Tafintseva V, Lintvedt TA, Solheim JH, Zimmermann B, Rehman HU, Virtanen V, Shaikh R, Nippolainen E, Afara I, Saarakkala S, Rieppo L, Krebs P, Fomina P, Mizaikoff B, Kohler A. Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics. Molecules. 2022; 27(3):873. https://doi.org/10.3390/molecules27030873
Preprocessing strategies for sparse infrared spectroscopy : a case study on cartilage diagnostics
|Author:||Tafintseva, Valeria1; Lintvedt, Tiril Aurora1,2; Solheim, Johanne Heitmann1;|
1Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
2Norwegian Institute for Food Fisheries and Aquaculture Research (Nofima), 9291 Tromsø, Norway
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90220 Oulu, Finland
4Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
5Department of Orthopedics, Traumatology, Hand Surgery, Kuopio University Hospital, 70210 Kuopio, Finland
6Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany
|Online Access:||PDF Full Text (PDF, 18.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022052338127
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2022-08-22
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
217 Medical engineering
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
© 2022 by the authors. 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/).