Optimization of sample clean-up for the determination of small amounts of MOSH and MOAH in edible oils - method DGF C-VI 22 (20).

Methods for determining MOSH and MOAH in edible oils showed major problems with interlaboratory comparability of analytical results, especially in the lower concentration range below 10 mg/kg. However, a method with improved sensitivity and reproducibility is urgently needed to obtain a valid data basis for minimization efforts. To cope this problem a new method was created in 2020.

Determining MOSH and MOAH with High Sensitivity in Vegetable Oil─A New, Reliable, and Comparable Approach Using Online LC-GC-FID─Evaluation of Method Precision Data.

A method for the analysis of saturated mineral oil hydrocarbons (MOSH) and aromatic mineral oil hydrocarbons (MOAH) has been developed to improve interlaboratory precisions especially for amounts below 10 mg/kg. This approach was adopted as the standard method DGF C-VI 22 (20) of the German Society of Fat Sciences. Therefore, this method was evaluated on different edible oils containing a variety of interfering biogenic substances.

A Bayesian Approach to the Estimation of Parameters and Their Interdependencies in Environmental Modeling.

We present a case study for Bayesian analysis and proper representation of distributions and dependence among parameters when calibrating process-oriented environmental models. A simple water quality model for the Elbe River (Germany) is referred to as an example, but the approach is applicable to a wide range of environmental models with time-series output. Model parameters are estimated by Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling.

Symplectic Gaussian process regression of maps in Hamiltonian systems.

We present an approach to construct structure-preserving emulators for Hamiltonian flow maps and Poincaré maps based directly on orbit data. Intended applications are in moderate-dimensional systems, in particular, long-term tracing of fast charged particles in accelerators and magnetic plasma confinement configurations. The method is based on multi-output Gaussian process (GP) regression on scattered training data.

Gaussian Process Regression for Data Fulfilling Linear Differential Equations with Localized Sources.

Specialized Gaussian process regression is presented for data that are known to fulfill a given linear differential equation with vanishing or localized sources. The method allows estimation of system parameters as well as strength and location of point sources. It is applicable to a wide range of data from measurement and simulation.