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Sensors 2015, 15(1), 1354-1364; doi:10.3390/s150101354

Improving the Performance of an Electronic Nose by Wine Aroma Training to Distinguish between Drip Coffee and Canned Coffee

1
Core Research Facilities for Basic Science, The Jikei University School of Medicine, Minato-ku, Tokyo 105-8461, Japan
2
Department of Cardiovascular Surgery, Tokyo Women's Medical University, Shinjuku-ku, Tokyo 162-8666, Japan
3
Department of Data Science, The Institute of Statistical Mathematics, Tachikawa, Tokyo 190-8562, Japan
*
Author to whom correspondence should be addressed.
Received: 15 October 2014 / Accepted: 4 January 2015 / Published: 12 January 2015
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Abstract

Coffee aroma, with more than 600 components, is considered as one of the most complex food aromas. Although electronic noses have been successfully used for objective analysis and differentiation of total coffee aromas, it is difficult to use them to describe the specific features of coffee aroma (i.e., the type of smell). This is because data obtained by electronic noses are generally based on electrical resistance/current and samples are distinguished by principal component analysis. In this paper, we present an electronic nose that is capable of learning the wine related aromas using the aroma kit “Le Nez du Vin,” and the potential to describe coffee aroma in a similar manner comparable to how wine experts describe wine aroma. The results of our investigation showed that the aromas of three drip coffees were more similar to those of pine and honey in the aroma kit than to the aromas of three canned coffees. Conversely, the aromas of canned coffees were more similar to the kit coffee aroma. In addition, the aromatic patterns of coffees were different from those of green tea and red wine. Although further study is required to fit the data to human olfaction, the presented method and the use of vocabularies in aroma kits promise to enhance objective discrimination and description of aromas by electronic noses. View Full-Text
Keywords: electronic nose; coffee; Le Nez du Vin; wine aroma; similarity; FF-2A; objective description electronic nose; coffee; Le Nez du Vin; wine aroma; similarity; FF-2A; objective description
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fujioka, K.; Tomizawa, Y.; Shimizu, N.; Ikeda, K.; Manome, Y. Improving the Performance of an Electronic Nose by Wine Aroma Training to Distinguish between Drip Coffee and Canned Coffee. Sensors 2015, 15, 1354-1364.

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