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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...
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/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. |
format | Online Article Text |
id | pubmed-8839829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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