Comparison of the discriminating power of Raman and surface‐enhanced Raman spectroscopy with established techniques for the examination of liquid and gel inks
Bibliographic record
Abstract
The ability to discriminate between inks is important for forensic document analysis. Here, Raman spectroscopy (RS) and surface‐enhanced RS have been compared to the traditional document examination techniques of video spectral comparison and thin layer chromatography on a population of blue and black‐coloured liquid and gel inks. It was found that in most cases, the Raman techniques provided a similar or better discriminating power than the conventional methods. Importantly, this study allowed us to determine whether the same underlying changes in composition were being exploited by the different methods to discriminate between samples. It was found that there was indeed a high degree of commonality in the sample pairs being discriminated by the various techniques. This work can therefore underpin introduction of Raman methods into standard operating procedures for ink analysis since it not only measures the extent of discrimination between samples but can also explain the origin of the spectral changes that are used to distinguish between them. © 2013 John Wiley & Sons Ltd and Crown copyright
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".