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Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination.

Johan Kjeldbjerg Lassen1, Robert Bradshaw2, Palle Villesen1,3

  • 1Bioinformatics Research Center, Aarhus University, Universitetsbyen 81, 3. Building 1872, DK-8000 Aarhus, Denmark.

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|July 14, 2023
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Summary

Matrix-Assisted Laser Desorption Ionisation-Mass Spectrometry Imaging (MALDI-MSI) can identify unique ink compositions in questioned documents. This technique, combined with machine learning, accurately differentiates between gel pens over time, enhancing forensic document analysis.

Keywords:
MALDIdocumentsinkmachine learning

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Questioned document examination is crucial for forgery detection.
  • Spectroscopy methods are the standard for document analysis.
  • Matrix-Assisted Laser Desorption Ionisation-Mass Spectrometry Imaging (MALDI-MSI) is a powerful tool for analyzing biological traces.

Purpose of the Study:

  • To explore the application of MALDI-MSI in questioned document examination.
  • To differentiate between seven gel pens based on ink composition using MALDI-MSI and chemometrics.
  • To assess the stability and classification robustness of ink analysis over 44 days.

Main Methods:

  • Utilized Matrix-Assisted Laser Desorption Ionisation-Mass Spectrometry Imaging (MALDI-MSI) for ink chemical composition detection and imaging.
  • Employed chemometric approaches, including elastic net logistic regression, for data modeling and classification.
  • Validated the classification model using blind signature analysis and machine learning cross-validation.

Main Results:

  • MALDI-MSI successfully detected and imaged the chemical composition of gel pen inks.
  • The combined MALDI-MSI and chemometric approach achieved 100% accuracy in classifying different pens.
  • The classification model demonstrated robustness in differentiating inks over a 44-day period.

Conclusions:

  • The coupling of MALDI-MSI with machine learning provides a robust method for ink discrimination in questioned documents.
  • This technique shows significant potential for expanding forensic versatility in document analysis.
  • Further research is warranted to investigate confounding factors like paper types and environmental conditions.