University of Oulu

Prakash, M., Sarin, J. K., Rieppo, L., Afara, I. O., & Töyräs, J. (2017). Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage. Applied Spectroscopy, 71(10), 2253–2262.

Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage

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Author: Prakash, Mithilesh1; Sarin, Jaakko K.1,2; Rieppo, Lassi1,3;
Organizations: 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: SAGE Publications, 2017
Publish Date: 2019-07-02


Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (\(R^2_{Tissue~thickness} = 75.6\%\), \(R^2_{Dynamic~modulus} = 64.9\%\)) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

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Series: Applied spectroscopy
ISSN: 0003-7028
ISSN-E: 1943-3530
ISSN-L: 0003-7028
Volume: 71
Issue: 10
Pages: 2253 - 2262
DOI: 10.1177/0003702817726766
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Funding: This study was funded by the Academy of Finland (project 267551, University of Eastern Finland), Kuopio University Hospital (VTR projects 5041750 and 5041744, PY210 Clinical Neurophysiology) and Instrumentarium Science Foundation (170033). Dr. Afara would like to acknowledge grant funding from the Finnish Cultural Foundation (00160079).
Copyright information: © 2017 SAGE Publications. This document is the Accepted Manuscript version of a Published Work that appeared in final form at