Abstract
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.
Original language | English |
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Journal | Applied Spectroscopy |
Volume | 46 |
Issue number | 12 |
Pages (from-to) | 1780-1784 |
ISSN | 0003-7028 |
DOIs | |
Publication status | Published - 1992 |