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- Electronic nose (9)
- Voltammetric electronic tongue (5)
- SPME-GC-MS (3)
- Adulteration detection (2)
- Chemometric techniques (2)
- Cumin discrimination (2)
- Gas sensors (2)
- Geographic differentiation (2)
- Geographical origins (2)
- Saffron (2)
Organisationseinheit der BAM
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 customer's 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 b-pinene, m-cymene, g-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 sensor's performance as well as chemometric tools in order to deal with a complex dataset.
This work presents the testing of a developed elec¬tronic nose for outdoor odour nuisance monitoring. The sensor system consists of a sampling system, a measuring cell equipped with commercially available low-cost gas sensor elements and a data acquisition system. The sensor testing showed that the developed prototype is able to distinguish, identify and partially quantify individual odorous substances like ethanol, ammonia and hydrogen sulphide. Ethanol and ammonia could even be detected below or near their odour threshold.
Different electronic sensor systems coupled with multivariate data analysis were applied to characterize and classify seven saffron samples and to verify their declared geographical origin. The proposed electronic sensing consists of a low-cost electronic nose (E-nose) based on metal oxide semiconductor sensors and a voltammetric electronic tongue (VE-tongue) based on voltammetric sensors. The ability of multivariable analysis methods such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Support Vector Machines (SVMs) to classify the saffron samples according to their geographical origin have been investigated. Both PCA and HCA have shown an overlapping of E-nose responses. Moreover, the SVM analysis of the E-nose database reached a 66.07% success rate in the recognition of the saffron sample odour. On the other hand, good discrimination has been reached using PCA and HCA in the VE-tongue characterization case, besides a 100% accuracy in the saffron flavour recognition was attained. To validate the proposed electronic sensing systems, analytical chemical methods such as SPME-GC-MS and UV-Vis spectrophotometry were used. These analytical methods could be helpful tools to identify the composition of volatile compounds of the analysed saffron samples. Moreover, UV-Vis spectrophotometry was also used to determine the non-volatile profile of the samples from different geographic origins. It is demonstrated that the electronic sensing systems' findings are in a satisfactory correlation with the analytical methods. In the light of these results, we might say that the electronic systems offer a fast, simple and efficient tool to recognize the declared geographical origin of the saffron samples.
The objective of this work is to report the improvement obtained in the discrimination of complex aroma and taste with subtle differences in odor and flavor, by the use of fast suitable procedures for the cases of measurements in the field demanding decision-making in real time. The proposed electronic sensing systems, formed by an E-nose and VE-tongue, were used to discriminate cumin cultivars from different geographical origins, as well as to quantify the cumin adulteration percentages by using unsupervised and supervised chemometric tools. Thus, the results obtained are sufficiently encouraging as a starting point for the development of new electronic sensing systems with more improvement such the reliability of the sensors’ performance as well as the chemometric tools in order to deal with a complex dataset.
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, SPMEGC-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.
The presence of wastewater and air pollution has become an important risk factor for citizens, not only in terms of problems related to health risks, but also because of its negative impact on the country's image. For this reason, malodorous emission monitoring and control techniques are in high demand in urban areas and industries. The aim of this work is first to build an electronic nose (e-nose) and a Voltammetric Electronic tongue (VE-tongue) in order to study their ability to discriminate between polluted and clean environmental samples. Secondly, Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), and Solid Phase Micro Extraction-Gas Chromatography–Mass Spectrometry (SPME-GC-MS) are utilized to explain this discrimination by identifying specific compounds from these samples. Indeed, the e-nose, consisted of metal oxide semiconductor gas sensors, is used for the assessment of the studied odorous air and headspace samples from water and wastewater sites. Moreover, the VE-tongue, based on metal electrodes, is utilized to determine the patterns of the sensor array responses, which serve as fingerprints profiles of the analyzed liquid samples. Chemometric tools, such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs) are operated for the processing of data from the e-nose and the VE-tongue. By using the both systems, the analyses of headspace and liquid samples from the seven sites allow better discrimination. To explain the cause of the obtained discrimination, TD-GC-MS and SPME-GC-MS analyses are well performed to identify compounds related sites. According to these outcomes, the proposed e-nose and VE-tongue are proved to be rapid and valuable tools for analysis of environmental polluted matrices.
In this study, the ability of an electronic nose developed to analyze and monitor odor emissions from three poultry farms located in Meknes (Morocco) and Berlin (Germany) was evaluated. Indeed, the potentiality of the electronic nose (e-nose) to differentiate the concentration fractions of hydrogen sulfide, ammonia, and ethanol was investigated. Furthermore, the impact change of relative humidity values (from 15% to 67%) on the responses of the gas sensors was reported and revealed that the effect remained less than 0.6%. Furthermore, the relevant results confirmed that the developed e-nose system was able to perfectly classify and monitor the odorous air of poultry farms.
In this work, we demonstrate the ability of an electronic nose system based on an array of six-semiconductor gas sensors for outdoor air quality monitoring over a day at a traffic road in downtown of Meknes city (Morocco). The response of the sensor array reaches its maximum in the evening of the investigated day which may due to high vehicular traffic or/and human habits resulting in elevated concentrations of pollutants. Dataset treatment by Principal Component Analysis and Discriminant Function Analysis shows a good discrimination between samples collected at different times of the day. Moreover, Support Vector Machines were used and reached a classification success rate of 97.5 %. Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS) technique was used to validate the developed e-nose system by identifying the composition of the analyzed air samples. The discrimination obtained by e-nose system was in good agreement with the TD-GC-MS results. This study demonstrates the usefulness of TD-GC-MS and e-nose, providing high accuracy in discriminating outdoor air samples collected at different times. This demonstrates the potential of using the e-nose as a rapid, easy to use and inexpensive environmental monitoring system.
Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.
Wastewater contains harmful chemicals and heavy metals that are known to cause various environmental and health problems. Therefore, the water quality control using sensitive, simple, fast, accurate, and portable tools is of great importance. This study aimed to evaluate the pollution parameters of wastewaters collected at different treatment stages from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewaters by using an electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) combined with chemometric techniques. Water and wastewater pollution parameters determination were performed using inductively coupled plasma optical emission spectrometry for the determination of cations, and anions by using ion chromatography. Chemometric techniques, such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to process the E-nose and E-tongue datasets to describe the similarities between the samples. In addition, Partial Least Squares Regression (PLSR) model was constructed using electronic sensing data to simultaneously predict the concentration values of physicochemical parameters. The obtained correlation coefficient, for training and testing sets, is higher than 0.91 for the prediction of the concentration of all physicochemical parameters, except for iron (Fe) which remains 0.84. These results suggest that simple, portable, and inexpensive tools such as electronic nose and tongue are suitable for wastewater analysis.