O2‐PLS for qualitative and quantitative analysis in multivariate calibration

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A review on machine learning principles for multi-view biological data integration (2016, methodology): “O2-PLS further generalizes this idea in a predictive model in both ways, namely, X$ Y, by modeling the Y-orthogonal variation in X, the X-orthogonal variation in Y and the X-Y joint covariation [122, 123].”
OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification (2006, methodology): “Further details of the OPLS algorithm are described by Trygg [15,22].”
Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review (2012, background): “[146]), Kernel OPLS [147], Projected OSC, Piecewise OSC [148], external parameter orthogonalization (EPO) [149], error”
Prediction and spectral profile estimation in multivariate calibration (2004, background): “The part of the unknown variation that is correlated to Y will affect the pure profile estimates for both direct and indirect calibration, as discussed in Trygg [17, Section 2.3].”
Some theoretical properties of the O‐PLS method (2004, methodology): “Since then, numerous algorithms have been developed, as reviewed in References [5–12].”