Talanta 67 (2005) 610–616 Classification of white wine aromas with an electronic nose J. Lozano ∗ , J.P. Santos, M.C. Horrillo Laboratorio de Sensores, Instituto de F´ ısica Aplicada, CSIC, C/Serrano 144, 28006 Madrid, Spain Received 11 November 2004; received in revised form 8 February 2005; accepted 14 March 2005 Available online 14 April 2005 Abstract This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%. © 2005 Elsevier B.V. All rights reserved. Keywords: Aromatic compound; Thin film sensor; Pattern recognition techniques 1. Introduction 1.1. Biological and electronic noses Human nose is much more complicated than other human senses like the ear and the eye, at least regarding the mech- anisms responsible for the primary reaction to an external stimulus. Therefore, it has been much simpler to mimic the auditory and the visual senses. In olfaction hundreds of dif- ferent classes of biological receptors are involved. Although several interesting developments have been made regarding so-called electronic noses, their performance is far from that of our olfactory sense. They are not as sensitive as our nose to many odorous compounds. Despite this difference, chemical sensor arrays combined with pattern recognition methods are very useful in many practical applications like monotonous tasks in quality control. Electronic noses are thus emerging as new instrumentation, which can be used to measure the quality or identify an aroma of a product. They work in a similar way and have, in that aspect, a large similarity with the human nose [1,2]. ∗ Corresponding author. Tel.: +34 915618806; fax: +34 915631794. E-mail addresses: jesloz@ifa.cetef.csic.es (J. Lozano), carmenhorrillo@ifa.cetef.csic.es (M.C. Horrillo). The human olfactory system is very complex, and has been recently successfully investigated and recognized with the Nobel Prize [2–4]. Each olfactory receptor cell possesses only one type of odorant receptor, and each receptor can detect a limited number of odorant substances. The electronic nose is an electronic system that tries to imitate the structure of the human nose. Both systems are based on non specific receptors (cells and sensors) followed by a posterior signal processing. An accepted definition of an electronic nose is: “an instru- ment which comprises an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex odours” [5] and tries to characterise different gas mixtures [3,6,7]. It uses currently a number of individual sensors (typically 5–100) whose selectivities towards different molecules over- lap. The response from a chemical sensor is usually measured as the change of some physical parameter, e.g. conductivity or current. The response times for these devices range from seconds up to a few minutes. This is a significant drawback for these devices, and thus one of the main research topics in this field is to reduce the response time. A simple flow chart of the typical structure of an electronic nose is shown in Fig. 1. By teaching a computer (or hardware) to recognise those patterns we have used to train the electronic nose, it should 0039-9140/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.talanta.2005.03.015