University of Oulu

Rehman, H. U., Tafintseva, V., Zimmermann, B., Solheim, J. H., Virtanen, V., Shaikh, R., Nippolainen, E., Afara, I., Saarakkala, S., Rieppo, L., Krebs, P., Fomina, P., Mizaikoff, B., & Kohler, A. (2022). Preclassification of broadband and sparse infrared data by multiplicative signal correction approach. Molecules, 27(7), 2298.

Preclassification of broadband and sparse infrared data by multiplicative signal correction approach

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Author: Rehman, Hafeez Ur1; Tafintseva, Valeria1; Zimmermann, Boris1;
Organizations: 1Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway
2Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland
3Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
4Department of Orthopedics, Traumatology, Hand Surgery, Kuopio University Hospital, 70210 Kuopio, Finland
5Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2023-01-26


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 (RMSE) 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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Series: Molecules
ISSN: 1420-3049
ISSN-E: 1420-3049
ISSN-L: 1420-3049
Volume: 27
Issue: 7
Article number: 2298
DOI: 10.3390/molecules27072298
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This work was supported by the Europe Union’s Horizon 2020 Research and Innovation Programme (H2020-ICT-2016-2017) project MIRACLE (Grant Agreement Number 780598).
EU Grant Number: (780598) MIRACLE - Mid-infrared arthroscopy innovative imaging system for real-time clinical in depth examination and diagnosis of degenerative joint diseases
Copyright information: © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (