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Accounting for spatial dependency in multivariate spectroscopic data

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Article (2.639Mb)
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final draft
Date
2018
Author(s)
Prakash, M
Sarin, JK
Rieppo, L
Afara IO
Töyräs, J
Unique identifier
10.1016/j.chemolab.2018.09.010
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Citation
Prakash, M. Sarin, JK. Rieppo, L. Afara IO. Töyräs, J. (2018). Accounting for spatial dependency in multivariate spectroscopic data.  CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 182, 166-171. 10.1016/j.chemolab.2018.09.010.
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© Elsevier B.V.
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CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

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.

Subjects
linear mixed effects   articular cartilage   near infrared (NIR) spectroscopy   spectroscopic mapping   principal components   LASSO   
URI
https://erepo.uef.fi/handle/123456789/7127
Link to the original item
http://dx.doi.org/10.1016/j.chemolab.2018.09.010
Publisher
Elsevier BV
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  • Luonnontieteiden ja metsätieteiden tiedekunta [1133]
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