A comparative study on several algorithms for denoising of thin layer densitograms

This paper compares classical filtering techniques (Savitzky-Golay, Adaptive Degree Polynomial Filter, Fourier denoising, Butterworth and Chebyshev IIR filters) and the wavelet shrinkage (31 mother wavelets, 3 thresholding techniques and 11 decomposition levels). These techniques were compared with the original noisy signal and the reference signal, denoised experimentally by the averaging of 64 measurements. The best similarity to reference signal was observed in the case of filters, but they slightly oversmoothed the signal. The wavelet shrinkage gave less denoised signals. The significant influence of the thresholding technique and decomposition level was observed (the best conditions-the level 2 or 3 and soft thresholding). Changing of the mother wavelet almost does not change the result, which is similar to the earlier literature findings. The presented results can be used as general recommendations for denoising of the densitometric fingerprints before applying further chemometric algorithms. The best choices are: Savitzky-Golay filter of appropriate (optimized against the autocorrelation) window width or wavelet shrinkage with Haar wavelet, soft thresholding and high decomposition level.