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Frisch K, Nielsen KL, Hasselstro M JRB, Fink R, Rasmussen SV, Johannsen M. Desorption Electrospray Ionization Mass Spectrometry Imaging of Powder-Treated Fingermarks on Forensic Gelatin Lifters and its Application for Separating Overlapping Fingermarks. Anal Chem 2024. [PMID: 39028891 DOI: 10.1021/acs.analchem.4c02305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2024]
Abstract
Fingermarks are frequently collected at crime scenes by using gelatin lifters for preservation and transport of the marks to a forensic laboratory for inspection. The gelatin lifters preserve both the imprint of the fingermark pattern necessary for identification purposes and the chemical residue of the mark potentially useful for profiling the person who left the fingermark. The fingermark patterns are traditionally recorded using photography/optical imaging, but methods for chemical analysis of fingermark residues on gelatin lifters are scarce. Here we report the first method for the chemical analysis of fingermarks on gelatin lifters using desorption electrospray ionization mass spectrometry (DESI-MS) imaging. The imaging can be done directly on the gelatin support without any sample preparation, supporting immediate operational use of the method for fingermarks collected at crime scenes. Operational use of the method is further supported by successful chemical imaging of fingermarks enhanced by traditional dusting with forensic powders and lifted off different surfaces (glass, stainless steel, painted aluminum, polystyrene, cardboard, and plastic) as well as fingermarks lifted multiple times. We also demonstrate that the present method can be used to visually separate natural overlapping powder-treated fingermarks, and the chemical composition of the fingermarks can be analyzed on the gelatin support by DESI-MS/MS. The presented method has potential for integration into the traditional workflow for fingermark analysis, and will allow more fingermarks collected at crime scenes to be evaluated both visually and chemically.
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Affiliation(s)
- Kim Frisch
- Department of Forensic Medicine, Aarhus University, Aarhus N 8200, Denmark
| | - Kirstine L Nielsen
- Department of Forensic Medicine, Aarhus University, Aarhus N 8200, Denmark
| | | | - Rikke Fink
- National Special Crime Unit, Danish Police, Glostrup 2600, Denmark
| | | | - Mogens Johannsen
- Department of Forensic Medicine, Aarhus University, Aarhus N 8200, Denmark
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Frisch K, Nielsen KL, Francese S. MALDI MSI Separation of Same Donor's Fingermarks Based on Time of Deposition-A Proof-of-Concept Study. Molecules 2023; 28:molecules28062763. [PMID: 36985735 PMCID: PMC10054356 DOI: 10.3390/molecules28062763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 03/30/2023] Open
Abstract
Despite the advent of DNA profiling, fingerprints still play an important role in suspect identification. However, if single crime scene marks may be challenging to identify, overlapping fingermarks, understandably, pose an even greater challenge. In the last decade, mass spectrometry-imaging methods have provided a possible solution to the separation of fingermarks from two or more donors, based on the differential chemical composition. However, there are no studies attempting to separate overlapping marks from the same donor. This is important in relation to fingermark deposition at different times, which could be critical, for example, to ascertain legitimate access to the scene. In the work presented here, we investigate whether Matrix-Assisted Laser Desorption Ionisation Mass Spectrometry Imaging can separate the same donor's fingermarks deposited at different times based on intra-donor fingermark composition variability. Additionally, the hypothesis that the different times of deposition could be also determined was investigated in the view of linking the suspect at the scene at different times; the dating window of MALDI MSI within the selected molecular range was explored. Results show that it is possible to separate overlapping fingermarks from the same donor in most cases, even from natural marks. Fresh marks (0 days) could be separated from those of fourteen days of age, though the latter could not be distinguished from the set aged for seven days. Due to the use of only one donor, these are to be considered preliminary data, though findings are interesting enough to warrant further investigation of the capabilities and limitations of this approach using a larger cohort of donors.
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Affiliation(s)
- Kim Frisch
- Department of Forensic Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | | | - Simona Francese
- Centre for Mass Spectrometry Imaging, Biomolecular Sciences Research Centre, College of Health, Wellbeing and Life Science, Sheffield Hallam University, Sheffield S1 1WB, UK
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Bécue A, Champod C. Interpol review of fingermarks and other body impressions 2019 - 2022). Forensic Sci Int Synerg 2022; 6:100304. [PMID: 36636235 PMCID: PMC9830181 DOI: 10.1016/j.fsisyn.2022.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Andy Bécue
- University of Lausanne, School of Criminal Justice, Faculty of Law Criminal Justice and Public Administration, Switzerland
| | - Christophe Champod
- University of Lausanne, School of Criminal Justice, Faculty of Law Criminal Justice and Public Administration, Switzerland
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Zhang P, Shen Q, Zhou Y, He F, Zhao B, Wang Z, Xu R, Xu Y, Yang Z, Meng L, Dang D. Synthesis of D-A typed AIE luminogens in isomeric architecture and their application in latent fingerprints imaging. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.107910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Gardner W, Winkler DA, Cutts SM, Torney SA, Pietersz GA, Muir BW, Pigram PJ. Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder. Anal Chem 2022; 94:7804-7813. [PMID: 35616489 DOI: 10.1021/acs.analchem.1c05453] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.
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Affiliation(s)
- Wil Gardner
- Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia.,La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.,CSIRO Manufacturing, Clayton, Victoria 3168, Australia
| | - David A Winkler
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.,Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, U.K
| | - Suzanne M Cutts
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Steven A Torney
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Geoffrey A Pietersz
- Immune Therapies Laboratory, Burnet Institute, Melbourne, Victoria 3004, Australia.,Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | | | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia
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Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems. Biointerphases 2022; 17:020802. [DOI: 10.1116/6.0001590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.
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