Extraction of pure component spectrum from mixture spectra containing a known diluent

Multivariate data analysis techniques are widely used in getting better insight into the processes in the fields like chemometrics, speech processing, biomedical signal processing and astronomy. In the present study, the problem of extracting the spectrum of a pure component from Near Infrared (NIR) mixture spectra containing a known diluent is tackled. Different multivariate data analysis methods such as Ordinary Least Square (OLS), Principal Component Regression (PCR) and Non Negative Matrix Factorization (NMF) are modified to solve the problem. It is shown that including partial knowledge such as the spectrum of the known diluent in the data analysis techniques, accounting for errors in the absorbance measurements, and imposing non-negativity constraints on absorbance and concentrations estimates, results in better estimation of the pure component spectrum. © IFAC.