Show simple item record

dc.contributor.authorPrakash, M
dc.contributor.authorSarin, JK
dc.contributor.authorRieppo, L
dc.contributor.authorAfara IO
dc.contributor.authorTöyräs, J
dc.date.accessioned2018-11-05T09:42:29Z
dc.date.available2018-11-05T09:42:29Z
dc.date.issued2018
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/7127
dc.description.abstractWe 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.
dc.language.isoenglanti
dc.publisherElsevier BV
dc.relation.ispartofseriesCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
dc.relation.urihttp://dx.doi.org/10.1016/j.chemolab.2018.09.010
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectlinear mixed effects
dc.subjectarticular cartilage
dc.subjectnear infrared (NIR) spectroscopy
dc.subjectspectroscopic mapping
dc.subjectprincipal components
dc.subjectLASSO
dc.titleAccounting for spatial dependency in multivariate spectroscopic data
dc.description.versionfinal draft
dc.contributor.departmentDepartment of Applied Physics, activities
uef.solecris.id57729400en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© Elsevier B.V.
dc.relation.doi10.1016/j.chemolab.2018.09.010
dc.description.reviewstatuspeerReviewed
dc.format.pagerange166-171
dc.publisher.countryAlankomaat
dc.relation.issn0169-7439
dc.relation.volume182
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessEi


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record