Khalid Tahri

E-Nose and e-Tongue combination for improved recognition of fruit juice samples

There are many important challenges related to food security analysis by application of chemical and electrochemical sensors. One critical parameter is the development of reliable tools, capable of performing an overall sensory analysis. In these systems, as much information as possible is required in relation to smell, taste and colour. Here, we investigated the possibility of using a multisensor data fusion approach, which combines an e-Nose and an e-Tongue, adept in generating combined aroma and taste profiles. In order to shed light on this concept, classification of various Tunisian fruit juices using a low-level of abstraction data fusion technique was attempted. Five tin oxide-based Taguchi Gas Sensors were applied in the e-Nose instrument and the e-Tongue was designed using six potentiometric sensors. Four different commercial brands along with eleven fruit juice varieties were characterised using the e-Nose and the e-Tongue as individual techniques, followed by a combination of the two together. Applying Principal Component Analysis (PCA) separately on the respective e-Nose and e-Tongue data, only few distinct groups were discriminated. However, by employing the low-level of abstraction data fusion technique, very impressive findings were achieved. The Fuzzy ARTMAP neural network reached a 100% success rate in the recognition of the eleven-fruit juices. Therefore, data fusion approach can successfully merge individual data from multiple origins to draw the right conclusions that are more fruitful when compared to the original single data. Hence, this work has demonstrated that data fusion strategy used to combine e-Nose and e-Tongue signals led to a system of complementary and comprehensive information of the fruit juices which outperformed the performance of each instrument when applied separately.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage days discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor.

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.

Instrumental assessment of red meat origins and their storage time using electronic sensing systems

Objective and rapid electronic sensing systems for distinguishing among meat species and identifying the degree of spoilage have been developed. A metal oxide sensor-based electronic nose system consisting of six sensors is designed and used to analyze the headspace emanating from beef, goat and sheep meats stored at 4 °C. A rapid, non-destructive technique based on the electronic tongue system formed by seven working electrodes is also applied and used to analyse the fingerprint of the electrochemical compounds of the three meat samples. Data analysis is performed by two pattern recognition methods: Principal Component Analysis (PCA) and Support Vector Machines (SVMs). Discrimination and classification function analyses are performed on the response of the electronic nose and electronic tongue systems to each of the three red meats. The obtained results show that the three red meats can be distinguished and the number of days spent in cold storage can be identified.

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.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.

Distinguishment, identification and aroma compound quantification of Portuguese olive oils based on physicochemical attributes, HS‐GC/MS analysis and voltammetric electronic tongue

BACKGROUND In this paper, various extra-virgin and virgin olive oils samples from different Portuguese markets were studied. For this purpose, a voltammetric electronic tongue (VE-tongue), consisting of two kinds of working electrode within the array, together with physicochemical analysis and headspace gas chromatography coupled with mass spectrometry (HS-GC-MS), were applied. In addition, preliminary considerations of relationships between physicochemical parameters and multisensory system were reported. RESULTS The physicochemical parameters exhibit significant differences among the analyzed olive oil samples that define its qualities. Regarding the aroma profile, 14 volatile compounds were characterized using HS-GC-MS; among these, hex-2-enal, hexanal, acetic acid, hex-3-ene-1-ol acetate and hex-3-en-1-ol were semi-quantitatively detected as the main aroma compounds in the analyzed samples. Moreover, pattern recognition methods demonstrate the discrimination power of the proposed VE-tongue system. The results reveal the VE-tongues ability to classify olive oil samples and to identify unknown samples based of built models. In addition, the correlation between VE-tongue and physicochemical analysis exhibits a remarkable prediction model aimed at anticipating carotenoid content. CONCLUSION The preliminary results of this investigation indicate that physicochemical and HS-GC-MS analysis, together with multisensory system coupled with chemometric techniques, presented a satisfactory performance regarding olive oil sample discrimination and identification.

Geographical provenience differentiation and adulteration detection of cumin by means of electronic sensing systems and SPME-GC-MS in combination with different chemometric approaches

The detection of the aroma and flavour volatile compounds of spices is key in product quality control. Accordingly, it is necessary to develop new electronic sensing systems for food adulteration control and authenticity assessment for protecting customers health. In this work, the capability of the E-nose and VE-tongue in combination with SPME-GC-MS to correctly discriminate between several cumin samples of different geographical origins and to detect their adulteration, by using unsupervised and supervised chemometric tools, was evaluated. Regarding the aroma profile, eleven volatile compounds were characterized by SPME-GC-MS; all of them were found in cumin powder while only eight are found in cumin seeds. The main volatile compounds detected were β-pinene, m-cymene, γ-terpinene, cuminaldehyde and cuminic alcohol, in different proportions depending on the cumin sample form (seed or powder). In summary, the results obtained are sufficiently encouraging as a starting point for the development of new electronic sensing systems with more improvement in the reliability of the sensors performance as well as chemometric tools in order to deal with a complex dataset.

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.