"When several dependent data are available for calibration, two approaches can be used in PLS regression: either properties are calibrated for one at a time (PLS1), or properties are calibrated at once (PLS2). In PLS1 model, the Y response consists of a single variable. When there is more than one Y response a separated model must be constructed for each Y response. In PLS2 model, responses are multivariate. PLS1 and PLS2 models provide different prediction set and PLS2 regression give better results than PLS1 regression only if Y variables are strongly correlated."
(Page 134 in: O. Galtier, O. Abbas, Y. Le Dréau, C. Rebufa, J. Kister, J. Artaud, N. Dupuy 2011. Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions. Vibrational Spectroscopy 55 (2011) 132–140)
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