Quality control decisions with near infrared data
In this paper, as an alternative to multivariate regression methods, quality control tasks are posed as a decision problem: a sample is acceptable (this means that it follows its way to market) or not (then, it should be carefully examined according to laboratory procedures). The parameter to control is the content of water in samples of ampicillin trihydrate, based on near-infrared (NIR) spectra obtained from reflectance measurements. For modelling purposes, Genetic Inside Neural Network (GINN) is used. GINN is a neural network-based tool designed to perform the best possible decision by means 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 Science B.V. All rights reserved.
quality control nir modelling neural networks type-ii error genetic algorithms discrimination type-i error