Classification of document papers by infrared spectroscopy and multivariate statistical techniques

Kher, A; Mulholland, M; Reedy, B; Maynard, P

Classification of document papers by infrared spectroscopy and multivariate statistical techniques

Kher, A Mulholland, M Reedy, B

Maynard, P

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Publication Type:: Journal Article
Citation:: Applied Spectroscopy, 2001, 55 (9), pp. 1192 - 1198
Issue Date:: 2001-09-01

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Field	Value	Language
dc.contributor.author	Kher, A	en_US
dc.contributor.author	Mulholland, M	en_US
dc.contributor.author	Reedy, B https://orcid.org/0000-0003-1797-8091	en_US
dc.contributor.author	Maynard, P https://orcid.org/0000-0003-4953-9114	en_US
dc.date.issued	2001-09-01	en_US
dc.identifier.citation	Applied Spectroscopy, 2001, 55 (9), pp. 1192 - 1198	en_US
dc.identifier.issn	0003-7028	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3554
dc.description.abstract	Infrared (IR) spectra of different varieties of document papers were collected with the use of attenuated total reflectance (ATR, 4000-650 cm-1, eight paper varieties) and diffuse reflectance (DRIFTS, 9000-2500 cm-1, six paper varieties) techniques. The spectral data were classified by the application of soft independent modeling of class analogies (SIMCA), using principal components analysis (PCA) to estimate the distance of separation between the different classes of paper samples and discriminant analysis (DA) to obtain a probabilistic classification. The use of DA on spectral data needed a preliminary data reduction step, either by PCA-decomposition of spectra or the selection of discrete spectral features having maximum discriminating ability. The aim of this research was to evaluate these data-reduction techniques and compare the discriminating power of these two spectral techniques (DRIFTS and ATR) by the application of PCA and DA. The use of PCA scores as DA variables provided the best resolution (100% correct classification) for the DRIFTS spectra, while PCA on the ATR spectra resulted in the best discrimination, separating 67.86% paper pairs completely with the use of cross-validation. The results of this study reemphasize that infrared spectroscopy coupled with multivariate statistical methods of analysis could provide a powerful discriminating tool for the forensic questioned document examiner.	en_US
dc.relation.ispartof	Applied Spectroscopy	en_US
dc.relation.isbasedon	10.1366/0003702011953199	en_US
dc.subject.classification	Analytical Chemistry	en_US
dc.title	Classification of document papers by infrared spectroscopy and multivariate statistical techniques	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	55	en_US
utslib.for	0301 Analytical Chemistry	en_US
utslib.for	0306 Physical Chemistry (incl. Structural)	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Chemistry and Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	55	en_US

Abstract:

Infrared (IR) spectra of different varieties of document papers were collected with the use of attenuated total reflectance (ATR, 4000-650 cm-1, eight paper varieties) and diffuse reflectance (DRIFTS, 9000-2500 cm-1, six paper varieties) techniques. The spectral data were classified by the application of soft independent modeling of class analogies (SIMCA), using principal components analysis (PCA) to estimate the distance of separation between the different classes of paper samples and discriminant analysis (DA) to obtain a probabilistic classification. The use of DA on spectral data needed a preliminary data reduction step, either by PCA-decomposition of spectra or the selection of discrete spectral features having maximum discriminating ability. The aim of this research was to evaluate these data-reduction techniques and compare the discriminating power of these two spectral techniques (DRIFTS and ATR) by the application of PCA and DA. The use of PCA scores as DA variables provided the best resolution (100% correct classification) for the DRIFTS spectra, while PCA on the ATR spectra resulted in the best discrimination, separating 67.86% paper pairs completely with the use of cross-validation. The results of this study reemphasize that infrared spectroscopy coupled with multivariate statistical methods of analysis could provide a powerful discriminating tool for the forensic questioned document examiner.

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http://hdl.handle.net/10453/3554