Abstract
Studying of paper carriers of information has become increasingly important in forensic practice. This is caused by growing number of bogus or in other manner fraudulently alternated documents. An identification of individual paper sheets and studying their differences are the parts of forensic analysis of multiple sheets documents. This paper focuses on the possibilities of processing of mathematical and statistical data obtained by measurements of FT-IR spectral characteristics of individual paper sheets in a studied document. The spectral data were used to create data matrix which contains values of absorbance and interval of wavenumber. The method of differentiation is based on the comparison of maximum absorbance values within the range of wavenumbers from 1562 to 1215 cm−1. This spectrum range is specific in occurrence of absorption bands that belong to calcium carbonate CaCO3—an element of paper added into it in relatively high amounts in the form of filler which content or percentage occurrence may be different considering to the paper producer or production technology. Based on the above-said, the paper sheets originating from different producers or from various production technology units can be differentiated one from another using a robust statistical analysis.
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References
Bohmer E (1980) Filling and loading. In: Casey JP (ed) Pulp and paper: chemistry and chemical technology, Chap. 4, vol 3. Wiley-Interscience Publ., New York
Brunelle RL, Crawford KR (2003) Advances in the forensic analysis and dating of writing ink. Charles C. Thomas Publisher, Springfield (ISBN 0-398-07347-3)
Brunelle RL, Reed RW (1984) Forensic examination of ink and paper. Charles C. Thomas Publisher, Springfield (ISBN 0-398-04935-1)
Causin V, Marega C, Marigo A, Casamassima R, Peluso G, Ripani L (2010) Forensic differentiation of paper by X- ray diffraction and infrared spectroscopy. Forensic Sci Int 197:70–74. https://doi.org/10.1016/j.forsciint.2009.12.056
Ellen D (2006) The scientific examination of documents: methods and techniques. CRC Press, Taylor & Francis, Boca Raton. https://doi.org/10.4324/9780203483596 (ISBN 0-8493-3925-1)
Hon D (1986) Fourier transform IR spectroscopy and electron spectroscopy for chemical analysis: use in the study of paper documents. Adv Chem 212:349–361. https://doi.org/10.1021/ba-1986-0212.ch019 (chap. 19)
Kher A, Mulholland M, Reedy B, Maynard P (2001) Classification of document papers by infrared spectroscopy and multivariate statistical techniques. Appl Spectrosc 55:1192–1198. https://doi.org/10.1366/0003702011953199
Manso M, Carvalho ML (2009) Application of spectroscopic techniques for the study of paper documents: a survey. Spectrochim Acta Part B 64:482–490. https://doi.org/10.1016/j.sab.2009.01.009
McKinney W (2010) Data structures for statistical computing in Python. In: Proceedings of the 9th Python in science conference, Austin, Texas, pp. 51–56. http://conference.scipy.org/proceedings/scipy2010/pdfs/proceedings.pdf. Accessed 27 Jan 2016
Mendiburu F (2014) Agricolae: statistical procedures for agricultural research. R package version 1.1-8. http://CRAN.R-project.org/package=agricolae. Accessed 27 Jan 2016
Python Software Foundation (2014) Python language reference, version 2.7. http://www.python.org. Accessed 27 Jan 2016
Rousseeuw PJ, Croux Ch (1993) Alternatives to the median absolute deviation. J Am Stat Assoc 88:1273–1283. https://doi.org/10.2307/2291267
R Development Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN: 3-900051-07-0. http://www.R-project.org/
van Es A, de Koeijer J, van der Peijl G (2009) Discrimination of document paper by XRF, LA–ICP–MS and IRMS using multivariate statistical techniques. Sci Justice 49:120–126. https://doi.org/10.1016/j.scijus.2009.03.006
Walt S, Colbert SCh, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22–30. https://doi.org/10.1109/MCSE.2011.37
Wickham H (2009) ggplot2: Elegant graphics for data analysis. Springer, New York. https://doi.org/10.18637/jss.v035.b01 (ISBN 978-0-387-98140-6)
Wickham H (2012) Scales: scale functions for graphics. R package version 0.2.3. http://CRAN.R-project.org/package=scales. Accessed 27 Jan 2016
Workman JJ (2001) Infrared and Raman spectroscopy in paper and pulp analysis. Appl Spectrosc Rev 36:139–168. https://doi.org/10.1081/asr-100106154
Acknowledgements
This work was supported by the Agency for Research and Development in Slovak Republic under contract no. APVV-0324-10, VEGA Grant agency under contract no. VEGA-0888/15 and other support received from the Center of Excellence for security research within operational program Research and Development, code ITMS 26240120034, co-funded by European Regional Development Fund.
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Tiňo, R., Vizárová, K., Provazníková, J. et al. Utilization of statistical analysis of FT-IR spectra in forensic examination of paper. Chem. Pap. 72, 2265–2272 (2018). https://doi.org/10.1007/s11696-018-0482-y
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DOI: https://doi.org/10.1007/s11696-018-0482-y