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Multivariate analysis for forensic characterization,discrimination, and classification of marker pen inks
Authors:Vishal Sharma  Raj Kumar  Karan Devgan  Pawan Kumar Mishra  Adam Ekielski  Vijay Kumar
Affiliation:1. Institute of Forensic Science &2. Criminology, Panjab University, Chandigarh, Uttar Pradesh, India;3. Department of Wood Science, Mendel University in Brno, Brno, Czech Republic;4. Department of Applied Physics, Chandigarh University, Gharuan, Mohali, Punjab, India
Abstract:The multivariate analysis methods have recently gained high popularity within the field of forensic sciences because of their high accuracy and precision. The accurate and unbiased results are the preliminary need for a forensic investigation. The aim of the present work is to examine the marker pen inks which are widely used in various places like documentation in parcels, for photograph attestation, and also as a study material in the classroom. This research is focused on the three important aspects; first is to characterize the marker inks, second, to discriminate permanent marker and whiteboard marker inks using destructive (extracting of ink samples from paper substrate) and nondestructive (without ink extraction) techniques of ultraviolet–visible absorbance combined with peak identification examination as well as chemometric methods, and the third is to build a classification model for permanent and whiteboard marker inks. It is concluded that the chemometric method, that is, principal component analysis provides better discrimination power as compared to visual examination. However, destructive and nondestructive approaches give almost similar discriminating power. The classification model developed using linear discriminant analysis provides 87.5% of correct classification of marker ink samples. The method can further be used to formulate a statistical model for the determination of class/group of the other forensic exhibits.
Keywords:Forensic science  linear discriminant analysis  marker pen ink  multivariate analysis  principle component analysis
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