DocumentCode :
3119949
Title :
Principal component analysis and pattern recognition combined with visible spectroscopy in the classification of food quality
Author :
Farrell, M.O. ; Lewis, E. ; Flanagan, C. ; Lyons, W.B. ; Jackman, N.
Author_Institution :
Limerick Univ., Ireland
fYear :
2004
fDate :
24-27 Oct. 2004
Firstpage :
597
Abstract :
The online measurement of the colour of food internally and externally has already been shown to be an invaluable parameter in the process control of large industrial ovens. The system, described in this article is based on optical fibre technology is intended for accurate measurement of food colour. It employs artificial intelligence through the use of neural networks to make decisions regarding the cooking stage of the product. This paper examines the application of principal component analysis, using Karhunen Loeve decomposition, to the spectral data before applying the pattern recognition technique. With Karhunen Loeve decomposition it is possible to reduce the dimensions of this solution to a smaller subspace by only including significant data and thus eliminating redundant or highly correlated information. This method was tested on the following food products: steamed skinless chicken fillets, roast whole chickens, sausages, pastry, bread crumb coating and char-grilled chicken fillets.
Keywords :
Karhunen-Loeve transforms; colorimetry; colour; fibre optic sensors; food manufacturing; food processing industry; food products; food technology; neural nets; ovens; pattern recognition; principal component analysis; signal classification; visible spectra; Karhunen Loeve decomposition; artificial intelligence; bread crumb coating; char-grilled chicken fillets; correlated information; food quality classification; large industrial ovens; neural networks; online food colour measurement; optical fibre technology; pastry; pattern recognition; principal component analysis; process control; product cooking stage; redundant information; roast whole chickens; sausages; spectral data; steamed skinless chicken fillets; visible spectroscopy; Color; Electrical equipment industry; Food industry; Industrial control; Optical fibers; Ovens; Pattern recognition; Principal component analysis; Process control; Spectroscopy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensors, 2004. Proceedings of IEEE
Print_ISBN :
0-7803-8692-2
Type :
conf
DOI :
10.1109/ICSENS.2004.1426236
Filename :
1426236
Link To Document :
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