Electronic nose based tea quality standardization
UNSPECIFIED (2003) Electronic nose based tea quality standardization. In: INNS/IEEE International Joint Conference on Neural Networks (IJCNN 03), JUL 20-24, 2003, PORTLAND, OR.Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/S0893-6080(03)00092-3
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 overtired 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. (C) 2003 Elsevier Science Ltd. All rights reserved.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Journal or Publication Title:||NEURAL NETWORKS|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Number of Pages:||7|
|Page Range:||pp. 847-853|
|Title of Event:||INNS/IEEE International Joint Conference on Neural Networks (IJCNN 03)|
|Location of Event:||PORTLAND, OR|
|Date(s) of Event:||JUL 20-24, 2003|
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