Borah, S., Hines, Evor, Leeson, Mark S., Iliescu, Daciana, Bhuyan, M. and Gardner, J. W. (2008) Neural network based electronic nose for classification of tea aroma. Sensing and Instrumentation for Food Quality and Safety, Vol.2 (No.1). pp. 7-14. doi:10.1007/s11694-007-9028-7 ISSN 1932-7587.

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Abstract

This paper describes an investigation into the performance of a Neural Network (NN) based Electronic Nose (EN) system, which can discriminate the aroma of different tea grades. The EN system comprising of an array of four tin-oxide gas sensors was used to sniff thirteen randomly selected tea grades, which were exemplars of eight categories in terms of aroma profiles. The mean and peak of the transient signals generated by the gas sensors, as a result of aroma sniffing, were treated as the feature vectors for the analysis. Principal Component Analysis (PCA) was used to visualise the different categories of aroma profiles. In addition, K-means and Kohonen’s Self Organising Map (SOM) cluster analysis indicated there were eight clusters in the dataset. Data classification was performed using supervised NN classifiers; namely the Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) network, and Constructive Probabilistic Neural Network (CPNN) were used for aroma classification. The results were that the three NNs performed as follows: 90.77, 92.31, and 93.85%, respectively in terms of classification accuracy. Hence the performance of the proposed method of aroma analysis demonstrates that it is possible to use NN based EN to assist with the tea quality monitoring procedure during the tea grading process. In addition the results indicate the possibility for standardization of the tea aroma in numeric terms.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH):	Neural networks (Computer science) , Tea -- Sensory evaluation, Olfactometry, Olfactory receptors
Journal or Publication Title:	Sensing and Instrumentation for Food Quality and Safety
Publisher:	Springer New York LLC
ISSN:	1932-7587
Official Date:	March 2008
Dates:	Date Event March 2008 Published
Volume:	Vol.2
Number:	No.1
Number of Pages:	8
Page Range:	pp. 7-14
DOI:	10.1007/s11694-007-9028-7
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	17 December 2015
Date of first compliant Open Access:	17 December 2015

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