Detection and imaging of thermochromic ink compounds in erasable pens using desorption electrospray ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: Thermochromic ink pens are widely accessible worldwide and have gained popularity among the general public. These pens are very useful to undo mistakes while writing important documents or exams. They are also, however, misused in committing crimes such as counterfeiting checks or wills. Thus, the forensics community is in need of techniques that will allow these forgeries to be detected rapidly, reliably and conveniently. METHODS: Thermochromic ink compounds were investigated using Desorption Electrospray Ionization (DESI) coupled with an LTQ mass spectrometer and Thin-Layer Chromatography (TLC). Tandem mass spectrometric analysis was conducted using Electrospray Ionization (ESI) coupled with an Orbitrap LTQ mass spectrometer performing Collision-Induced Dissociation (CID) for identification of ink traces. RESULTS: Chemical marker ions characteristic of the state of ink (visible or invisible) were identified and mapped in ink traces by the use of DESI-MS imaging. These ions can be employed by forensic experts as fingerprint markers in forged documents. The marker ions were also characterised by conducting tandem mass spectrometry using paper spray in an Orbitrap LTQ mass spectrometer. CONCLUSIONS: Specific chemical components yielding ions of m/z 400, 405, 615 and 786 were distinguished as only being apparent in the invisible and reappeared state of the ink. The absence of these compounds in the original state of the ink enabled their recognition as useful chemical determinants in detecting forgery. DESI-MSI was thus shown to be a very useful, convenient and reliable technique for detecting forgery in paper documents due to its fast and reproducible mode of analysis, with no sample preparation and minimal damage to the document under investigation. Copyright © 2017 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".