intensities inevitably vary across the active area of the printed sensor. This paper will discuss the use of receiver operating characteristics (ROC) for the analysis of inkjet-printed SERS sensors. The aim is to provide an alternative measurand to the SERS enhancement factor that can be used to compare different types of SERS substrates. We have developed statistical analysis from multiple data sets obtained from sensors exposed to both analyte and control to determine the probability of positive detection (PD) at various analyte concentration. This dependence describes the ROC of the sensor and also provides confidence level associated with a given detection limit. We propose this methodology for the evaluation of SERS sensors to enable their field applications.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".