A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging.
In hyperspectral analysis, PLS-discriminant analysis (PLS-DA) is being increasingly used in conjunction with pure spectra where it is often referred to as PLS-Classification (PLS-Class). PLS-Class has been presented as a novel approach making it possible to obtain qualitative information about the distribution of the compounds in each pixel using little a priori knowledge about the image (only the pure spectrum of each compound is needed). In this short note it is shown that the PLS-Class model is the same as a straightforward classical least squares (CLS) model and it is highlighted that it is more appropriate to view this approach as CLS rather than PLS-DA. A real example illustrates the results of applying both PLS-Class and CLS.
Department of Food Science, Quality and Technology, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark.
José Manuel
JM
Amigo
Carsten
C
Ravn
Neal B
NB
Gallagher
Rasmus
R
Bro
D000465:Algorithms; D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013057:Spectrum Analysis / Q000379:methods