Nezha El Bari

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.

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.

Novel strategy for sulfapyridine detection using a fully integrated electrochemical Bio-MEMS: Application to honey analysis.

Sulfapyridine (SPy) is a sulfonamide antibiotic largely employed as veterinary drugs for prophylactic and therapeutic purposes. Therefore, its spread in the food products has to be restricted. Herein, we report the synthesis and characterization of a novel electrochemical biosensor based on gold microelectrodes modified with a new structure of magnetic nanoparticles (MNPs) coated with poly(pyrrole-co-pyrrole-2-carboxylic acid) (Py/Py-COOH) for high efficient detection of SPy. This analyte was quantified through a competitive detection procedure with 5-[4-(amino)phenylsulfonamide]-5-oxopentanoic acid-BSA (SA2-BSA) antigens toward polyclonal antibody (Ab-155). Initially, gold working electrodes (WEs) of integrated biomicro electro-mechanical system (BioMEMS) were functionalized by Ppy-COOH/MNPs, using a chronoamperometric (CA) electrodeposition. Afterward, SA2-BSA was covalently bonded to Py/Py-COOH/MNP modified gold WEs through amide bonding. The competitive detection of the analyte was made by a mixture of a fixed concentration of Ab-155 and decreasing concentrations of SPy from 50µgL-1 to 2ngL-1. Atomic Force Microscopy characterization was performed in order to ensure Ppy-COOH/MNPs electrodeposition on the microelectrode surfaces. Electrochemical measurements of SPy detection were carried out using electrochemical impedance spectroscopy (EIS). This biosensor was found to be highly sensitive and specific for SPy, with a limit of detection of 0.4ngL-1. This technique was exploited to detect SPy in honey samples by using the standard addition method. The measurements were highly reproducible for detection and interferences namely, sulfadiazine (SDz), sulfathiazole (STz) and sulfamerazine (SMz). Taking these advantages of sensitivity, specificity, and low cost, our system provides a new horizon for development of advanced immunoassays in industrial food control.

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.

DPPG Liposomes Adsorbed on Polymer Cushions: Effect of Roughness on Amount, Surface Composition and Topography.

The adsorption of intact liposomes onto solid supports is a fundamental issue when preparing systems with encapsulated biological molecules. In this work, the adsorption kinetic of 1,2-dipalmitoyl-sn-glycero-3-[phospho-rac-(1-glycerol)] (sodium salt) liposomes onto cushions prepared from commom polyelectrolytes by the layer-by-layer technique was investigated with the main objective of finding the surface conditions leading to the adsorption of intact liposomes. For this purpose, different cushion surface roughnesses were obtained by changing the number of cushion bilayers. The adsorbed amount per unit area was measured through quartz crystal microbalance, surface morphology was characterized by atomic force microscopy, and the surface composition was assessed by X-ray photoelectron spectroscopy. The results show that (1) the amount of adsorbed lipids depends on the number of cushion bilayers, (2) the cushions are uniformly covered by the adsorbed lipids, and (3) the surface morphology of polymer cushions tunes liposome rupture and its adsorption kinetics. The fraction of ruptured liposomes, calculated from the measured amount of adsorbed lipids, is a function of surface roughness together with other surface morphology parameters, namely the dominating in-plane spatial feature size, the fractal dimension, and other textural features as well as amplitude and hybrid parameters.

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.

Potential application of the electronic nose for shelf‐life determination of raw milk and red meat

The present study describes the performance of an electronic nose in food odor analysis. This methodology was successfully applied to odor characterization of milk stored at 4° C during 4 days and of beef and sheep meat stored at 4° C for up to 15 days. The electronic nose sensor system coupled to PCA as a pattern recognition technique, is able to reveal characteristic changes in raw milk and red meat quality related to storage time. Additionally, a bacteriological method was selected as the reference method to consistently train the electronic nose system for both beef and sheep meat analysis.

Development and application of a novel electrochemical immunosensor for tetracycline screening in honey using a fully integrated electrochemical Bio-MEMS

Tetracycline (TC) is a veterinary drug, wildly prescribed for prophylactic and therapeutic purposes. Consequently, its remaining residues in food products have to be regularized. We report in this paper about the development of a novel immunosensor based on an integrated bio micro-electromechanical system (Bio-MEMS) containing eight gold microelectrodes (µWEs), an integrated silver and platinum reference and counter electrodes, respectively. TC immobilization on the µWEs surface was conducted using three methods. The first through functionalization with 4-aminophenylacetic acid (CMA), the second by functionalization with CMA followed by preconcentration of a new structure of magnetic nanoparticles (MNPs) coated with poly (pyrrole-co-pyrrole-2-carboxylic acid) (Py/Py-COOH/MNPs) cross-linked with Ab-TC, and the last one directly through the functionalization with Py/Py-COOH/MNPs. The analyte was quantified by competitive detection with TC immobilized on the µWEs surface toward specific polyclonal antibody (Ab-TC), using a mixture of a fixed concentration of Ab-TC and decreasing levels of TC one from 0.1 pg mL-1 to 1000 pg mL-1. Microcontact printing, followed by fluorescence microscopy characterization were performed during the functionalization of the immunosensor surface to certify that the corresponding immune detection process is taking place. This immunosensor was found to be highly sensitive with a limit of detection of 1.2 pg mL-1 and specific in the presence of interferents. The standard addition method was exploited to detect TC in honey samples. The present immunosensor platform is up-and-coming for TC detection which can dramatically decrease the time of analysis providing a new pathway for advanced immunoassays development in industrial food control.