A novel chemical image sensor consisting of integrated microsensor array chipsand pattern recognition

Abstract

A novel chemical image sensor developed for liquid component analysis is proposed in thispaper; using it, pH values ranging from 1 to 12 and six kinds of metal ion, namely Cu2+, Fe2+, Fe3+, Ca2+, Zn2+, andMg2+,can be detected qualitatively and quantitatively. The sensor applies theprinciples of optical chemistry and microfabrication technology to detectthe ion concentrations in the solution, and has the advantages of highsensitivity, reduced contamination, a lower sample volume required, andthe capability of detecting several indices at one time. Moreover, threemultivariate data analysis methods are suggested in the paper for treating theraw data acquired from the microbeads, and predicting the results. Thestudy demonstrates that the principal component analysis is capable ofclassifying six kinds of cation with success. Both partial least-squaresregression (PLS) and artificial neural networks (ANN) can be used tocompute the pH values quantitatively; furthermore, the PLS method has theadvantage of requiring fewer iteration steps than the ANN approach.