2003 Special issue Electronic nose based tea quality standardization Ritaban Dutta a, * , K.R. Kashwan b , M. Bhuyan b , E.L. Hines a , J.W. Gardner a a Department of Engineering, University of Warwick, Coventry CV4 7AL, UK b Department of Electronics, Tezpur University (A Central University), Napaam, Tezpur (Assam) 784028, India Abstract In this paper we have used a metal oxide sensor (MOS) based electronic nose (EN) to analyze five tea samples with different qualities, namely, drier month, drier month again over-fired, well fermented normal fired in oven, well fermented overfired in oven, and under fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which is generated by hundreds of Volatile Organic Compounds (VOCs) and Non-Volatile Organic Compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam Tea) and Assamica Sinesis (Darjeeling and Japanese Tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human or ganoleptic profiling panels. These methods are expensive in terms of time and labour and 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 to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of 4 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. The data were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM). We also explored the use of a Self-Organizing Map (SOM) method along with a Radial Basis Function network (RBF) and a Probabilistic Neural Network classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with 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. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Electronic nose; Tea flavour; Odours 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 tongue), olfaction (sense of smell by nose) and trigeminal (sense of irritation). Taste is 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 mucous membranes and in the skin, they also respond to many volatile chemicals and it is thought to be especially important in the detection of irritants and chemically reactive species. In the perception of flavour all three chemoreceptor systems are involved but olfaction plays by far the greatest role with other two senses contributing much less to the overall perception (Dutta, Hines, Gardner, Udrea, & Boilot, 2003). 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 of two types, simple and complex. 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 odorant molecules. All naturally occurring odours are complex mixtures. Odorants are typically small hydrophobic, organic mol- ecules 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 proteins having been identified—in a total of about 50 million. These cells send their signals to secondary nodes and then cells located in the olfactory bulb. There is a marked convergence at this stage with between 1000 and 0893-6080/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0893-6080(03)00092-3 Neural Networks 16 (2003) 847–853 www.elsevier.com/locate/neunet * Corresponding author.