Open AccessDATA ACQUISITION AND DATA PROCESSING CHALLENGES IN HEAVY METAL MEASUREMENTS
Author(s) -
José Miguel Pereira,
Ricardo Salgado
Publication year - 2015
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.14.3.812
Subject(s) - outlier , computer science , data processing , gaussian , curve fitting , standard deviation , data point , signal processing , data mining , data acquisition , system of measurement , mean squared error , gaussian function , algorithm , statistics , artificial intelligence , mathematics , machine learning , digital signal processing , physics , quantum mechanics , astronomy , computer hardware , operating system
Water quality is a key factor to preserve human life quality, as well as, environmental and biological ecosystems. This paper highlights specific issues related with the acquisition and data processing of heavy metal measurement systems. Between the main challenges related with this kind of measurements, such as the ones related with the very low signal amplitudes to be measured, since very low concentrations, in the order of tens of p.p.b., of some heavy metals, can be very dangerous for human life and for ecosystems sustainability. Additional challenges, that are associated with online heavy metals measurements, are related with the capability to obtain accurate results using a low number of measurement points. Thus, the main goal of this paper is a comparison of different data processing algorithms that can be used to improve heavy metal measurement accuracy when a segmented voltammetric voltage scan is performed with a low number of measurement points. Regarding data processing of the measurement data, B-Spline, Gaussian and artificial neural network based techniques are compared with traditional least mean square techniques based on polynomial curve fitting. The performance of each technique is evaluated in terms of the required number of measurement points, for a given root mean square deviation between curve fitted and experimental data. A brief comparison of the different techniques, in terms of insensitivity to errors caused by measurement data outliers, is also presented.