Some recent developments in PLS modeling

By Svante Wold, Johan Trygg, A. Berglund, H. Antti. 2001

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Some theoretical properties of the O‐PLS method (2004, background): “One of the principal applications of NIR spectroscopy is to determine a linear relationship between a set of calibration data (spectral matrix X) and a set of concentrations or reference values of samples (response matrix Y) [1–3].”
Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations (2012, methodology): “It should be noted, however, that variance-based SEM employs PLS regression or variations of it for the calculation of the weights to be assigned to each indicator in a latent variable (Lohmöller, 1989; Wold et al., 2001).”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…model created can be reduced to the form of yˆ= bX (where, yîs the predicted numerical response, b is the loadings vector if there is a single response variable, and X is the input data vector) (Alsberg et al., 1998; Wold et al., 2001) and many good visualization and statistics tools are available.”
Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy (2016, unknown): “Further, this approach enables the modelling of several response variables simultaneously, while effectively addressing strongly collinear and noisy predictor variables (Wold et al., 2001).”