Binary encoded 2nd-differential spectrometry using UV-Vis spectral data and neural networks in the estimation of species type and concentration

Abstract

An approach to determining the type and concentration of a range of representative contaminants, chlorine, nitrate and ammonia in waste water, based on a three-stage scheme for processing data from ultraviolet and visible (UV-Vis) spectra, is described. In simulation in the laboratory, data for the study are derived from laboratory-based measurements of such spectra from mixtures of common chemical pollutants in water at levels around their legal limits and from mathematical models based on these measurements. Through the work, it is concluded that mathematical procedures alone, i.e. self-learning, are not currently effective, while classification based on a model for absorption spectra with prior knowledge of the expected chemistry in a particular water system under study, is more likely to be successful.