Multivariate Calibration, an Overview

Abstract

Numerous methods of multivariate calibration methods exist with ridge regression, principal component regression, and partial least squares being the most popular methods in analytical chemistry. This mini‐review overviews multivariate calibration and provides a common theme with respect to the bias/variance tradeoff (harmony) and the harmony/parsimony tradeoff for model selection. Other multivariate calibration considerations are briefly reviewed. A few applications are noted.

Keywords:

• Multivariate calibration
• bias
• variance
• harmony
• parsimony
• regularization

Acknowledgment

This material is based upon work supported by the National Science Foundation under Grant No. CHE-0400034 and is gratefully acknowledged.