Seeding multivariate algorithms for spectral analysis, a data augmentation approach to enhance analytical performance

Seeding spectral datasets by augmenting the data matrix with either the full spectrum or selected spectral features in order to bias multivariate analysis towards the solution of interest is explored. It is demonstrated that such seeding can have a profound effect on the endpoint of the analysis. Us... View More