Multivariate detection capability using a neural classifier for nonselective signals

Abstract

A new methodology is proposed based on a neural network to determine the detection capability of an analytical procedure, in complex matrices, with the evaluation of the probability of false detection, α, and false nondetection, β, according to the ISO norms. This methodology is designed for first or greater order signals for which there is currently no procedure with these characteristics, which makes it difficult to use these signals in analytical procedures standardized according to the ISO norm. The procedure consists of: (i) an experimental design suited to the increase in analyte to be detected from a threshold level; (ii) a homogenisation of the multivariate signals by a Piecewise Direct Standardization (PDS) transformation; (iii) the training of a neural network with stochastic learning, Genetic Inside Neural Network (GINN), which optimises α and β directly. The procedure was applied to the polarographic determination of Tl(I)/Pb(II) mixtures and indomethacin/tenoxicam mixtures. In the first case one can assure the detection of 1 mM (threshold: 12 mM) with α and β less than 5% for both metals. While for tenoxicam it is possible to detect less than 10% of 12 mM (threshold) with α<10% and β<5%, for indomethacin one can assure less than 10% of 86 mM (threshold) with α and β less than 5%.