Ballpoint Pen Inks: Characterization by Positive and Negative Ion-Electrospray Ionization Mass Spectrometry for the Forensic Examination of Writing Inks
Bibliographic record
Abstract
A method based on profiling of dye components by electrospray ionization mass spectrometry (ESI/MS) is described for the characterization of ballpoint pen inks. The method involves benzyl alcohol (30 microL) extraction of ink from paper. The extracts of ink lines 1 and 5 mm in length are used for direct ESI/MS analysis in positive and negative modes, respectively. The instrumental analysis takes 3 min. Basic and acid dyes in the inks are detected in the positive and negative modes, respectively, with each dye yielding one or two characteristic ion peaks. The mass spectrum, which is mainly a compositional signature of the dyes in the ink, was not affected by the type of paper from which the ink was extracted, or by natural ageing of the ink on document in the absence of light. However, exposure to fluorescent illumination caused dealkylation of polyalkylated basic dyes and resulted in changes in the homologous distribution of the dyes. In this study, a total of 44 blue inks, 23 black inks, and 10 red inks have been analyzed, and the mass spectra were used to establish a searchable library. ESI/MS analysis provides a simple and fast way to compare ink specimens and in combination with on-line library search permits rapid screening of inks for forensic document investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".