Doehlert Design-desirability Function Multi-criteria Optimal Separation Of 17 Phenolic Compounds From Extra-virgin Olive Oil By Capillary Zone Electrophoresis.

In Brazil, the consumption of extra-virgin olive oil (EVOO) is increasing annually, but there are no experimental studies concerning the phenolic compound contents of commercial EVOO. The aim of this work was to optimise the separation of 17 phenolic compounds already detected in EVOO. A Doehlert matrix experimental design was used, evaluating the effects of pH and electrolyte concentration. Resolution, runtime and migration time relative standard deviation values were evaluated. Derringer's desirability function was used to simultaneously optimise all 37 responses. The 17 peaks were separated in 19min using a fused-silica capillary (50μm internal diameter, 72cm of effective length) with an extended light path and 101.3mmolL(-1) of boric acid electrolyte (pH 9.15, 30kV). The method was validated and applied to 15 EVOO samples found in Brazilian supermarkets.

Beam-energy Dependence Of Charge Separation Along The Magnetic Field In Au+au Collisions At Rhic.

Local parity-odd domains are theorized to form inside a quark-gluon plasma which has been produced in high-energy heavy-ion collisions. The local parity-odd domains manifest themselves as charge separation along the magnetic field axis via the chiral magnetic effect. The experimental observation of charge separation has previously been reported for heavy-ion collisions at the top RHIC energies. In this Letter, we present the results of the beam-energy dependence of the charge correlations in Au+Au collisions at midrapidity for center-of-mass energies of 7.7, 11.5, 19.6, 27, 39, and 62.4 GeV from the STAR experiment. After background subtraction, the signal gradually reduces with decreased beam energy and tends to vanish by 7.7 GeV. This implies the dominance of hadronic interactions over partonic ones at lower collision energies.

Comparison Between Automated And Microscopic Analysis In Body Fluids Cytology.

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Separation Of Glycosidic Catiomers By Twim-ms Using Co2 As A Drift Gas.

Traveling wave ion mobility mass spectrometry (TWIM-MS) is shown to be able to separate and characterize several isomeric forms of diterpene glycosides stevioside (Stv) and rebaudioside A (RebA) that are cationized by Na(+) and K(+) at different sites. Determination and characterization of these coexisting isomeric species, herein termed catiomers, arising from cationization at different and highly competitive coordinating sites, is particularly challenging for glycosides. To achieve this goal, the advantage of using CO2 as a more massive and polarizable drift gas, over N2 , was demonstrated. Post-TWIM-MS/MS experiments were used to confirm the separation. Optimization of the possible geometries and cross-sectional calculations for mobility peak assignments were also performed. Copyright © 2015 John Wiley & Sons, Ltd.

Preparative separation of flavonoids from the medicinal plant Davilla elliptica St. Hill. by high-speed counter-current chromatography

High-speed counter-current chromatography (HSCCC) is a major tool for the fast separation of natural products from plants. It was used for the preparative isolation of the flavonoid monoglucosides present in the aerial parts of the Davilla elliptica St. Hill. (Dilleniaceae). This species is used in Brazilian folk medicine for the treatment of gastric disorders. The optimum solvent system used was composed of a mixture of ethyl acetate-n-propanol-water (140:8:80, v/v/v) and led to a successful separation of quercetin-3-O-alpha-L-rhamnopyranoside and myricetin-3-O-alpha-L-rhamnopyranoside in approximately 3.0 hours with purity higher than 95%. Identification was performed by ¹H NMR, 13C NMR and HPLC-UV-DAD analyses.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.