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Sensors 2016, 16(5), 636; doi:10.3390/s16050636

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

1
Department of Mechanical Engineering of Biosystems, Urmia University, Urmia 5756151818, Iran
2
Research Institute of Forests and Rangelands, Tehran 1496813111, Iran
3
Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord 8818634141, Iran
4
Department of Electronic, Electrical and Automatic Control Engineering, Universitat Rovira i Virgili, Tarragona 43007, Spain
*
Authors to whom correspondence should be addressed.
Academic Editor: Carmen Horrillo Güemes
Received: 7 April 2016 / Revised: 22 April 2016 / Accepted: 27 April 2016 / Published: 4 May 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
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Abstract

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil. View Full-Text
Keywords: Fuzzy ARTMAP; LDA; electronic nose; Rosa damascene; essential oil; classification Fuzzy ARTMAP; LDA; electronic nose; Rosa damascene; essential oil; classification
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MDPI and ACS Style

Gorji-Chakespari, A.; Nikbakht, A.M.; Sefidkon, F.; Ghasemi-Varnamkhasti, M.; Brezmes, J.; Llobet, E. Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose. Sensors 2016, 16, 636.

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