Statistical modelling investigation of MALDI-MSI-Based approaches for document examination.

KJELDBJERG LASSEN, Johan, BRADSHAW, Robert, VILLESEN, Palle and FRANCESE, Simona (2023). Statistical modelling investigation of MALDI-MSI-Based approaches for document examination. Molecules (Basel, Switzerland), 28 (13): 5207.

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Abstract

Questioned document examination aims to assess if a document of interest has been forged. Spectroscopy-based methods are the gold standard for this type of evaluation. In the past 15 years, Matrix-Assisted Laser Desorption Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has emerged as a powerful analytical tool for the examination of finger marks, blood, and hair. Therefore, this study intended to explore the possibility of expanding the forensic versatility of this technique through its application to questioned documents. Specifically, a combination of MALDI-MSI and chemometric approaches was investigated for the differentiation of seven gel pens, through their ink composition, over 44 days to assess: (i) the ability of MALDI MSI to detect and image ink chemical composition and (ii) the robustness of the combined approach for the classification of different pens over time. The training data were modelled using elastic net logistic regression to obtain probabilities for each pen class and assess the time effect on the ink. This strategy led the classification model to yield predictions matching the ground truth. This model was validated using signatures generated by different pens (blind to the analyst), yielding a 100% accuracy in machine learning cross-validation. These data indicate that the coupling of MALDI-MSI with machine learning was robust for ink discrimination within the dataset and conditions investigated, which justifies further studies, including that of confounders such as paper brands and environmental factors.

Item Type:	Article
Uncontrolled Keywords:	MALDI; documents; ink; machine learning; 0304 Medicinal and Biomolecular Chemistry; 0305 Organic Chemistry; 0307 Theoretical and Computational Chemistry; Organic Chemistry; 3404 Medicinal and biomolecular chemistry; 3405 Organic chemistry
Identification Number:	https://doi.org/10.3390/molecules28135207
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	24 Jul 2023 14:57
Last Modified:	11 Oct 2023 13:02
URI:	https://shura.shu.ac.uk/id/eprint/32188

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