The Representation of Chemical Spectral Data for Classification

Porro, Diana; Duin, Robert W.; Talavera, Isneri; Hdez, N.

doi:10.1007/978-3-642-10268-4_60

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Dealing with so many variables (features) as the number of wavenumbers for which 255 intensities of spectroscopic signals were measured is both inconvenient and prevents easy observation of the genuine spectra similarities and differences. As suggested in [28,29,30] the so-called distance representation has many advantages over classical feature representation. It not only enables for substantial data compression, but also easily copes with the non-linear problems [28].…”

Section: Reduction Of Data Dimensionalitymentioning

confidence: 99%

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

Martyna

Zadora

Neocleous

et al. 2016

Analytica Chimica Acta

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Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta, 931, pp. 34-46. (doi:10.1016Acta, 931, pp. 34-46. (doi:10. /j.aca.2016 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/119762/ Chemistry, 3 Ingardena, Poland, 9 Westerplatte, Poland, University of Silesia in Katowice, Institute of Chemistry, Chemometric Research Group, 9 Szkolna, Poland, University of Glasgow, School of Mathematics and Statistics, 15 University Gardens, Glasgow G12 8QW, United Kingdom, Abstract 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 multivari- * Corresponding authorEmail addresses: rzepecka@chemia.uj.edu.pl (Agnieszka Martyna), gzadora@ies.krakow.pl (Grzegorz Zadora), tereza.neocleous@glasgow.ac.uk (Tereza Neocleous), amichalska@ies.krakow.pl (Aleksandra Michalska), nema.dean@glasgow.ac.uk (Nema Dean) Preprint submitted to Analytica Chimica ActaMarch 25, 2016 ate 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-samples variability while maximising the between-samples 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.

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Section: Reduction Of Data Dimensionalitymentioning

confidence: 99%

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

Martyna

Zadora

Neocleous

et al. 2016

Analytica Chimica Acta

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Dissimilarity representation on functional spectral data for classification

Porro-Muñoz

Talavera

Duin

et al. 2011

Journal of Chemometrics

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In chemometrics, spectral data are typically represented by vectors of features in spite of the fact that they are usually plotted as functions of e.g. wavelengths and concentrations. In the representation, this functional information is thereby not reflected. Consequently, some characteristics of the data that can be essential for discrimination between samples of different classes or any other analysis are ignored. Examples are the continuity between measured points and the shape of curves. In the Functional Data Analysis (FDA) approach, the functional characteristics of spectra are taken into account by approximating the data by real valued functions, e.g. splines. Another solution is the Dissimilarity Representation (DR), in which classifiers are trained in a space built by dissimilarities with training examples or prototypes of each class. Functional information may be incorporated in the definition of the dissimilarity measure. In this paper we compare the feature-based representation of chemical spectral data with three other representations: FDA, DR defined on raw data and DR defined on FDA descriptions. We analyze the classification results of these four representations for five data sets of different types, by using different classifiers. We demonstrate the importance of reflecting the functional characteristics of chemical spectral data in their representation, and we show when the presented approaches are more suitable.

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