CPACT Webinar on
Robust Constrained
Partial Least Squares: A Robust Integrated Algorithm for Multivariate
Regression in the Presence of Outliers, Interfering Analytes, and Structured
External Influences
Puneet Mishra, Wageningen
University and Research
1st October
2026 at 3pm (UK time)
Partial least squares (PLS)
regression is widely used for multivariate calibration in high-dimensional and
collinear settings. However, classical PLS relies on least squares optimization
and is therefore sensitive to anomalous observations, leverage points, and
structured spectral interferences. Robust PLS variants mitigate the influence
of outliers via alternative estimators or iterative reweighting, whereas
orthogonalization strategies such as external parameter orthogonalization (EPO)
aim to remove structured external variation.
These approaches are typically
applied independently, despite the frequent coexistence of outliers and
structured external variation in real spectroscopic data. We propose robust
constrained partial least squares (RC-PLS), a unified algorithm that integrates
iterative reweighting within each LV extraction step with constrained
orthogonalization of loading vectors against predefined structured external
variation. The proposed algorithm retains the computational structure of
classical PLS while improving robustness and interpretability.
Evaluation on NIR and Raman
datasets demonstrates enhanced prediction stability and reduced sensitivity to
both anomalous samples and structured external variation compared with standard
PLS. RC-PLS provides a coherent framework for constrained and robust latent
variables regression in real-world chemometric applications.
This webinar will last no longer
than one hour.
The webinar is free to attend and
is for CPACT members only.
Please register at https://universityofstrathclyde.webex.com/weblink/register/r0a23eefa765a2f61cfffebb296e5b469