Journal
TALANTA
Volume 59, Issue 4, Pages 735-749Publisher
ELSEVIER
DOI: 10.1016/S0039-9140(02)00615-X
Keywords
differential pulse anodic stripping voltammetry; multivariate calibration; fast wavelet transform; variables selection
Categories
Ask authors/readers for more resources
Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible. (C) 2002 Elsevier Science B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available