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

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 an...

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Autores principales: Kjeldbjerg Lassen, Johan, Bradshaw, Robert, Villesen, Palle, Francese, Simona
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343405/
https://www.ncbi.nlm.nih.gov/pubmed/37446869
http://dx.doi.org/10.3390/molecules28135207
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author Kjeldbjerg Lassen, Johan
Bradshaw, Robert
Villesen, Palle
Francese, Simona
author_facet Kjeldbjerg Lassen, Johan
Bradshaw, Robert
Villesen, Palle
Francese, Simona
author_sort Kjeldbjerg Lassen, Johan
collection PubMed
description 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.
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spelling pubmed-103434052023-07-14 Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination Kjeldbjerg Lassen, Johan Bradshaw, Robert Villesen, Palle Francese, Simona Molecules Article 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. MDPI 2023-07-04 /pmc/articles/PMC10343405/ /pubmed/37446869 http://dx.doi.org/10.3390/molecules28135207 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kjeldbjerg Lassen, Johan
Bradshaw, Robert
Villesen, Palle
Francese, Simona
Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title_full Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title_fullStr Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title_full_unstemmed Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title_short Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination
title_sort statistical modelling investigation of maldi-msi-based approaches for document examination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343405/
https://www.ncbi.nlm.nih.gov/pubmed/37446869
http://dx.doi.org/10.3390/molecules28135207
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