Quality control decisions with near infrared data

M. S. Sánchez, E. Bertran, L. A. Sarabia, M. C. Ortiz, M. Blanco, J. Coello

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

Abstract

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. Copyright (C) 2000 Elsevier Science B.V.
Original languageEnglish
Pages (from-to)69-80
JournalChemometrics and Intelligent Laboratory Systems
Volume53
Issue number1-2
DOIs
Publication statusPublished - 13 Nov 2000

Keywords

  • Discrimination
  • Genetic algorithms
  • Modelling
  • Neural networks
  • NIR
  • Quality control
  • Type-I error
  • Type-II error

Fingerprint Dive into the research topics of 'Quality control decisions with near infrared data'. Together they form a unique fingerprint.

  • Cite this