Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
This work addresses the need for reliable in-line size monitoring in polymerization processes, representing an incremental improvement over existing methods.
The researchers tackled the problem of accurately determining polymer particle size from Raman spectroscopy, proposing three machine learning workflows using diffusion maps and achieving promising predictions with conformal autoencoders outperforming state-of-the-art methods.
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.