Variables Selection from the Patterns of the Features Applied to Spectroscopic Data—An Application Case

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Yayımlandı:Mathematics vol. 13, no. 1 (2025), p. 99
Yazar: Romero-Béjar, José L
Diğer Yazarlar: Esquivel, Francisco Javier, Esquivel, José Antonio
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MDPI AG
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100 1 |a Romero-Béjar, José L  |u Department of Statistics and Operations Research, University of Granada, 18011 Granada, Spain; <email>jlrbejar@ugr.es</email> (J.L.R.-B.); <email>jesquivel@ugr.es</email> (F.J.E.); Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain; Institute of Mathematics, University of Granada (IMAG), Ventanilla 11, 18001 Granada, Spain 
245 1 |a Variables Selection from the Patterns of the Features Applied to Spectroscopic Data—An Application Case 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Spectroscopic data allows for the obtaining of relevant information about the composition of samples and has been used for research in scientific disciplines such as chemistry, geology, archaeology, Mars research, pharmacy, and medicine, as well as important industrial use. In archaeology, it allows the characterization and classification of artifacts and ecofacts, the analysis of patterns, the characterization and study of the exchange of materials, etc. Spectrometers provide a large amount of data, the so-called “big data” type, which requires the use of multivariate statistical techniques, mainly principal component analysis, cluster analysis, and discriminant analysis. This work is focused on reducing the dimensionality of the data by selecting a small subset of variables to characterize the samples and presents a mathematical methodology for the selection of the most efficient variables. The objective is to identify a subset of variables based on spectral features that allow characterization of the samples under study with the least possible errors when performing quantitative analyses or discriminations between different samples. The subset is not predetermined and, in each case, is obtained for each set of samples based on the most important features of the samples under study, which allows for a good fit to the data. The reduction of the number of variables to an important performance based on the previously chosen difference between features, with a great fit to the raw data. Thus, instead of 2151 variables, a minimum optimal subset of 32 valleys and 31 peaks is obtained for a minimum difference between peaks or between valleys of 20 nm. This methodology has been applied to a sample of minerals and rocks extracted from the ECOSTRESS 1.0 spectral library. 
610 4 |a US Geological Survey Johns Hopkins University 
653 |a Data analysis 
653 |a Vegetation 
653 |a Minerals 
653 |a Datasets 
653 |a Cluster analysis 
653 |a Big Data 
653 |a Spectrum analysis 
653 |a Principal components analysis 
653 |a Spectrometers 
653 |a Signal processing 
653 |a Multivariate analysis 
653 |a Variables 
653 |a Industrial applications 
653 |a Energy 
653 |a Error reduction 
653 |a Discriminant analysis 
653 |a Libraries 
653 |a Valleys 
653 |a Radiation 
653 |a Asymmetry 
700 1 |a Esquivel, Francisco Javier  |u Department of Statistics and Operations Research, University of Granada, 18011 Granada, Spain; <email>jlrbejar@ugr.es</email> (J.L.R.-B.); <email>jesquivel@ugr.es</email> (F.J.E.); Laboratory of 3D Archaeological Modelling, University of Granada, 18011 Granada, Spain 
700 1 |a Esquivel, José Antonio  |u Laboratory of 3D Archaeological Modelling, University of Granada, 18011 Granada, Spain; Department of Prehistory and Archaeology, University of Granada, 18011 Granada, Spain 
773 0 |t Mathematics  |g vol. 13, no. 1 (2025), p. 99 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3153862563/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3153862563/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3153862563/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch