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Journal of Advances in Applied Physics
JAAP > Volume 2, Number 1, February 2020

Reducing Temperature Calibration Error in Multivariate Analysis of Fluorescence Spectra

Download PDF  (354.9 KB)PP. 9-14,  Pub. Date:January 15, 2020
DOI: 10.22606/jaap.2020.21002

Author(s)
Mikhail Khodasevich, Vladimir Aseev, Victor Klinkov, Evgenia Tsimerman, Darya Borisevich
Affiliation(s)
B.I.Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
National Research University of Information Technologies, Mechanics, and Optics, St. Petersburg, Russia
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
B.I.Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
Abstract
A method has been demonstrated to reduce temperature calibration error by integrated using principal component analysis, hierarchical cluster analysis and searching combination moving window interval projection to latent structures for fluorescent spectra of Er-doped 98MgCaSrBaYAl2F14-2Ba(PO3)2 and Yb-doped CaF2. The consecutive and consistent use of these multivariate methods for outliers detection, forming training and test datasets and variable selection is shown to allow more than twofold reducing the root-mean-square error of temperature calibration in comparison with the application of projection to latent structures without variable selection.
Keywords
Projection to latent structures, principal component analysis, cluster analysis, calibration, fluorescence spectrum.
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