MOS–MOSFET gas sensors array measurements versus sensory and chemical characterisation of VOC’s emissions from car seat foams

Morvan, M; Talou, Thierry; Beziau, Jean-François

doi:10.1016/s0925-4005(03)00425-8

Cited by 30 publications

(19 citation statements)

References 13 publications

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“…However, the variation was random and not significant; high standard deviation was found across various sample volumes (50-500 L) but not across vials in the carousel. This was also confirmed in the literature [32,33]. Almost all MOSFET sensors of e-nose 1 were found to be replicate dependent and almost all MOS sensors varied over time.…”

Section: Mosfet/mos E-nose Characterisationsupporting

confidence: 83%

Methodological variation in headspace analysis of liquid samples using electronic nose

Knobloch

Turner

Spooner

et al. 2009

Sensors and Actuators B: Chemical

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a b s t r a c tIn past years, numerous electronic nose (e-nose) developments have been published describing analyses of solid-, liquid-or gaseous media in microbiological-, environmental-, agricultural-or medical applications. However, little has been reported about complex methodological pitfalls that might be associated with commercially available e-nose technology. In this paper, some of these pitfalls such as temperature, the use of filters and mass flow using different sampling methods (static-and dynamic sampling) are described for two generations of conducting polymer e-noses (ST114/214, CPs, both Scensive Tech. Ltd.). A comparison with metal oxide semiconducting field effect transistor/metal oxide semiconductor (MOSFET/MOS) e-noses regarding stability across replicates and over time was made. Changes in temperature were found to give larger sensor responses, whereas the application of filters led to quantitative and qualitative changes in sensor responses due to a change in mass flow which was also affected by the sampling method. Static sampling provided more stable flows across replicates. Variation was investigated for CPs and MOSFET/MOS e-noses that gave different responses over time and across replicates. These methodological factors cause a lack of stability and reproducibility, demonstrating the pitfalls of e-nose technology and therefore limit their utility for discriminating between samples.

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Section: Mosfet/mos E-nose Characterisationsupporting

confidence: 83%

Methodological variation in headspace analysis of liquid samples using electronic nose

Knobloch

Turner

Spooner

et al. 2009

Sensors and Actuators B: Chemical

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“…Different kinds of tests have been performed, and examples are given in literature [4]- [7]. This paper shows that these undesirable gases can also be detected with a MOS sensor module and classified closely related to the human perception.…”

Section: Introductionmentioning

confidence: 99%

MEMS Gas-Sensor Array for Monitoring the Perceived Car-Cabin Air Quality

Blaschke¹,

Tille

Robertson³

et al. 2006

IEEE Sensors J.

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Microelectromechanical-system (MEMS) metaloxide gas sensors have reached a mature stage, which makes mass market applications in the automotive area possible. In contrast to the already established flap-control system, which controls the access of (combustion) gases from outside the vehicle to the car cabin, the system studied here detects odor events created within the car cabin. The events under study have been cigarette smoke, fast-food odor, manure, and bioeffluents (flatulence). As the reference cannot be a "simple analytical measurement," a human test panel for assessing the hedonic impression on a scale from 0 to 5 is used as reference. The technical system is a MEMS metal-oxide-sensor array consisting of three different sensors. The data-evaluation approach used here is combining the human-sensory data and the MEMS sensor data. The task is performed by the combination of two independent algorithms, where one is related to the normalized conductance and the other to signal variance. Using a combined approach has the advantage that "false" events are suppressed. After the algorithm was successfully transferred onto a microcontroller, real-life data were recorded and classified. Several practical examples are given in this paper. The overall gas-sensor system reaches good accordance with the human-sensory impression, which is represented by air quality levels. This enables the design of a demand-controlled ventilation system. Index Terms-Air quality, gas sensor, heating ventilation and air conditioning (HVAC), low power, microelectromechanical systems (MEMS), sensor array.

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“…Multiple factor analysis provides useful information on the main associations between the different groups of variables and was successfully used to explore the contribution of the sensory and laboratory data sets for food sample characterisation (Morvan et al ., ; Valente et al ., ). It was carried out on the combined data, namely intensity of sensory attributes describing aroma and flavour, intensity of sensory descriptors of texture, PUFA and SFA concentrations and laboratory descriptors of texture.…”

Section: Resultsmentioning

confidence: 99%

Enhanced utilisation of nonmarketable fish: physical, nutritional and sensory properties of ‘clean label’ fish burgers

Husein

Secci

Dinnella

et al. 2018

Int J of Food Sci Tech

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Summary Four ‘clean label’ formulations for fish burgers made with mechanically separated fish meat were characterised in sensory, textural and chemical terms. The formulations differed in the ratios of European sea bass to rainbow trout (50:50 and 30:70) and the ratios of fish to potato flakes (dry matter ratio, DMR: 2.5:1 and 1.5:1). The sensory profile was mainly influenced by DMR. Recipes with the higher DMR were positively correlated with sandy, dry and crusty attributes, salty taste and overall flavour. Soft texture was perceived for recipes with the lower DMR, although no differences in texture were detected by a texturometer. Lowering DMR increased ash and water content and decreased protein content, as expected. The results indicated that 100 g of burger provided more essential fatty acids than the recommended daily intake, irrespective of formulation. In conclusion, multiple factor analysis indicated that the main changes detected and perceived were due to DMR.

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MOS–MOSFET gas sensors array measurements versus sensory and chemical characterisation of VOC’s emissions from car seat foams

Cited by 30 publications

References 13 publications

Methodological variation in headspace analysis of liquid samples using electronic nose

Methodological variation in headspace analysis of liquid samples using electronic nose

MEMS Gas-Sensor Array for Monitoring the Perceived Car-Cabin Air Quality

Enhanced utilisation of nonmarketable fish: physical, nutritional and sensory properties of ‘clean label’ fish burgers

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