Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage. Issue 10 (October 2017)

• Record Type: Journal Article
• Authors: Prakash, Mithilesh; Sarin, Jaakko K.; Rieppo, Lassi; Afara, Isaac O.; Töyräs, Juha
• Is Part Of: Applied spectroscopy. Volume 71:Issue 10(2017)
• Page Start: 2253
• Page End: 2262
• Publication Date: 2017-10
• Subjects: Uninformative variable elimination -- UVE -- arthroscopy -- multivariate regression -- cartilage -- near-infrared spectroscopy -- NIR; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702817726766
• Languages: English
• ISSNs: 0003-7028

Abstract

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.