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Abstract. Simple, sensitive and selective methods are proposed for the spectrophotometric determination of drugs, viz., tetracycline HC1, nifurtimox, ethionamide, propranolol HC1 and isonicotinic acid hydrazide, based on their reactivity with N-bromosuccinimide (NBS). The method involves the addition of excess NBS of known concentration in the presence of 0.25 M HC1 and the unreacted NBS is determined by the measurement of the decrease in the absorbance of the dye celestine blue (2m,x 540 rim), which was found to be the most suitable of several dyes tested. This method was applied for the determination of drug contents in pharamaceutical formulations and enabled the determination of the drugs in microgram quantities (0.2 to 5 gg/ml). Standard deviations were typically ~<0.5mg per dose (RSD 0.1-1.0%). No interferences were observed from excipients and the validity of the method was tested against reference method. Recoveries were 99.8-101.1%.

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A simple and sensitive method is proposed for the spectrophotometric determination of drugs, viz. isoniazid, trimeprazine tartarate, pyridoxine HCI and captopril based on their reactivity with potassium bromate. The method involves the addition of excess KBt03 of known concentration in the presence of 0.7M HCl and the unreacted KBr03 is determined by the measurement of the decrease in the

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In this study, we apply the non-linear dynamical systems theory for the assessment built on recurrence-quantification analysis technique for characterizing—differentiating non-linear electro encephalograph (EEG) signals dynamics. The technique offers convenient quantifiable data plus information over normal, tumultuous, or probability and statistical stochastic properties of inherent systems dynamics theory. The R.Q.A-established processes as the quantifiable mathematical features of non-linear electroencephalograph signals dynamics. Average amount of mutual information (AAMI) applied to compute highly applicable feature-manifestation sub-sets out of R.Q.A-built centered-features. The chosen features were then fed into the computer using artificial intelligence based neural net-works for clustering the data of encephalograph-signals to identify ictic(i.e.,ictal), inter ictal, followed by state of controls. The study is implemented by validating R.Q.A with a data base for various issues of categorization. Results showed that the combination of five selected features created on AAMI attained the precision of100% and proves dominance of R.Q.A. Nonlinear dynamical control systems theory and analysis techniques centered on R.Q.A can be used as an appropriate methodology for distinguishing the non-linear systems dynamics of encephalograph signals data also epileptic seizures tracing.