Sensors and Actuators B 94 (2003) 228–237 Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach Ritaban Dutta a,∗ , E.L. Hines a , J.W. Gardner a , K.R. Kashwan b , M. Bhuyan b a Division of Electrical and Electronic Engineering, School of Engineering, University of Warwick, Coventry CV4 7AL, UK b Department of Electronics, Tezpur University (A Central University), Napaam, Tezpur, Assam 784028, India Received 5 February 2003; received in revised form 27 March 2003; accepted 14 April 2003 Abstract In this paper, we have (analyzed using a metal oxide sensor (MOS)-based electronic nose (EN)) five tea samples with different qual- ities, namely, drier month, drier month again over-fired, well-fermented normal fired in oven, well-fermented over-fired in oven, and under-fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which are determined by hundreds of volatile organic compounds (VOC) and non-volatile organic compounds present in tea. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of for example time and labour. The methods are also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and thus to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of four MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry resulting in our tea data-set. The data were processed using principal component analysis (PCA), fuzzy C means (FCM) algo- rithm. The data were then analyzed following the neural network paradigms, following the self-organizing map (SOM) method along with radial basis function (RBF) network and probabilistic neural network (PNN) classifier. Using FCM and SOM feature extraction techniques along with RBF neural network, we achieved 100% correct classification for the five different tea samples, each of which have different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under-fermented, etc. © 2003 Elsevier B.V. All rights reserved. Keywords: Odour detector; Tea flavour sensor; Metal oxide sensor; Sensor array; Analytical instrumentation; Neural network; Fuzzy processing 1. Introduction 1.1. Olfactory system and chemical senses To humans, the sensation of flavour is due to three main chemoreceptor systems. These are gustation (sense of taste by the tongue), olfaction (sense of smell by the nose) and trigeminal (sense of irritation) systems. Taste is understood to be used to detect non-volatile chemicals, which enter the mouth while the sense of smell is used to detect volatile compounds. Receptors for the trigeminal sense are located in the mucous membranes and in the skin; they also respond to many volatile chemicals. It is thought to be especially important in the detection of irritants and chemically reactive species. In the perception of flavour all three chemoreceptor ∗ Corresponding author. Tel.: +44-2476-528146; fax: +44-2476-418922. E-mail address: r.dutta@warwick.ac.uk (R. Dutta). systems are involved but olfaction plays by far the greatest role with the other two senses contributing much less to our overall perception [1]. The smell sensation is a chemical and neural process wherein odorant molecules stimulate the olfactory receptor cells that are located high up in the nose, in the olfactory epithelium. Odours are believed to be of two types, simple and complex. The nature of stimulus and not the quality of sensation distinguish these. A simple odour is one which consists of only one type of odorant molecule whereas a complex odour is a mixture of many, different types of odor- ant molecules. All naturally occurring odours are complex mixtures. Odorants are typically small hydrophobic, organic molecules containing one or two functional groups. The size, shape and polar properties of the molecules determine its odour properties. Broad patterns of response are shown by the mammalian olfactory system consisting of a large number of non-specific receptors—with about 300 different olfactory binding pro- 0925-4005/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0925-4005(03)00367-8