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@ARTICLE{Koronaki:1025659,
      author       = {Koronaki, Eleni D. and Kaven, Luise F. and Faust, Johannes
                      M. M. and Kevrekidis, Ioannis G. and Mitsos, Alexander},
      title        = {{N}onlinear {M}anifold {L}earning {D}etermines {M}icrogel
                      {S}ize from {R}aman {S}pectroscopy},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-03048},
      year         = {2024},
      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.},
      keywords     = {Machine Learning (cs.LG) (Other) / Signal Processing
                      (eess.SP) (Other) / FOS: Computer and information sciences
                      (Other) / FOS: Electrical engineering, electronic
                      engineering, information engineering (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2403.08376},
      url          = {https://juser.fz-juelich.de/record/1025659},
}