title: A novel algorithm for linear multivariate calibration based on the mixed model of samples.
abstract: We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.
authors.affiliation: Institute of Analytical Science, School of Chemistry and Material Science, Northwest University, Xi'an 710069, PR China; Department of Chemistry & Chemical Engineering, Xi'an University of Arts and Science, Xi'an 710065, PR China.
authors.forename: Xuemei
authors.initials: X
authors.lastname: Wu
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Liu
authors.forename: Hua
authors.initials: H
authors.lastname: Li
keywords: Lagrange multiplier; Mixed model; Multivariate calibration; Partial least squares