Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
Abstract
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross-linked polymer) samples in a diameter range of 208nm to 483 nm. The conformal autoencoders substantially outperform state-of-the-art methods and results for the first time in a promising prediction of polymer size from Raman spectra.
Authors 5
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Affiliation as printed
Faculté des Sciences , de la Technologie et de la Communication , Université de Luxembourg , Maison du Nombre , Avenue de la Fonte 6 , L-4364 Esch-sur-Alzette , Luxembourg
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Affiliation as printed
Process Systems Engineering (AVT.SVT) , RWTH Aachen University , Forckenbeckstr. 51 , 52074 Aachen , Germany
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Affiliation as printed
Process Systems Engineering (AVT.SVT) , RWTH Aachen University , Forckenbeckstr. 51 , 52074 Aachen , Germany
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Affiliation as printed
Department of Chemical and Biomolecular Engineering and
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