Ueber L. v. Uslar's und J. Erdmann's Methode zur Nachweisung der Alkaloïde

Analytical and Bioanalytical Chemistry -     

Ethanol/water solutions as certified reference materials for breath alcohol analyzer calibration

The Federal Institute for Materials Research and Testing (BAM), Germany, has issued a series of large volume ethanol in water certified reference materials (CRMs), primarily developed for the calibration of evidential breath alcohol analyzers in Germany. The certified parameter is the ethanol mass concentration at 20 °C. When used in a wet bath simulator, the solutions deliver gas samples that meet the requirements set by the Organization of Legal Metrology for calibration of breathalyzers. The materials were prepared gravimetrically by spiking of ethanol into water in single 5 L units. A complete uncertainty budget for the preparation process has been established. The purity of the commercial ethanol stock solution was identified to be the main source of uncertainty. For stability and homogeneity measurements and for the verification of the gravimetric mass concentration of the CRMs, a robust high-precision gas chromatography, with flame-ionization detection method for ethanol determination in aqueous samples was developed and validated. The good performance of this method has been demonstrated in several international comparisons organized by the Consultative Committee for Amount of Substance—Metrology in Chemistry at the International Bureau of Weights and Measures.     

On the discovery of events in EEG data utilizing information fusion

One way to tackle brain computer interfaces is to consider event related potentials in electroencephalography, like the well established P300 phenomenon. In this paper a multiple classifier approach to discover these events in the bioelectrical signal and with them whether or not a subject has recognized a particular pattern, is employed. Dealing with noisy data as well as heavily imbalanced target class distributions are among the difficulties encountered. Our approach utilizes partitions of electrodes to create robust and meaningful individual classifiers, which are then subsequently combined using decision fusion. Furthermore, a classifier selection approach using genetic algorithms is evaluated and used for optimization. The proposed approach utilizing information fusion shows promising results (over 0.8 area under the ROC curve).     

Ueber die quantitative chemische Bestimmung der Milchbestandtheile

Ohne ZusammenfassungSt. Petersburg, 10. Februar 1887.