Akademik Veri Yönetim Sistemi
Fabad Journal of Pharmaceutical Sciences, cilt.38, sa.3, ss.159-165, 2013 (Scopus)
As it is known, the basis of the modern analytical chemistry is the instrumental analysis methods based on the evaluation of analytical signals such as spectra, chromatograms, kinetic curves and others obtained from instruments. Nowadays, the traditional evaluation of analytical signals may not always provide the desired results for the chemical and pharmaceutical analysis, where most of the analysis processes is hyper complex. Hence, combined application of conventional instrumental methods and some chemometric signal processing methods can be necessary for the analysis of complex systems. As a result, conventional analysis techniques coupled with signal processing tools enhance their ability of resolution, separation and analysis tremendously. In this context, several signal processing tools have been developed for many application areas from data analysis to data compression. One of the newest additions has been wavelets. Wavelet transform (WT) can be classified into two categories; discrete wavelets transform and continuous wavelets transform. WT approach is a powerful signal processing tool for data reduction, denoising, baseline correction and resolution of overlapping spectra. In our previous studies, the WT signal processing tools in combination with conventional spectral analysis techniques were applied to the analysis of drugs in multicomponent samples. Very recently, fractional wavelet transform was successfully applied to increase the lower signal content and to reduce the spectral data length for the drug analysis. In this review I will give some typical applications of the wavelet transforms to spectrophotometric, voltammetric and chromatographic signals for the analysis of drug substances.