Conception et développement d'un système multicapteurs en gaz et en liquide pour la sécurité alimentaire

Electronic noses and tongues systems based on chemical and electrochemical sensors are an advantageous solution for the characterisation of odours and tastes that are emanating from food products. The cross-selectivity of the sensor array coupled with patter recognition methods is the key element in the design and development of these systems. In this context, we have demonstrated the ability of an electronic nose device to discriminate between different types of drugs, to analyse cheeses freshness, to identify adulterated cheeses and to differentiate between potable and wastewaters. We have also succeeded to correctly classify drinking waters (mineral, natural, sparkling and tap) and wastewaters by using a potentiometric electronic tongue. This study was validated by Gas Chromatography coupled with Mass Spectrometry (GC-MS). Furthermore, we have developed a voltammetric electronic tongue based on a Diamond Doped Boron electrode to differentiate treatment stages of domestic and hospital wastewaters and to identify different heavy metals (Pb, Hg, Cu, Cd, Ni and Zn) contained in Rhône river. The Differential Pulse Anodic Stripping Voltammetry (DPASV) was used as an electrochemical method to characterise the studied waters. Finally, the hybrid multisensor systems have proven to be good analytical tools to characterise the products of food industry such as Tunisian juices and Moroccan olive oilsLes systèmes de nez et de langues électroniques à base de capteurs chimiques et électrochimiques constituent une solution avantageuse pour la caractérisation des odeurs et des saveurs émanant des produits agroalimentaires. La sélectivité croisée de la matrice des capteurs couplée aux méthodes de reconnaissance de formes est l'élément clé dans la conception et le développement de ces systèmes. Dans cette optique, nous avons démontré la capacité d'un dispositif expérimental de nez électronique à discriminer entre les différents types de drogues, à analyser la fraîcheur des fromages, à identifier entre les fromages adultérés et à différentier entre les eaux potables et usées. Nous avons également réussi à classifier correctement les eaux potables (minérales, de source, gazeuse et de robinet) et usées par utilisation d'une langue électronique potentiométrique. Cette étude a été validée par la Chromatographie en Phase Gazeuse couplée à la Spectrométrie de Masse (CPG-MS). En outre, nous avons développé une langue électronique voltammétrique à base d'une électrode de Diamant Dopé au Bore pour différencier les phases de traitement des eaux usées domestiques et hospitaliers et pour identifier les différents métaux lourds (Pb, Hg, Cu, Cd, Ni et le Zn) contenus dans l'eau du fleuve Rhône. La Voltammétrie à Redissolution Anodique à Impulsion Différentielle (DPASV) a été utilisée comme une méthode électrochimique pour caractériser les eaux étudiées. Enfin, les systèmes multicapteurs hybrides se sont avérés un bon outil analytique pour caractériser les produits de l'industrie agroalimentaire tels que les jus tunisiens et les huiles d'olives marocaine

Home monitoring for older singles: A gas sensor array system

Many residential environments have been equipped with sensing technologies both to provide assistance to older people who have opted for aging-in-place and to provide information to caregivers and family. However, such technologies are often accompanied by physical discomfort, privacy concerns, and complexity of use. We explored the feasibility of monitoring home activity using chemical sensors that pose fewer privacy concerns than, for example, video-cameras and which do not suffer from blind spots. We built a monitoring device that integrates a sensor array and IoT capabilities to gather the necessary data about a resident in his/her living space. Over a period of 3 months, we uninterruptedly measured the living space of a typical elder person living on his/her own. To record the level of activity during the same period and obtain a ground truth for the activity, a set of motion sensors were also deployed in the house. Home activity was extracted from a PCA space moving-window which translated sensor data into the event space; this also compensated for environmental and sensor drift. Our results show that it is possible to monitor the person’s home activity and detect sudden deviations from it using a low-cost, non-invasive, system based on gas sensors that gather data on the air composition in the living space. We made the dataset publicly available at https://archive.ics.uci.edu/ml/index.php2.This work was supported by the Spanish Ministry of Economy and Competitiveness (www.mineco.gob.es) PID2021-122952OB-I00, DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), Share4Rare project (Grant Agreement 780262), ISCIII (grant AC22/00035), ACCIÓ (grant Innotec ACE014/20/000018) and Pla de Doctorats Industrials de la Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (2022 DI 014), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant No. 101029808). JF also acknowledges the CERCA Program/Generalitat de Catalunya and the Serra Húnter Program. B2SLab is certified as 2017 SGR 952.Peer ReviewedPostprint (author's final draft

Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

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

Design and development of a gas and liquid multisensors system for food safety

Les systèmes de nez et de langues électroniques à base de capteurs chimiques et électrochimiques constituent une solution avantageuse pour la caractérisation des odeurs et des saveurs émanant des produits agroalimentaires. La sélectivité croisée de la matrice des capteurs couplée aux méthodes de reconnaissance de formes est l'élément clé dans la conception et le développement de ces systèmes. Dans cette optique, nous avons démontré la capacité d'un dispositif expérimental de nez électronique à discriminer entre les différents types de drogues, à analyser la fraîcheur des fromages, à identifier entre les fromages adultérés et à différentier entre les eaux potables et usées. Nous avons également réussi à classifier correctement les eaux potables (minérales, de source, gazeuse et de robinet) et usées par utilisation d'une langue électronique potentiométrique. Cette étude a été validée par la Chromatographie en Phase Gazeuse couplée à la Spectrométrie de Masse (CPG-MS). En outre, nous avons développé une langue électronique voltammétrique à base d'une électrode de Diamant Dopé au Bore pour différencier les phases de traitement des eaux usées domestiques et hospitaliers et pour identifier les différents métaux lourds (Pb, Hg, Cu, Cd, Ni et le Zn) contenus dans l'eau du fleuve Rhône. La Voltammétrie à Redissolution Anodique à Impulsion Différentielle (DPASV) a été utilisée comme une méthode électrochimique pour caractériser les eaux étudiées. Enfin, les systèmes multicapteurs hybrides se sont avérés un bon outil analytique pour caractériser les produits de l'industrie agroalimentaire tels que les jus tunisiens et les huiles d'olives marocainesElectronic noses and tongues systems based on chemical and electrochemical sensors are an advantageous solution for the characterisation of odours and tastes that are emanating from food products. The cross-selectivity of the sensor array coupled with patter recognition methods is the key element in the design and development of these systems. In this context, we have demonstrated the ability of an electronic nose device to discriminate between different types of drugs, to analyse cheeses freshness, to identify adulterated cheeses and to differentiate between potable and wastewaters. We have also succeeded to correctly classify drinking waters (mineral, natural, sparkling and tap) and wastewaters by using a potentiometric electronic tongue. This study was validated by Gas Chromatography coupled with Mass Spectrometry (GC-MS). Furthermore, we have developed a voltammetric electronic tongue based on a Diamond Doped Boron electrode to differentiate treatment stages of domestic and hospital wastewaters and to identify different heavy metals (Pb, Hg, Cu, Cd, Ni and Zn) contained in Rhône river. The Differential Pulse Anodic Stripping Voltammetry (DPASV) was used as an electrochemical method to characterise the studied waters. Finally, the hybrid multisensor systems have proven to be good analytical tools to characterise the products of food industry such as Tunisian juices and Moroccan olive oil

Toward a Selective Detection of Ethanol by Perspiration

International audienc

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

International audienceAtrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF

Relevance Vector Machine as Data-Driven Method for Medical Decision Making

International audienceThe aim of this work is to develop an efficient data-driven method for automatic medical decision making, especially for cardiac arrhythmia diagnosis. To achieve this goal, we have targeted the most common arrhythmia worldwide -atrial fibrillation (AF). Most of reported studies are dealing with inter-beat interval time series analysis coupled with univariate and/or multivariate data-driven methods. The state of the art of this subject revealed that although satisfactory detection findings have been achieved for long AF durations, there is still scope for improvement which needs to be addressed for brief episodes which is highly desired by healthcare professionals. Relevance vector machine (RVM) has been developed to address this issue. Several kernel functions and parameters have been tested to optimize RVM. Five geometrical and nonlinear features were extracted from 30s inter-beat time series. The RVM classifier was trained on 3000 randomly selected samples from four publicly-accessible sets of clinical data and tested on 1000 samples. The performance of the diagnosis model was evaluated by 10-fold cross-validation method. The results showed that the RVM model performed better than do existing algorithms, with 96.58% success rate. The automatic diagnosis on another dataset of 118985 samples of AF and Normal Sinus Rhythm (NSR) has yield 96.64% of classification accuracy. This automated data-driven decision making approach can be exploited for medical diagnosis of other arrhythmias

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

International audienceThe aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity

Heart rhythm characterization through induced physiological variables

International audienceAtrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy o