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 analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.
Chen
J
J
Department of Chemical Engineering, The University of Leeds, Leeds LS2 9JT, UK.
Wang
X Z
XZ
D000273:Adipose Tissue; D005504:Food Analysis; D005583:Fourier Analysis; D008055:Lipids / Q000032:analysis*; D008460:Meat / Q000032:analysis; D008956:Models, Chemical; D011506:Proteins / Q000032:analysis*; D013055:Spectrophotometry, Infrared / Q000379:methods; D013213:Starch / Q000032:analysis*; D014867:Water / Q000032:analysis*
D008055:Lipids; D011506:Proteins; D014867:Water; D013213:Starch