Treffer: Online pattern recognition, using ANN and SOM, to determine quality during the cooking process in the food industry

Title:
Online pattern recognition, using ANN and SOM, to determine quality during the cooking process in the food industry
Authors:
SHERIDAN, Cormac, O'FARRELL, Marion, LEWIS, Elfed, FLANAGAN, Colin, JACKMAN, Nick
Source:
IEE Irish signals and systems conference 2005 (Dublin Ireland, 1-2 September 2005)IEE conference publication. :249-253
Publisher Information:
London: Institution of Electrical Engineers, 2005.
Publication Year:
2005
Physical Description:
print, 3 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Electronics, Electronique, Electrical engineering, Electrotechnique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Electronique, Electronics, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Propriétés des circuits, Circuit properties, Circuits optiques et optoélectroniques, Optical and optoelectronic circuits, Optique intégrée. Fibres et guides d'onde optiques, Integrated optics. Optical fibers and wave guides, Réseaux neuronaux, Neural networks, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Traitement du signal, Signal processing, Reconnaissance des formes, Pattern recognition, Affichage polychrome, Color display, Visualización policromo, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse composante principale, Principal component analysis, Análisis componente principal, Autoorganisation, Self organization, Autoorganización, Boucle anticipation, Feedforward, Ciclo anticipación, Capteur fibre optique, Fiber optic sensors, Capteur mesure, Measurement sensor, Captador medida, Capteur optique, Optical sensor, Captador óptico, Caractéristique spectrale, Spectral data, Característica espectral, Carte autoorganisatrice, Self-organising feature maps, Classification automatique, Automatic classification, Clasificación automática, Classification signal, Signal classification, Extraction caractéristique, Feature extraction, Fibre optique, Optical fiber, Fibra óptica, Industrie alimentaire, Food industry, Industria alimenticia, Lumière réfléchie, Reflected light, Luz reflejada, Méthode statistique, Statistical method, Método estadístico, Optique intégrée, Integrated optics, Optica integrada, Rayonnement visible, Visible radiation, Radiación visible, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Réseau neuronal, Neural network, Red neuronal, Système en ligne, On-line systems, Traitement signal, Signal processing, Procesamiento señal, 0707D, 0760V
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
Food Design Application Ltd., Newtown, Castletroy, Limerick, Ireland
ISSN:
0537-9989
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17565666
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Electronics

Telecommunications and information theory
Accession Number:
edscal.17565666
Database:
PASCAL Archive

Weitere Informationen

This paper reports on two methods of classifying the spectral data from an optical fibre based sensor system as used in the food industry. The first method uses a feed-forward back-propagation Artificial Neural Network while the second method involves using Kohonen Self-Organising Maps. The sensor monitors the food colour online as the food cooks by examining the reflected light, in the visible region, from both the surface and the core of the product. The combination of using Principal Component Analysis (PCA) and backpropagation neural networks has been successfully investigated previously. In this paper, results obtained using this method are compared with results obtained using a Self-Organising Map trained on the Principal Components. PCA is performed on the reflected spectra, which form a colourscale - a scale developed to allow the quality of several products of similar colour to be monitored i.e. a single classifier is trained, using the colourscale data that can classify several food products. The results presented show that both classifiers perform well.