Implementation of the Discrete Wavelet Transform Used in the Calibration of the Enzymatic Biosensors

The extensive use of insecticides in modern agriculture has raised serious public concern regarding the environment and food safety, and considerable efforts have been devoted to the development of highly sensitive detection methods (Bachmann et al., 2000; Istamboulie et al., 2007). Biosensors have been described for many years (Andreescu and Marty, 2006a) as good candidates to replace or complement conventional analytical methods, as they can provide real-time qualitative information about the composition of a sample with minimum treatment. In recent years, biosensors have played an important role in the determination of pesticides, because the compounds used in its composition have anticholinergic properties and therefore strongly inhibit cholinesterase enzymes. Evaluation of cholinesterase activity is the crucial factor in the construction of biosensors, however, in the case of multicomponent samples (more common in reality) is absolutely essential include appropriate data processing tools to find relationships between the biosensor responses and the measured data. In most cases, it is necessary a first data pretreatment step in order to explore and validate these obtained information (Ehrentreich, 2002). Many applications related with the use of biosensor responses entail data interpretation problem related to: (1) noisy records due to temperature changes; (2) data acquisition noise present in records, (3) presence of interference signals in the biosensor response mainly contaminated by signals coming for the electrochemical equipment i.e. potentiostats, magnetic stirrings and thermostats (Cai and Harrington, 1998; Zanchettin and Ludermir, 2007): (4) according with the inhibition method the responses can be slow which implies signals with information in low frequencies and a large number of samples per essay (Arduini et al., 2010). In this way, different data processing strategies have been proposed in order to achieve better interpretation models and discard irrelevant content coming from original data. During the last decade, Wavelet Transform (WT) has been widely employed in signal processing analysis were denoising and compression is an important step in the data mining

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