Z. Haddi

Aerosol-Assisted CVD-Grown WO3 Nanoneedles Decorated with Copper Oxide Nanoparticles for the Selective and Humidity-Resilient Detection of H2S

A gas-sensitive hybrid material consisting of Cu2O nanoparticle-decorated WO3 nanoneedles is successfully grown for the first time in a single step via aerosol-assisted chemical vapor deposition. Morphological, structural, and composition analyses show that our method is effective for growing single-crystalline, n-type WO3 nanoneedles decorated with p-type Cu2O nanoparticles at moderate temperatures (i.e., 380 °C), with cost effectiveness and short fabrication times, directly onto microhot plate transducer arrays with the view of obtaining gas sensors. The gas-sensing studies performed show that this hybrid nanomaterial has excellent sensitivity and selectivity to hydrogen sulfide (7-fold increase in response compared with that of pristine WO3 nanoneedles) and a low detection limit (below 300 ppb of H2S), together with unprecedented fast response times (2 s) and high immunity to changes in the background humidity. These superior properties arise because of the multiple p-n heterojunctions created at the nanoscale in our hybrid nanomaterial.

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.

Aerosol-Assisted CVD-Grown PdO Nanoparticle-Decorated Tungsten Oxide Nanoneedles Extremely Sensitive and Selective to Hydrogen

We report for the first time the successful synthesis of palladium (Pd) nanoparticle (NP)-decorated tungsten trioxide (WO3) nanoneedles (NNs) via a two-step aerosol-assisted chemical vapor deposition approach. Morphological, structural, and elemental composition analysis revealed that a Pd(acac)2 precursor was very suitable to decorate WO3 NNs with uniform and well-dispersed PdO NPs. Gas-sensing results revealed that decoration with PdO NPs led to an ultrasensitive and selective hydrogen (H2) gas sensor (sensor response peaks at 1670 at 500 ppm of H2) with low operating temperature (150 °C). The response of decorated NNs is 755 times higher than that of bare WO3 NNs. Additionally, at a temperature near that of the ambient temperature (50 °C), the response of this sensor toward the same concentration of H2 was 23, which is higher than that of some promising sensors reported in the literature. Finally, humidity measurements showed that PdO/WO3 sensors displayed low-cross-sensitivity toward water vapor, compared to bare WO3 sensors. The addition of PdO NPs helps to minimize the effect of ambient humidity on the sensor response.

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.

Application of a portable electronic nose device to discriminate and identify cheeses with known percentages of cow's and goat's milk

A portable electronic nose comprising an array of 6 metal oxide semiconductor sensors was developed and used, jointly with pattern recognition methods, to discriminate and identify several cheeses made from goats, cows milk and their mixtures. Principal Component Analysis (PCA) was used to visualize the different categories of aroma profiles and Multivariate Analysis of Variance (MANOVA) was performed to test the significance of the differences between cheeses groups. Database was then elaborated using supervised classifiers such as Discriminant Factor Analysis (DFA) with leave one out approach. The results indicate that the portable electronic nose device can clearly and rapidly distinguish between cows milk cheese, goats milk cheese and cheeses containing variable amounts of cows and goats milk. So, this system can be used in order to avoid fraud and to fulfill customer expectations.

Geographical classification of Virgin Olive Oils by combining the electronic nose and tongue

Although the great interest of development of performed gas and liquid sensors, lack of cross-sensitivity still remains the major drawback of electronic sensing systems such as electronic nose and tongue. We propose here an approach aimed at overcoming this shortcoming. So a performed data fusion method of electronic nose and tongue was used in order to classify five Virgin Olive Oils (VOOs) picked up from five Moroccan geographical areas. The electronic nose instrument consists of five commercial available MOS TGS gas sensors and the electronic tongue was designed using four voltammetric electrodes. Two techniques, i.e., Principal Component Analysis (PCA) and Support Vector Machines (SVMs) were used to develop classification models using as inputs specific features extracted from the collected sensor signals. Great enhancement in successful discrimination between all VOOs was achieved when compared to the individual systems due to a performed low-level of abstraction data fusion.

Pt/WO 3 microsensor grown by cold wall reactor Aerosol Assisted Chemical Vapor Deposition for C 6 H 6 and NO 2 detection

Aerosol Assisted Chemical Vapor Deposition (AACVD) induced via localized heating of the gas sensor shows high flexibility, low cost and capability for the direct synthesis of low-dimensional metal oxide nanostructures in a wide scale of substrates. In this work, we report for the first time the successful co-deposition of tungsten trioxide nanowires decorated with Pt nanoparticles (Pt/WO3) in a single step, via AACVD method employing the self-heating capability of MEMS transducer platforms. E-SEM and XRD analysis have been used to investigate the morphology and the composition of the nanostructures grown. The fabricated gas microsensors have been tested toward different concentration of NO2 and C6H6. In comparison with our previous work, these new results show a clear improvement in the synthesis of the nanostructures, a highly enhanced sensitivity towards small concentrations of benzene, and good sensitivity and selectivity toward NO2.