Improving detection power in trace analysis using wavelet transform.

Environmental analysis most often is trace analysis. Therefore, the concentrations are commonly in the lower working range near the limit of detection of the corresponding analytical method. However, whenever the instrument's analytical noise is too large, it dominates the signal curves and analytes cannot be detected anymore. Furthermore, the evaluation of peaks with defined baselines is hindered very much. One possibility for de-noising is wavelet transform which is presented in this work. Different wavelet functions are applied and Symlet4 is suggested as the most powerful for analytical peaks that resemble Gaussian distribution curves, as it improves limits of detection by factors 6 to 7. The comparison of different wavelet functions has been carried out for two modern analytical scopes. At first, chromatograms are de-noised for the speciation of four arsenic compounds via the coupling of HPLC and ICP-MS. Secondly, the determination of cadmium is shown by HR-CS AAS, which is one of the most recently developed devices in atomic absorption spectrometry and allows the registration of three-dimensional spectra in order to investigate the spectral vicinity of analytical lines. On the basis of these investigations, we recommend using wavelet transform with Symlet4 for all analytical techniques which are resulting in similar signal curves.

Comparing different means of signal treatment for improving the detection power in HPLC-ICP-MS.

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.

Wavelet transform as a new approach to the enhancement of signal-to-noise ratio in anodic stripping voltammetry.

De-noising signals is a frequent aim achieved by signal processing in analytical chemistry. The purpose is to enable the detection of trace concentrations of analytes. The limit of detection is defined as the lowest amount of analyte that still causes signals greater than the background noise. Appropriate de-noising decreases only the noise and maintains the measurement signal, so that signal-to-noise ratios are enhanced. One adequate mean of signal processing for this purpose is wavelet transform, which still is not a common tool in analytical chemistry. In this paper, the ability of de-noising by wavelet transform is shown for measurements in anodic stripping voltammetry using a hanging mercury drop electrode. The calculation of limits of detection and signal-to-noise ratios on the basis of peak-to-peak noise is exercised to quantify the performance of de-noising. Furthermore, signal shape with regard of easing the application of base lines is discussed. Different wavelet functions are used, and the results are compared also to Fourier transform. Coiflet2 was found out to reduce noise by the factor of 330 and is proposed as the adequate wavelet function for voltammetric and similar signals.