Literature DB >> 24147967

The discrimination of colored acrylic, cotton, and wool textile fibers using micro-Raman spectroscopy. Part 1: in situ detection and characterization of dyes.

Patrick Buzzini1, Genevieve Massonnet.   

Abstract

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633-nm laser did not provide Raman information. The 514-nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830-nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.
© 2013 American Academy of Forensic Sciences.

Entities:  

Keywords:  Raman spectroscopy; discriminating power; dye analysis; fibers; forensic science; trace evidence

Year:  2013        PMID: 24147967     DOI: 10.1111/1556-4029.12298

Source DB:  PubMed          Journal:  J Forensic Sci        ISSN: 0022-1198            Impact factor:   1.832


  2 in total

1.  Scientometric analysis of the forensic science literature for fibre as an evidence type: Access and data availability.

Authors:  Virginie Galais; Holly Fleming; Niamh Nic Daéid; Hervé Ménard
Journal:  Forensic Sci Int Synerg       Date:  2022-05-17

Review 2.  The Identification of Cotton Fibers Dyed with Reactive Dyes for Forensic Purposes.

Authors:  Daria Śmigiel-Kamińska; Jolanta Wąs-Gubała; Piotr Stepnowski; Jolanta Kumirska
Journal:  Molecules       Date:  2020-11-20       Impact factor: 4.411

  2 in total

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