Evolving Chemometric Models - A New Paradigm for Handling Dynamic (Stream-based) Calibration in Chemical Industry

Description

The presenter will conceive a new paradigm in the calibration and design of chemometric models from (FT-)NIR spectra. Opposed to batch off-line calibration through the usage of classical statistical methods (such as PLSR, PCR and several extensiond) or more general machine learning based methods (such as support vector machines, neural networks, fuzzy systems), evolving chemometric models can serve as core engine for addressing the incremental updating of calibration models fully automatically in on-line or even in-line installations. Such updates may become indispensable whenever a certain system dynamics or non-stationary environmental influences cause significant changes in the process. Typically, models trained in batch off-line mode then become outdated easily, leading to severe deteriorations of their quantification accuracy, which may even badly influence the (supervision of the) whole chemical process. An approach how to update chemometric models quickly and ideally with lowest possible costs in terms of additional target measurements will be presented in this talk. It will be based on PLS-fuzzy models where the latter are trained based on the score space obtained through the latent variables. This leads to a new form of a non-linear PLSR with embedded piece-wise local predictors, having granular characteristics and even offering some interpretability aspects.

Period	13 Sept 2017
Event title	7th International Chemometrics Research Meeting
Event type	Conference
Location	NetherlandsShow on map