Peer-Reviewed Journal Details
Mandatory Fields
O'Farrell, M; Lewis, E; Flanagan, C; Lyons, WB; Jackman, N
Sensors And Actuators A-Physical
Using a reflection-based optical fibre system and Neural Networks to evaluate the quality of food in a large-scale industrial oven
Optional Fields
optical fibre sensor Artificial Neural Network pattern recognition colour classification food processing industry back propagation learning
A sensor system utilising optical fibre sensor techniques is reported for the online examination of food colour within large-scale industrial ovens. The various food products that are processed within these ovens are interrogated by employing spectroscopic techniques, with the resulting spectral patterns being interrogated and classified with the aid of Artificial Neural Networks. A system based on pattern recognition has been developed which is capable of classifying the various colours, that can occur for each product into those which are favourable and those that are not optimum. This information can be used by the producer to control the cooking process online and optimise food quality. Spectral results are presented for a number of different food products, such as Sausages, Whole Chickens, Fresh Beef Burgers, High Cereal Patti Burgers and Marinated Chicken Thighs and Wings. This range of products was selected by Food Design Application Ltd. and was considered an adequate representation of the most popular food products cooked in the oven by their leading customers. A Neural Network was developed for each product and successfully classified the products into the following stages; raw, light correct and dark. (C) 2004 Elsevier B.V. All rights reserved.
Grant Details