Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra

Martyna, A., Zadora, G., Neocleous, T. , Michalska, A. and Dean, N. (2016) Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta, 931, pp. 34-46. (doi: 10.1016/j.aca.2016.05.016) (PMID:27282749)

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Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Dean, Dr Nema and Neocleous, Dr Tereza
Authors: Martyna, A., Zadora, G., Neocleous, T., Michalska, A., and Dean, N.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Analytica Chimica Acta
ISSN (Online):1873-4324
Published Online:24 May 2016
Copyright Holders:Copyright © 2016 Elsevier
First Published:First published in Analytica Chimica Acta 931:34-46
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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