First derivative-supervised pattern recognition for the flow-injection spectrophotometric discrimination of s-triazines in water

Abstract

A new spectrophotometric method is presented for the automated differentiation of chloro- (0.5 to 5.0 mg/mL), thio- (0.5 to 5.0 mg/mL) and methoxy-triazines (1 to 10 mg/mL) in water samples. Classification models obtained by K-nearest neighbours, Soft Independent Modeling of Class Analogy, and Partial Least Squares-Discriminatory Analysis were constructed from zero order and first derivative absorption spectra as independent variables, in the spectral range from 210 to 270 nm. Binary responses were used as classifying variables (with/without certain group of triazines). With this dichotomous structure, parameters related to 2x2 contingency tables were used to evaluate the performance of the models. For tap and well water samples, sensitivity and selectivity values equal or higher than 50 % were obtained from autoscaled first derivative spectra, discriminated by Partial Least Squares-Discriminatory Analysis.