Performance of an electronic nose for quality estimation of ground meat

Abstract

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 t h e resulting patterns of sensor signats were treated with pattem 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.

Electronic nose and neural network use for the classification of honey

“…A number of potential applications of the electronic nose in food industry have been reported, such as for quality estimation of ground meat (Winquist et al, 1993), detection of boar taint in meat (Bourrounet et al, 1995), detection of gender differences in meat products (Berdague and Talou, 1993), estimation of fish freshness (Scweizer-Berberich et al, 1994;Winquist et al, 1995), evaluation of shelf life of fresh vegetables (Riva et al, 2001), to follow beer production (Pearce et al, 2003) and to predict the degree of mouldy/ musty odour in cereal (Borjesson et al, 1996).…”

Application of Artificial Neural Networks in Meat Production and Technology

“…These involve identification of animal species in ground meat mixtures (Winquist et al, 1993) or fat tissue (Beattie et al, 2007) LD -longissimus dorsi; TB -triceps brachii; R 2 -coefficient of determination; r -correlation coefficient; P -prediction; C -classification; VIS -visible; NIR -near infrared; IR -infrared. (Hwang et al, 1997), detection of RN -phenotype in pigs (Josell et al, 2000), the "walk-through" weighing of pigs (Wang et al, 2008), the efficiency of ANN for visual guidance of pig evisceration at the slaughter line (Christensen et al, 1996) and the use of ANN for the processing control of meat products (Eklöv et al, 1998;Ibarra et al, 2000;Santos et al, 2004).…”

“…Again, based on the literature reports, supervised learning strategy of ANN (BP-ANN, RBF) was applied in the majority of studies. There were also a few studies where unsupervised learning has been tested (Winquist et al, 1993;Beattie et al, 2007). A bibliographic overview given in Table 4 demonstrates the efficiency and successful classification rate of ANN based systems.…”

Malodor Detection Based on Electronic Nose

“…The Enose has been applied in a wide range of animal products, including: animal flesh product (e.g. meat (Blixt & Borch, 1999;Panigrahi et al, 2006;Vernat-Rossi et al, 1996;Winquist et al, 1993;), fish and seafood (Alimelli et al, 2007;Huang et al, 2011;Kent et al, 2004;Lorwongtragool et al, 2011;Ólafsdóttir, et al, 1997Rajamäki et al, 2006;Zhang et al, 2009), dairy product (e.g. cheese and milk) (Ampuero & Bosset, 2003;Drake et al, 2003;Seregély et al, 2006) and egg product (Suman et al, 2007;Yongwei et al, 2009)).…”