The Black Hole Effect in MCR with Raffaele Vitale

Join a 46-minute webinar exploring the "black hole effect" in Multivariate Curve Resolution (MCR) presented by Raffaele Vitale. Delve into the limitations of Alternating Least Squares (ALS) in MCR, particularly when dealing with data structures that violate specific distributional assumptions. Examine how the presence of minor components and large datasets can lead to incorrect resolutions in MCR-ALS. Gain a comprehensive understanding of this deleterious phenomenon and its analogy to biased calibration in regression models with high-leverage outlying observations. Discover two effective solutions to overcome the black hole effect: data pruning and object weighting based on essentiality measures for curve resolution. Enhance your knowledge of chemometrics and machine learning techniques in this informative session hosted by Chemometrics & Machine Learning in Copenhagen.