Abstract
Multivariate curve resolution (MCR) techniques can recover the pure component information from the spectral mixture data. Assuming an underlying bilinear model, the data matrix is to be factorized in matrices of the pure component spectra and the associated concentration profiles. Typically, there are solution continua exist which can be represented by the area of feasible solutions (AFS). This article analyzes the ambiguity of the matrix factorization problem. Various methods are reviewed for the construction and the numerical approximation of the nonnegative factorizations. An overview on the history of the AFS up to recent developments is included. Important properties of the AFS are discussed. Several example problems are analyzed mainly by the FACPACK software.
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