SEM-EDS analysis and characterization of glitter and shimmer cosmetic particles

Smith, 2018 Morgan, 2019, Criminal victimization, 2018 Edmond Locard. https://www.crimemuseum.org/ (accessed May 12, 2020). Kirk, 1953 Petherick, 2009 Blackledge, 2007, 1 Blackledge, 2007, GLITTER as forensic evidence Jiang, 2002, Novel pigment approaches in optically variable security inks including polarizing cholesteric liquid crystal (CLC) polymers, 247 Aardahl, 2005, A target glitter study, Sci. Justice, 45, 7, 10.1016/S1355-0306(05)71615-4 López-López, 2015, Confocal Raman spectrocopy for the analysis of nail polish evidence, Talanta, 138, 155, 10.1016/j.talanta.2015.02.031 Vernoud, 2011, Characterization of multilayered glitter particles using synchrotron FT-IR microscopy, Forensic Sci. Int., 210, 47, 10.1016/j.forsciint.2011.01.033 Zellner, 2009, Differentiation of twenty‐one glitter lip‐glosses by pyrolysis gas Chromatography/Mass spectroscopy, J. Forensic Sci., 54, 1022, 10.1111/j.1556-4029.2009.01109.x Gross, 2010, Glitter as a source of trace evidence, JASTEE, 1, 62 Griggs, 2011, Shimmer as forensic evidence, Global Forensic Sci Today, 1, 19 Gordon, 2004, The evidential value of cosmetic foundation smears in forensic casework, J. Forensic Sci., 49, 1244, 10.1520/JFS2004176 Choudhry, 1991, Comparison of minute smears of lipstick by microspectrophotometry and scanning electron microscopy/energy-dispersive spectroscopy, J. Forensic Sci., 36, 366, 10.1520/JFS13039J Afanasyev, 2019, Scanning electron microscope (advantages and disadvantages) Choudhary, 2017, Scanning electron microscope: advantages and disadvantages in imaging components, Int. J. Curr. Microbiol. Appl. Sci., 6, 1877, 10.20546/ijcmas.2017.605.207 Ngo, 1999, Energy dispersive spectroscopy, 205 Grieve, 1987, Glitter particles—an unusual source of trace evidence?, J. Forensic Sci. Soc., 27, 405, 10.1016/S0015-7368(87)72789-3 Najjar, 2018, Characterization of glitter cosmetic particles using FT-IR spectroscopy, SPECTROSCOPY, 33, 30 Nugrahaeni, 2016, Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification, 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), 163, 10.1109/ISEMANTIC.2016.7873831