Tarik Saidi

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 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.

Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue

Main possible honey fraud is the addition of various sugar syrups. But, there are also other types of fraud, such as deception on the geographical and/or botanical origin product. Providing a product of the hive with full authenticity is therefore crucial for the preservation of beekeeping. In this pursuit, voltammetric electronic tongue (VE-tongue) was employed to classify honey samples from different geographical and botanical origins. Furthermore, VE-tongue was used to detect adulterants such as glucose syrup (GS) and saccharose syrup (SS) in honey. The data obtained were analyzed by three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs), and hierarchical cluster analysis (HCA). These methods enabled the classification of 18 honeys of different geographical origins and 7 honeys of different botanical origins. Excellent results were obtained also in the detection of adulterated honey. Therefore, this simple method based on VE-tongue could be useful in the honey packaging and commercialization industry.

Detection of seasonal allergic rhinitis from exhaled breath VOCs using an electronic nose based on an array of chemical sensors

In this study, we investigate for the first time the ability of an electronic nose (E-nose) based on an array of chemical sensors to discriminate between breath Volatile Organic Compounds (VOCs) that characterize patients with Seasonal Allergic Rhinitis (SAR) and healthy states. To reach this aim, multivariate analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Support Vector Machines (SVMs) were applied for database as an alternative tools for the resolution of complex classification situations. The preliminary results reveal that VOC-patterns of exhaled breath were accurately discriminated patients with SAR from healthy controls. These findings indicate that the E-nose may succeed as a non-invasive diagnostic tool, low cost and rapid technique for breath analysis.

Ability of discrimination of breath from smoker and non-smoker volunteers by using an electronic nose based on WO 3 nanowires and SnO 2 sensors

Volatile organic compounds (VOCs) emanating from human breath could be used as non-invasive biomarkers of specific health states. Electronic nose technology has been emerged as new method to analyze human breath and produce specific VOCs patterns. The aim of this study was to test the feasibility of two sensor arrays to differentiate smokers and non-smokers breath. The first sensor array contains six MQ sensors based on SnO2, while the second one comprised six interdigitated sensors based on metal-functionalized WO3 nanowires prepared by aerosol assisted chemical vapor deposition (AACVD). Breath samples were collected in duplicate from 32 volunteers. Principal component analysis (PCA) and Support Vector Machines (SVMs) show that WO3 sensor array gives further accuracy results than SnO2 sensors regarding the discrimination among three clusters related to smokers, non-smokers and room air samples. This study provides more evidence that device based on WO3 could be useful tool for breath pattern screening.

Determination of safranal concentration in saffron samples by means of VE-Tongue, SPME-GC-MS, UV-Vis Spectrophotometry and multivariate analysis

An experimental investigation has been carried out to characterize and discriminate seven saffron samples and to verify their declared geographical origin using a voltammetric electronic tongue (VE-tongue). The ability of multivariable analysis methods such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) to classify the saffron samples according to their geographical origin have been investigated. A good discrimination has reached using PCA and HCA in the VE-tongue characterization case. Furthermore, cross validation and Partial Least Square (PLS) techniques were applied in order to build suitable management and prediction models for the determination of safranal concentration in saffron samples based on SPME-GC-MS and UV-Vis Spectrophotometry. The obtained results reveals that some relationships were established between the VE-tongue signal, SPME-GC-MS and UV-Vis spectrophotometry methods to predict safranal concentration levels in saffron samples by using the PLS model. In the light of these results, we can say that the proposed electronic system offer a fast, simple and efficient tool to recognize the declared geographical origin of the saffron samples.