Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the c...

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Published in:Journal of sensors 2014-01, Vol.2014 (2014), p.1-10
Main Authors: El Bari, Nezha, Saidi, Tarik, Haddi, Zouhair, Tahri, Khalid, Bougrini, Madiha, Bouchikhi, Benachir
Format: Article
Language:English
Subjects:
Chromatography
Classification
Data processing
Electronic noses
Food
Food quality
Fraud
Helianthus
Mass spectrometry
Metal oxide semiconductors
Methods
Pattern recognition
Principal components analysis
Sensors
Success
Sunflower oil
Support vector machines
Vegetable oils
Voltammetry
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container_issue 2014
container_start_page 1
container_title Journal of sensors
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creator El Bari, Nezha
Saidi, Tarik
Haddi, Zouhair
Tahri, Khalid
Bougrini, Madiha
Bouchikhi, Benachir
description Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil.
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Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. 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subjects Chromatography
Classification
Data processing
Electronic noses
Food
Food quality
Fraud
Helianthus
Mass spectrometry
Metal oxide semiconductors
Methods
Pattern recognition
Principal components analysis
Sensors
Success
Sunflower oil
Support vector machines
Vegetable oils
Voltammetry
title Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue
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