A variable differential consensus method for improving the quantitative near-infrared spectroscopic analysis

Consensus methods have presented promising tools for improving the reliability of quantitative models in near-infrared (NIR) spectroscopic analysis. A strategy for improving the performance of consensus methods in multivariate calibration of NIR spectra is proposed. In the approach, a subset of non-collinear variables is generated using successive projections algorithm (SPA) for each variable in the reduced spectra by uninformative variables elimination (UVE). Then sub-models are built using the variable subsets and the calibration subsets determined by Monte Carlo (MC) re-sampling, and the sub-model that produces minimal error in cross validation is selected as a member model. With repetition of the MC re-sampling, a series of member models are built and a consensus model is achieved by averaging all the member models. Since member models are built with the best variable subset and the randomly selected calibration subset, both the quality and the diversity of the member models are insured for the consensus model. Two NIR spectral datasets of tobacco lamina are used to investigate the proposed method. The superiority of the method in both accuracy and reliability is demonstrated.