Signal analysis of voltammetric data series for water quality tests and classification

The growing interest into multi-sensor systems able to determine general attributes of a process under monitoring, has also involved the qualitative analysis of liquids; various methodologies to develop taste sensors, often referred to as "e-tongues" have been presented in the literature. The fundamental idea of this paper is to investigate how an adequate signal processing approach applied to a mature and affordable sensor technique (voltammetry) can address the issue of extracting an aggregate chemical information, useful to characterize the liquid under measurement. The methodological approach to the processing of the signal, an application experiment, and the test set which has been built for the experiment are described here. In the proposed approach, dimensionality reduction is obtained using the compaction properties of DCT (discrete cosine transform), where the transform is followed by an iterative orthogonalization procedure for the feature extraction step; the capability of discriminating between different samples is also discussed via representing each collected sequence in a low dimensionality feature space