Peer-Reviewed Journal Details
Mandatory Fields
O'Farrell, M; Lewis, E; Flanagan, C; Lyons, WB; Jackman, N
Sensors And Actuators B-Chemical
Combining principal component analysis with an artificial neural network to perform online quality assessment of food as it cooks in a large-scale industrial oven
Optional Fields
artificial neural networks backpropagation principal component analysis food industry pattern recognition cooked food REFLECTANCE SPECTRA COLOR BEEF SPECTROSCOPY PREDICTION VISION IMAGES TISSUE PROBE
A sensor system utilising optical fibre sensing techniques is reported, which has been applied to the food industry in order to control the cooking process in a large-scale industrial oven. By monitoring a wide variety of products as they are cooked in the oven it has been possible to classify their cooking stage. This paper examines the application of principal component analysis, using karhunen loeve decomposition, to the spectral data from the sensor prior to application of pattern recognition through the use of an artificial neural network. Investigations have been carried out to ascertain trends in various products in order to design a general colour scale, which can classify several products using a single neural network. The food types discussed in this paper are steamed skinless chicken fillets, roast whole chickens, marinated chicken wings, sausages, pastry, breadcrumb coating and char-grilled chicken fillets. (c) 2005 Elsevier B.V. All rights reserved.
Grant Details