Analysis of bioremediation respirometric data using wavelets

Abstract

The study of biodegradation using respirometry generates an enormous quantity of data, with several millions of registers for each variable. We have been treating this enormous amount of information using several mathematical techniques. The first step is always the filtration of the data in order to eliminate anomalies strange to the process, such as voltage breakages. The length of the data can be reduced using conventional statistical methodologies or by using wavelets or by combination of both. We have been applying wavelet analysis to signals generated by the respirometry of biodegradation with three different purposes: (i) as a method of data filtration or denoising that keeps the inner core structure of the information without aliasing; (ii) as an interpretation tool; (iii) to detect variation patterns at smaller scales. The synthesized signals can be subsequently used to create digital data-driven mathematical models, either single input-single output or multiple input-multiple output, using the tools of the system identification theory.