Developments in nonlinear multivariate calibration

This paper describes ongoing efforts by researchers to develop methods for detecting, studying, and modeling nonlinear spectral response in multicomponent spectroscopic assays. Some topics for future study are also identified. Tests for detecting nonlinear regions of spectral response in multivariate, multicomponent spectroscopic assays are described. These techniques can be used to study the capability of multivariate linear models like multiple linear regression, principal components regression and partial least-squares to approximate nonlinear response. With artificial neural networks it is possible to develop calibrations that accommodate different functional forms of nonlinear response in different spectral regions.