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Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for...

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Autores principales: Rehman, Hafeez Ur, Tafintseva, Valeria, Zimmermann, Boris, Solheim, Johanne Heitmann, Virtanen, Vesa, Shaikh, Rubina, Nippolainen, Ervin, Afara, Isaac, Saarakkala, Simo, Rieppo, Lassi, Krebs, Patrick, Fomina, Polina, Mizaikoff, Boris, Kohler, Achim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9000438/
https://www.ncbi.nlm.nih.gov/pubmed/35408697
http://dx.doi.org/10.3390/molecules27072298
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author Rehman, Hafeez Ur
Tafintseva, Valeria
Zimmermann, Boris
Solheim, Johanne Heitmann
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
author_facet Rehman, Hafeez Ur
Tafintseva, Valeria
Zimmermann, Boris
Solheim, Johanne Heitmann
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
author_sort Rehman, Hafeez Ur
collection PubMed
description Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error ([Formula: see text]) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.
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spelling pubmed-90004382022-04-12 Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach Rehman, Hafeez Ur Tafintseva, Valeria Zimmermann, Boris Solheim, Johanne Heitmann Virtanen, Vesa Shaikh, Rubina Nippolainen, Ervin Afara, Isaac Saarakkala, Simo Rieppo, Lassi Krebs, Patrick Fomina, Polina Mizaikoff, Boris Kohler, Achim Molecules Article Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error ([Formula: see text]) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied. MDPI 2022-04-01 /pmc/articles/PMC9000438/ /pubmed/35408697 http://dx.doi.org/10.3390/molecules27072298 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rehman, Hafeez Ur
Tafintseva, Valeria
Zimmermann, Boris
Solheim, Johanne Heitmann
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title_full Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title_fullStr Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title_full_unstemmed Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title_short Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach
title_sort preclassification of broadband and sparse infrared data by multiplicative signal correction approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9000438/
https://www.ncbi.nlm.nih.gov/pubmed/35408697
http://dx.doi.org/10.3390/molecules27072298
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