Quality evaluation of olive oil by statistical analysis of multicomponent stable isotope dilution assay data of aroma active compounds

An instrumental method for the evaluation of olive oil quality was developed. Twenty-one relevant aroma active compounds were quantified in 95 olive oil samples of different quality by headspace solid phase microextraction (HS-SPME) and dynamic headspace coupled to GC-MS. On the basis of these stable isotope dilution assay results, statistical evaluation by partial least-squares discriminant analysis (PLS-DA) was performed. Important variables were the odor activity values of ethyl isobutanoate, ethyl 2-methylbutanoate, 3-methylbutanol, butyric acid, E,E-2,4-decadienal, hexanoic acid, guaiacol, 2-phenylethanol, and the sum of the odor activity values of Z-3-hexenal, E-2-hexenal, Z-3-hexenyl acetate, and Z-3-hexenol. Classification performed with these variables predicted 88% of the olive oils? quality correctly. Additionally, the aroma compounds, which are characteristic for some off-flavors, were dissolved in refined plant oil. Sensory evaluation of these models demonstrated that the off-flavors rancid, fusty, and vinegary could be successfully simulated by a limited number of odorants

A-stability preserving perturbation of Runge–Kutta methods for stochastic differential equations

The paper is focused on analyzing the linear stability properties of stochastic Runge–Kutta (SRK) methods interpreted as a stochastic perturbation of the corresponding deterministic Runge–Kutta methods. In particular, we give a condition such that deterministic A-stability is automatically inherited by stochastic Runge–Kutta methods as mean-square A-stability. This issue provides classes of mean-square A-stable SRK methods straightforwardly