University of Oulu

Shaikh R, Tafintseva V, Nippolainen E, Virtanen V, Solheim J, Zimmermann B, Saarakkala S, Töyräs J, Kohler A, Afara IO. Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data. Journal of Personalized Medicine. 2023; 13(7):1036.

Characterisation of cartilage damage via fusing mid-infrared, near-infrared, and Raman spectroscopic data

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Author: Shaikh, Rubina1,2; Tafintseva, Valeria3; Nippolainen, Ervin1;
Organizations: 1Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
2School of Physics, Clinical and Optometric Sciences, Technological University Dublin, D07 XT95 Dublin, Ireland
3Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
4Research Unit of Medical Imaging, Physics and Technology, University of Oulu, 90570 Oulu, Finland
5Research Unit of Health Sciences and Technology, University of Oulu, 90220 Oulu, Finland
6Science Service Center, Kuopio University Hospital, 70210 Kuopio, Finland
7School of Information Technology and Electrical Engineering, The University of Queensland, Brisban, QLD 4072, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2023
Publish Date: 2023-10-12


Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

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Series: Journal of personalized medicine
ISSN: 2075-4426
ISSN-E: 2075-4426
ISSN-L: 2075-4426
Volume: 13
Issue: 7
Article number: 1036
DOI: 10.3390/jpm13071036
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Funding: This research was funded by the MIRACLE project-Horizon 2020 research and innovation programme-H2020-ICT-2017-1 (grant agreement 780598). The study was financially supported by the Academy of Finland (projects 315820, 310466), the Finnish Cultural Foundation (65211977 North Savo Regional Fund), and Kuopio University Hospital (VTR project 5203111 & 5203127). The authors would also like to acknowledge Horizon2020 and Enterprise Ireland (Diode, Project ID: MF 2021 0189).
EU Grant Number: (780598) MIRACLE - Mid-infrared arthroscopy innovative imaging system for real-time clinical in depth examination and diagnosis of degenerative joint diseases
Academy of Finland Grant Number: 310466
Detailed Information: 310466 (Academy of Finland Funding decision)
Copyright information: © 2023 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 (