A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data ana lysis that is based on independent component analysis (ICA). The main advan tage of the new method is that it is able to separate the spectra of the co nstituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unkn own. The ICA based method is therefore particularly useful in identifying t he unknown components in a mixture as well as in estimating their concentra tions. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regress ion (PCR), multiple linear regression (MLR), and partial least squares (PLS ) as well as Fourier and wavelet transforms.