Errors in oxygen tension measurements caused by halothane.
Halothane, independent of oxygen tension, increased the signal of the gold/silver-silver chloride microelectrode. The output of microelectrodes at different oxygen tensions in the presence of increasing amounts of halothane has been measured. Halothane causes an increase in the electrode signal which is proportional to its concentration. This effect results from the polarographic reduction of halothane. It is concluded that the gold/silver-silver chloride microelectrode cannot be used to measure oxygen tension during halothane anaesthesia.
The gas chromatographic estimation of halothane in blood using electron capture detector unit.
A method for quantitative estimation of low concentrations of halothane in blood by gas chromatography and electron capture detection has been described. The mean recovery of halothane from blood in the range of 7.10–26.12 mg/100 ml was 96.98% (SD 227). The mean of the standard deviations of duplicate extractions was 0.87. With low blood halothane concentrations 112.54–323.00 / * μg/100 ml, the mean per cent recovery was 107.41% (SD 5.18) and the mean of the standard deviations of duplicate extractions was 2.89.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.