Maryam Siadat

Erythrocyte aggregation: approach by light scattering determination.

Erythro-aggregometer is a Couette viscometer which was developed to measure aggregation parameters of red blood cells. The system is based on the analysis of the light intensity backscattered by a blood suspension. It allows to approach aggregation phenomenon in terms of kinetics, structural and rheological parameters. The measurement system designed for use with a microcomputer is suitable for both research and clinical investigations.

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling.

Comparative study of conductometric glucose biosensor based on gold and on magnetic nanoparticles.

The aim of this study was to show the feasibility and the performances of nanoparticle biosensing. A glucose conductometric biosensor was developed using two types of nanoparticles (gold and magnetic), glucose oxidase (GOD) being adsorbed on PAH (poly(allylamine hydrochloride)) modified nanoparticles, deposited on a planar interdigitated electrode (IDEs). The best sensitivities for glucose detection were obtained with magnetic nanoparticles (70 μM/mM and 3 μM of detection limit) compared to 45 μM/mM and 9 μM with gold nanoparticles and 30 μM/mM and 50 μM with GOD directly cross-linked on IDEs. When stored in phosphate buffer (20 mM, pH 7.3) at 4 °C, the biosensor showed good stability for more than 12 days.

Electrosprayed Metal Oxide Semiconductor Films for Sensitive and Selective Detection of Hydrogen Sulfide

Semiconductor metal oxide films of copper-doped tin oxide (Cu-SnO2), tungsten oxide (WO3) and indium oxide (In2O3) were deposited on a platinum coated alumina substrate employing the electrostatic spray deposition technique (ESD). The morphology studied with scanning electron microscopy (SEM) and atomic force microscopy (AFM) shows porous homogeneous films comprising uniformly distributed aggregates of nano particles. The X-ray diffraction technique (XRD) proves the formation of crystalline phases with no impurities. Besides, the Raman cartographies provided information about the structural homogeneity. Some of the films are highly sensitive to low concentrations of H2S (10 ppm) at low operating temperatures (100 and 200 °C) and the best response in terms of Rair/Rgas is given by Cu-SnO2 films (2500) followed by WO3 (1200) and In2O3 (75). Moreover, all the films exhibit no cross-sensitivity to other reducing (SO2) or oxidizing (NO2) gases.

Discrimination of a refrigerant gas in a humidity controlled atmosphere by using modelling parameters

Abstract For real life conditions of use, the application based on tin oxide gas sensors must be humidity controlled, because humidity is a very influent parameter which may cause false gas detection. In our application, we want to distinguish a refrigerant gas (Forane R134a) in an air conditioned atmosphere using a TGS gas sensor array. At first, this paper presents a summary of the sensor array time-dependent response in a Forane R134a gas concentration (0–1000 ppm) for a humid air atmosphere (0–85%). We show that these responses can be well fitted by a double exponential model for which we extract five modelling parameters. These variables are coupled or not with two experimental parameters, the steady-state conductance and the conductance dynamic slope, and then arranged in data bases. Afterwards, these data bases are treated by using two complementary pattern recognition methods: the principal component analysis (PCA) followed by the discriminant factorial analysis (DFA). We show the ability to discriminate the target gas, whatever the humidity rate, when the modelling parameters are coupled or not with the experimental parameters. Finally, the identification of unknown cases is described.

An Electronic Nose for the Identification of Forane R134a in an Air Conditioned Atmosphere

An electronic nose based on a TGS type sensor array for the main detection of Forane R134a has been characterised under closely controlled gas temperature and humidity conditions. This paper presents the dependence of the TGS sensor array to the gas temperature and the relative humidity rate values. A model is proposed for the sensor array behaviour for each of these parameters. Afterwards, the importance of these two atmospheric parameters is underlined and the need to control or to include them into a database is proven. We present the ability to identify the target gas with the discriminant factorial analysis method even if the relative humidity or the gas temperature differs from the nose database learning process.

An electronic nose for the discrimination of forane 134a and carbon dioxide in a humidity controlled atmosphere

The great interest for the development of performed gas sensors has now an increasing application field -- the electronic nose. We propose here to develop such a system for an environmental application in the field of the air quality control. Our application is based on the study of the learning process of our sensor system for a performed discrimination of a refrigerant gas forane 134a and carbon dioxide in a humidity and temperature controlled atmosphere. In this paper, we present first the characterisation results obtained for the metal oxide sensors used in this application. Afterwards, we propose to compare the discrimination results of the gases with several representative variables by using two pattern recognition methods: principal component analysis (PCA) and discriminant factorial analysis (DFA). We show the importance to use two types of complementary variables: the steady-state conductance and the conductance dynamic slope. We also propose a new method to reduce the drift effect and then we show the discrimination of the gases and also the identification of unknown cases by the way of the created decisive law.

Humidity effects on a commercially available refrigerant gas sensor (TGS 832)

In order to study the influence of several atmospheric parameters on Figaro type gas sensors response, we have designed a test equipment which allows us to characterize these kind of sensors under conditions of closely controlled temperature, humidity and contaminant gas concentrations. This equipment is mainly composed of a gas humidification system to satisfy the humidity and temperature control conditions, and a test chamber which enclose the sensors to be characterized. At first, this paper presents the developed system and its main requirements. Then, preliminary results are presented, showing the influence of the relative humidity on the behavior of an array of these sensors.

Discrimination of Forane 134a and carbon dioxide concentrations in an air conditioned atmosphere with an electronic nose: influence of the relative humidity

Abstract Nowadays, lots of efforts are done all over the world to protect the atmosphere from the greenhouse effects. In the field of smart sensors, new applications called electronic noses are developed and used for environmental air quality control. We present here such an application based on a TGS sensor array for the detection of two main gases inducing green house effects: a new refrigerant type gas and carbon dioxide (CO2). In this paper, we propose to study the influence of the relative humidity rate on the quantification of these gases and the ability of our system to successfully identify the concentration of unknown test samples. After summarising the experimental system used for the accurate characterisation of the chosen sensor array and giving the obtained experimental results, we present and compare the gas quantification results obtained with principal component analysis (PCA) and discriminant factorial analysis (DFA). We prove that the presence of humidity has a great influence on the sensor responses and the gas concentration discrimination in an air conditioned atmosphere and we show that DFA offers the best results for this quantification even that PCA is the more used method in the literature.

Application of electronic nose to beer recognition using supervised artificial neural networks

Employment of electronic nose is drawing many attentions in brewery because of its unique capability in assessing multi-component analytes, which is largely feasible for traditional single-sensor devises. This study was aimed to recognize between alcoholic and non alcoholic beers by use of a MOS-based electronic nose system coupled with artificial neural networks (ANN) to evaluate the capability of the system for a binary discrimination. The PCA score plot of the two first principal components accounted for 78% of variance and clearly discrimination was observed. This observation was confirmed by ANN in such as way radial basis function (RBF) and Backpropagation (BP) showed satisfactory results to binary discrimination between two types of beer as 100 % of classification accuracy for both training and testing data sets. This result confirms the ability of the electronic nose to be used in future for other applications to beer evaluation in our project.