Ensemble Learning for Chemical Sensor Arrays (original) (raw)
2004, Neural Processing Letters
Electrochemical sensors, like ion-selective field transistors (ISFET), are electronic devices that merge solid-state electronic technology with chemical sensors so as to be sensitive to the concentration of a particular ion in a solution. However, as it has been previously reported, their response does not only depend on a single ion but also is affected by several interfering ions found in the solution to be measured. These interfering ions can be considered as noise and consequently, a post-processing stage that increases the SNR is obligatory. Our work shows how ensemble learning methods could be used in an array of chemical sensors in order to deal with this problem. In particular, we introduce a novel neural learning architecture for ISFET arrays, which employ ISFET models as prior knowledge. The proposed ensemble learning systems are RBF-like solutions based on bagging and optimal linear combination. Several experimental results are included, which demonstrate the interest and viability of the proposed solution.
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In this paper, we present the design of electronic tongue system for multi-ion sensing applications. The ion-sensitive field-effect transistor (ISFET) detects the concentration of a particular ion in aqueous solution. However, when the given chemical solution contains two or more ions, the ISFET sensor can only provide the combined concentration of ions. In this end, our electronic tongue included a blind source separation (BSS) method of independent component analysis (ICA) to process the ISFET signals and to extract the concentrations of individual ions in the solution. The results of ISFET modeling based on fixed interference method (FIM) serve as a priori knowledge to help solve this blind source problem. Experiments are conducted on this electronic tongue system using aqueous solution containing hydrogen and sodium ions flowing through the array of dual H+ ISFET devices. The results of ICA processing successfully determined the concentration of hydrogen ions amidst the presence of sodium ions. This capability of ion separation allows us to move towards the development of smart electronic tongue systems for environmental and water quality monitoring.
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