Nonlinear Manifold Learning Determines Microgel Size from Raman
  Spectroscopy
Eleni D. Koronaki Luise F. Kaven Johannes M. M. Faust Ioannis G. Kevrekidis Alexander Mitsos
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.