title: Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression.
abstract: BACKGROUND
  Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients.
  
  
  RESULTS
  For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes.
  
  
  CONCLUSIONS
  Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data.
authors.affiliation: Technische Informatik, Universität Tübingen, Sand 13, D-72076 Tübingen, Germany ; Present address: Technische Informatik, Universität Leipzig, Augustusplatz 10, D-04109 Leipzig, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Bogdan
authors.affiliation: Technische Informatik, Universität Tübingen, Sand 13, D-72076 Tübingen, Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Brugger
authors.affiliation: Technische Informatik, Universität Tübingen, Sand 13, D-72076 Tübingen, Germany.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Rosenstiel
authors.affiliation: Institut für Organische Chemie, Universität Tübingen, Auf der Morgenstelle 18, D-72076 Tübingen, Germany.
authors.forename: Bernd
authors.initials: B
authors.lastname: Speiser
keywords: Diffusion coefficient; Gaussian process regression; Principal component analysis; Reaction mechanism; Support vector regression; Voltammetry