TY - JOUR
T1 - Using a reflection-based optical fibre system and Neural Networks to evaluate the quality of food in a large-scale industrial oven
AU - O'Farrell, Marion
AU - Lewis, Elfed
AU - Flanagan, Colin
AU - Lyons, William B.
AU - Jackman, N.
PY - 2004/9/21
Y1 - 2004/9/21
N2 - 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.
AB - 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.
KW - Artificial Neural Network
KW - Back Propagation Learning
KW - Colour classification
KW - Food processing industry
KW - Optical fibre sensor
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=4544361161&partnerID=8YFLogxK
U2 - 10.1016/j.sna.2004.04.051
DO - 10.1016/j.sna.2004.04.051
M3 - Article
AN - SCOPUS:4544361161
SN - 0924-4247
VL - 115
SP - 424
EP - 433
JO - Sensors and Actuators, A: Physical
JF - Sensors and Actuators, A: Physical
IS - 2-3 SPEC. ISS.
ER -