Quality control decisions with near infrared data

In this paper, as an alternative to multivariate regression methods, qualit y control tasks are posed as a decision problem: a sample is acceptable (th is means that it follows its way to market) or not (then, it should be care fully examined according to laboratory procedures). The parameter to contro l is the content of water in samples of ampicillin trihydrate, based on nea r-infrared (NIR) spectra obtained from reflectance measurements. For modell ing purposes, Genetic Inside Neural Network (GINN) is used. GINN is a neura l network-based tool designed to perform the best possible decision by mean s of simultaneous optimisation of both type-I and type-II errors. Further, this training is made without imposing any condition on the distribution of data (nonparametric) and under nonlinear conditions. (C) 2000 Elsevier Sci ence B.V. All rights reserved.