期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 182, 期 -, 页码 166-171出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2018.09.010
关键词
Linear mixed effects; Articular cartilage; Near infrared (NIR) spectroscopy; Spectroscopic mapping; Principal components; LASSO
类别
资金
- Academy of Finland [267551]
- Research Committee of the Kuopio University Hospital Catchment Area (Kuopio, Finland) [PY210, 5041750, 5041744, 5041772]
- Instrumentarium Science Foundation [170033]
- Finnish Foundation for Technology Promotion [8193]
- Finnish Cultural Foundation [00160079]
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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