2017
DOI: 10.1021/acs.analchem.6b04213
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Label-Free Chemical Imaging of Latent Fingerprints with Stimulated Raman Scattering Microscopy

Abstract: Fingerprints have long been the gold standard for personal identification in forensic investigations. Methods for cultivating and enhancing the visualization of latent fingerprints (LFPs) are continuously evolving. One important challenge is to identify suspicious chemicals present in fingerprint residues, which requires chemical imaging capability. Recently, vibrational spectroscopy has shown that LFP analysis through tape-lift, Raman mapping, and multivariate data analysis presents a useful tool for forensic… Show more

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Cited by 33 publications
(18 citation statements)
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“…SRS images were acquired using a homebuilt SRS microscope as described previously [29][30][31] and as shown in Fig. 1.…”
Section: Srs Imagingmentioning
confidence: 99%
“…SRS images were acquired using a homebuilt SRS microscope as described previously [29][30][31] and as shown in Fig. 1.…”
Section: Srs Imagingmentioning
confidence: 99%
“…Additionally, the spectral resolution of most current systems is limited to ~20-25 cm −1 [46,47]. While these capabilities are often sufficient for imaging in the high wavenumber region (2800-3050 cm −1 ), they are insufficient for imaging in the fingerprint region (700-1800 cm −1 ).…”
Section: Introductionmentioning
confidence: 99%
“…Nowadays, many methods exist as standard methods for the development of the LFPs on common substrates in routine forensic practice (Ezhilmaran and Adhiyaman, 2017 ; Lennard, 2020 ), but there are still some situations that it is difficult or impossible to recover LFPs for forensic investigators. The ongoing research is being directed at improved sensitivity, universality, convenience, and efficiency via the optimization of existing methods or new approaches, such as spectroscopy, mass spectrometry, immuno-labeling, and nanoparticles based approaches (Nakamura et al, 2015 ; Zhao et al, 2016 ; Figueroa et al, 2017 ; O'Neill et al, 2018 ; Kolhatkar et al, 2019 ; Bodelón and Pastoriza-Santos, 2020 ; Li et al, 2020 ).…”
Section: Introductionmentioning
confidence: 99%