University of Oulu

Prakash, M., Sarin, J., Rieppo, L., Afara, I., Töyräs, J. (2018) Accounting for spatial dependency in multivariate spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 182, 166-171. doi:10.1016/j.chemolab.2018.09.010

Accounting for spatial dependency in multivariate spectroscopic data

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Author: Prakash, M.1,2; Sarin, J.K.1,2; Rieppo, L.1,3;
Organizations: 1Department of Applied Physics, University of Eastern Finland
2Diagnostic Imaging Center, Kuopio University Hospital
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Elsevier, 2018
Publish Date: 2020-09-27


We examine a hybrid multivariate regression technique to account for the spatial dependency in spectroscopic data due to adjacent measurement locations in the same joint by combining dimension reduction methods and linear mixed effects (LME) modeling. Spatial correlation is a common limitation (assumption of independence) encountered in diagnostic applications involving adjacent measurement locations, such as mapping of tissue properties, and can impede tissue evaluations. Near-infrared spectra were collected from equine joints (n = 5) and corresponding biomechanical (n = 202), compositional (n = 530), and structural (n = 530) properties of cartilage tissue were measured. Subsequently, hybrid regression models for estimating tissue properties from the spectral data were developed in combination with principal component analysis (PCA-LME) scores and least absolute shrinkage and selection operator (LASSO-LME). Performance comparison of PCA-LME and principal component regression, and LASSO-LME and LASSO regression was conducted to evaluate the effects of spatial dependency. A systematic improvement in calibration models’ correlation coefficients and a decrease in cross validation errors were observed when accounting for spatial dependency. Our results indicate that accounting for spatial dependency using a LME-based approach leads to more accurate prediction models.

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Series: Chemometrics and intelligent laboratory systems
ISSN: 0169-7439
ISSN-E: 1873-3239
ISSN-L: 0169-7439
Volume: 182
Pages: 166 - 171
DOI: 10.1016/j.chemolab.2018.09.010
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This study was funded by the Academy of Finland (project 267551, University of Eastern Finland), Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project PY210 (5041750, 5041744 and 5041772), Kuopio, Finland), Instrumentarium Science Foundation (170033) and The Finnish Foundation for Technology Promotion (8193). Dr. Afara acknowledges grant funding from the Finnish Cultural Foundation (00160079).
Copyright information: © 2018 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license