Water discrimination based on the kinetic variations of AgNP spectrum

Abstract

The assessment of water quality and its classification have considerable importance on public health. This requires monitoring of a wide range of physical, chemical and biological parameters. Here, an array of sensors composed of absorbances in different wavelengths in a kinetic process was used for classification. The data were obtained in the kinetic absorbance variations of silver nanoparticles (AgNPs) in the presence of different mineral waters. Spectral variations with time for each water sample were vectorized, and the matrix composed of these vectors was analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA) as unsupervised clustering methods. The distinct clusters of nine different water samples were obtained using PCA and clustering by HCA resulted in an error rate of only 14.8%, which corresponds to misclassification of 4 water samples out of 27. The ability of the method for the discrimination of water samples using AgNP as the sole reagent can be attributed to the high dimensionality of data and the influence of the chemical environment in each water sample on the absorbance variations of AgNPs.