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Updated: Jun 20, 2025

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks
Published on: May 8, 2015
Discrimination and analytical profiling of colored printed documents using ATR-FTIR spectroscopy coupled with
Anjali Tomar1, Yamini1, Reeta R Gupta2
1Institute of Forensic Science & Criminology, Panjab University, Chandigarh 160014, India.
Abstract:
Printed documents are a common form of evidence in forensic document examination. The integration of spectroscopy with chemometrics have evolved evidential analytical interpretation of printing inks. However, we report the first ever study that explores the examination of both black and colored printed documents combined with explorative Principal Component Analysis (PCA) and supervised techniques viz. Soft independent modelling of class analogy (SIMCA) and Partial Least Square- Discriminant Analysis (PLS-DA). The study investigated 74 (40 Ink-based and 34 Toner- based) colored printed document samples using ATR-FTIR to discriminate and determine the source of origin of an unknown printed document using a non-destructive approach. Qualitative analysis by ATR- FTIR indicated the presence of polystyrene, bisphenol A and acrylates as the common binder polymers in the samples. The study was also able to obtain pigment information like presence of PR 57 and PR 146 in magenta, Carbon black in black, Copper Phthalocyanine and PB 15 in Cyan and PY 74 in yellow colored printed samples. Further, PCA has been used as an explorative technique that showed a variance of 97 % in the dataset and indicating that the color Cyan contributes to the maximum classification accuracy. SIMCA has been used as a supervised method to classify the known and test samples to their respective defined classes. However, SIMCA could only classify Toner-based samples in their respective class and inconclusive results were obtained in case of Ink-based samples. Finally, PLS-DA was also used to classify the two class of samples which resulted in a discrimination accuracy of 98.6 %. The derived model was also used for validation study on blind test samples which provided 100 % classification results.
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