PERFORMANCE OF AN ELECTRONIC NOSE FOR QUALITY ESTIMATION OF GROUND MEAT

An electronic nose is described, which consists of a gas sensor array combined with a pattern recognition routine. The sensor array used consists of ten metal-oxide-semiconductor field effect transistors with gates of catalytically active metals. It also contains four commercially available chemical sensors based on tin dioxide, so-called Taguchi sensors. In some studies, a carbon dioxide monitor based on infrared absorption is also used. Samples of ground beef and pork, stored in a refrigerator, have been studied. Gas samples from the meat were then led to the sensor array, and the resulting patterns of sensor signals were treated with pattern recognition software based on an artificial neural network as well as with an algorithm based on an abductory induction mechanism. When using all sensors for learning, the two nets could predict both type of meat and storage time quite well. Omitting the carbon dioxide monitor, both nets could predict type of meat, but storage time not so well. Finally, it is also shown how a net based on unsupervised training could be used to predict storage time for ground beef.