Discrimination and analytical profiling of colored printed documents using ATR-FTIR spectroscopy coupled with explorative and predictive statistical analysis: Part I

仿形(计算机编程) 有色的 傅里叶变换红外光谱 统计分析 分析化学(期刊) 光谱学 计算机科学 材料科学 色谱法 化学 统计 工程类 物理 化学工程 数学 复合材料 操作系统 量子力学
作者
Anjali Tomar,Yamini,R. Gupta,Manoj Kumar Choudhary,Shilpee Sachar,S.K. Mehta,Shweta Sharma
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:322: 124839-124839 被引量:1
标识
DOI:10.1016/j.saa.2024.124839
摘要

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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