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Open AccessArticle

A Bio-Inspired Herbal Tea Flavour Assessment Technique

1
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Jejawi 02600, Perlis, Malaysia
2
School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Perlis, Malaysia
3
Institute of Engineering Mathematics, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Perlis, Malaysia
*
Author to whom correspondence should be addressed.
Sensors 2014, 14(7), 12233-12255; https://doi.org/10.3390/s140712233
Received: 12 May 2014 / Revised: 25 June 2014 / Accepted: 26 June 2014 / Published: 9 July 2014
(This article belongs to the Section Physical Sensors)
Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers’ performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied. View Full-Text
Keywords: bio-inspired flavour assessment; e-nose; e-tongue; low level data fusion; intermediate level data fusion; classification; LDA; SVM; KNN; PNN; GC-MS bio-inspired flavour assessment; e-nose; e-tongue; low level data fusion; intermediate level data fusion; classification; LDA; SVM; KNN; PNN; GC-MS
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Zakaria, N.Z.I.; Masnan, M.J.; Zakaria, A.; Shakaff, A.Y.M. A Bio-Inspired Herbal Tea Flavour Assessment Technique. Sensors 2014, 14, 12233-12255.

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