Comparison of Different Calibration Methods Suited for Calibration Problems with Many Variables

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This paper describes and compares different kinds of statistical methods proposed in the literature as suited for solving calibration problems with many variables. These are: principal component regression, partial least-squares, and ridge regression. The statistical techniques themselves do not provide robust results in the spirit of calibration equations which can last for long periods. A way of obtaining this property is by smoothing and differentiating the data. These techniques are considered, and it is shown how they fit into the treated description.

Keywords: Calibration; Near-infrared reflectance (NIR); Partial least-squares; Principal component regression; Ridge regression; Smoothing

Document Type: Research Article


Affiliations: The Institute of Mathematical Statistics and Operations Research, The Technical University of Denmark, DK-2800 Lyngby, Denmark

Publication date: December 1, 1992

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