Locally Weighted Regression in Diffuse Near-Infrared Transmittance Spectroscopy
Abstract:This paper presents an application of locally weighted regression (LWR) in diffuse near-infrared transmittance spectroscopy. The data are from beef and pork samples. The LWR method is based on the idea that a nonlinearity can be approximated by local linear equations. Different weight functions (for the samples) as well as different distance measures for "closeness" are tested. The LWR is compared to principal component regression and partial least-squares regression. The LWR with weighted principal components is shown to give the best results. The improvements with respect to linear regression are up to 15% of the prediction errors.
Document Type: Research Article
Affiliations: MATFORSK, Oslovegen 1, 1430 Ås, Norway
Publication date: January 1, 1992
More about this publication?
- The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Membership Information
- Request copyrighted SAS materials
- Spectroscopic Nomenclature
- Focal Point (Open Access)
- ingentaconnect is not responsible for the content or availability of external websites