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Article

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

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Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Pathology Department, Southern Hardwoods Laboratory, Center for Forest Genetics & Ecosystems Biology, Southern Research Station, USDA Forest Service, 432 Stoneville Road, Stoneville, MS 38776-0227, USA
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Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
4
Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland
*
Authors to whom correspondence should be addressed.
Academic Editors: Anne Claude Romain and Simonetta Capone
Chemosensors 2021, 9(9), 243; https://doi.org/10.3390/chemosensors9090243
Received: 17 July 2021 / Revised: 13 August 2021 / Accepted: 20 August 2021 / Published: 31 August 2021
The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively. View Full-Text
Keywords: artificial neural networks; electronic nose; essential oils; partial least square (PLS); principal regression (PR); product adulteration testing; quality control; volatile organic compounds (VOCs) artificial neural networks; electronic nose; essential oils; partial least square (PLS); principal regression (PR); product adulteration testing; quality control; volatile organic compounds (VOCs)
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MDPI and ACS Style

Rasekh, M.; Karami, H.; Wilson, A.D.; Gancarz, M. Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils. Chemosensors 2021, 9, 243. https://doi.org/10.3390/chemosensors9090243

AMA Style

Rasekh M, Karami H, Wilson AD, Gancarz M. Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils. Chemosensors. 2021; 9(9):243. https://doi.org/10.3390/chemosensors9090243

Chicago/Turabian Style

Rasekh, Mansour, Hamed Karami, Alphus D. Wilson, and Marek Gancarz. 2021. "Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils" Chemosensors 9, no. 9: 243. https://doi.org/10.3390/chemosensors9090243

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