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Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand s...

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Autores principales: Tafintseva, Valeria, Lintvedt, Tiril Aurora, Solheim, Johanne Heitmann, Zimmermann, Boris, Rehman, Hafeez Ur, 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/PMC8839829/
https://www.ncbi.nlm.nih.gov/pubmed/35164133
http://dx.doi.org/10.3390/molecules27030873
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author Tafintseva, Valeria
Lintvedt, Tiril Aurora
Solheim, Johanne Heitmann
Zimmermann, Boris
Rehman, Hafeez Ur
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
author_facet Tafintseva, Valeria
Lintvedt, Tiril Aurora
Solheim, Johanne Heitmann
Zimmermann, Boris
Rehman, Hafeez Ur
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
author_sort Tafintseva, Valeria
collection PubMed
description The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm(−1), followed by peak normalization at 850 cm(−1) and preprocessing by MSC.
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spelling pubmed-88398292022-02-13 Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics Tafintseva, Valeria Lintvedt, Tiril Aurora Solheim, Johanne Heitmann Zimmermann, Boris Rehman, Hafeez Ur Virtanen, Vesa Shaikh, Rubina Nippolainen, Ervin Afara, Isaac Saarakkala, Simo Rieppo, Lassi Krebs, Patrick Fomina, Polina Mizaikoff, Boris Kohler, Achim Molecules Article The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm(−1), followed by peak normalization at 850 cm(−1) and preprocessing by MSC. MDPI 2022-01-27 /pmc/articles/PMC8839829/ /pubmed/35164133 http://dx.doi.org/10.3390/molecules27030873 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
Tafintseva, Valeria
Lintvedt, Tiril Aurora
Solheim, Johanne Heitmann
Zimmermann, Boris
Rehman, Hafeez Ur
Virtanen, Vesa
Shaikh, Rubina
Nippolainen, Ervin
Afara, Isaac
Saarakkala, Simo
Rieppo, Lassi
Krebs, Patrick
Fomina, Polina
Mizaikoff, Boris
Kohler, Achim
Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title_full Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title_fullStr Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title_full_unstemmed Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title_short Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
title_sort preprocessing strategies for sparse infrared spectroscopy: a case study on cartilage diagnostics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8839829/
https://www.ncbi.nlm.nih.gov/pubmed/35164133
http://dx.doi.org/10.3390/molecules27030873
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