title: Comparing different means of signal treatment for improving the detection power in HPLC-ICP-MS.
abstract: The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (inorganic As(III) and As(V) as well as organic dimethylarsinic acid and arsenobetaine) are to be determined in food and feeding stuff by HPLC-ICP-MS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Convolution with Gaussian distribution curves may be applied, but Fourier transform shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 6.
authors: Department of Environmental Analysis, Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University of Jena, Jena, Germany. simon.prikler@uni-jena.de
authors: Simon
authors: S
authors: Prikler
authors: Denis
authors: D
authors: Pick
authors: Jürgen W
authors: JW
authors: Einax
mesh_terms: D001152:Arsenicals / Q000032:analysis*; D002851:Chromatography, High Pressure Liquid / Q000295:instrumentation / Q000379:methods*; D057230:Limit of Detection; D013058:Mass Spectrometry / Q000295:instrumentation / Q000379:methods*
chemical_list: D001152:Arsenicals