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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9000438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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