Relationship between odour intensity assessed by human assessor and TGS sensor array response

Szczurek, Andrzej; Maciejewska, Monika

doi:10.1016/j.snb.2004.05.031

Cited by 19 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The S I-S8 sensors can respond to ammonia and amine compounds as well as acetic acid whereas these sensors show low responses to organic solvents such as THF, ethanol acetone and water. The increasing of resistance with rising volatile concentration can be modelled by the Plateau Bretano-Stevens law [37][38] in accordance with the previous work [19]. The insets in Fig.3 demonstrate the radar plot of the percent sensor responses when the sensors were tested with 1000 ppm of ammonia, amines and acetic acid.…”

Section: Dimethylaminesupporting

confidence: 85%

Portable e-nose based on polymer/CNT sensor array for protein-based detection

Lorwongtragool

Seesaard

Tongta

et al. 2012

2012 7th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

View full text Add to dashboard Cite

Portable electronic nose (e-nose) consisting of polymer/carbon nanotube (CNT) sensor array was developed to detect protein-based foods. Gas sensors were fabricated by spin coating functionalized CNT/polymer nanocomposite materials onto interdigitated electrodes. The sensors were tested with various types of volatile compounds such as ammonia, amine compounds, acetic acid, water and organic solvents in the range of ppm level. It was found that most sensors yield strong signals to ammonia, amine compounds and acetic acid as well, while they present quite low response to organic solvents and water. To understand the relation of the interaction of amine species related to the sensor response, we have performed molecular modelling based on the density functional theory (DFT) on one polymer structure providing the best response to volatile ammonia. Based on the principal component analysis (PCA), this portable e-nose was successfully applied for the classification of the seafood releasing different amount of amine compounds.

show abstract

Section: Dimethylaminesupporting

confidence: 85%

Portable e-nose based on polymer/CNT sensor array for protein-based detection

Lorwongtragool

Seesaard

Tongta

et al. 2012

2012 7th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

View full text Add to dashboard Cite

show abstract

“…To avoid baseline inconsistency, the percentage change in resistance was used as detailed elsewhere. 20 In case of single component odor of amines, a power function known as the Plateau-Bretano-Stevens law [21][22] can be adopted to model the sensitivity.…”

Section: Discussionmentioning

confidence: 99%

An Electronic Nose for Amine Detection Based on Polymer/SWNT-COOH Nanocomposite

Lorwongtragooll

Wisitsoraat²,

Kerdcharoen³

2011

j nanosci nanotechnol

View full text Add to dashboard Cite

An electronic nose (e-nose) system based on polymer/carboxylic-functionalized single-walled carbon nanotubes (SWNT-COOH) was developed for sensing various volatile amines. The SWNT-COOH dispersed in the matrix of different polymers; namely, polyvinyl chloride (PVC), cumene terminated polystyrene-co-maleic anhydride (cumene-PSMA), poly(styrenecomaleic acid) partial isobutyl/methyl mixed ester (PSE), and polyvinylpyrrolidon (PVP), were deposited on interdigitated gold electrodes to make the gas sensors. The response of these sensors to volatile amines was studied by both static and dynamic flow measurements. It was found that all sensors exhibited behaviors corresponding to Plateau-Bretano-Stevens law (R2 = 0.81 to 0.99) as the response to volatile amines. Real-world application was demonstrated by applying this e-nose to monitor the odor of sun-dried snakeskin gourami that was pre-processed by salting-preservation. This electronic nose can discriminate sun-dried fish odors with different stored days using a simple pattern recognition based on the principal component analysis (PCA).

show abstract

“…[9][10][11] At present, the pattern-recognition system is the key part because it affects analysis results greatly. [20][21][22] When the target compound was changed, the whole E-nose system had to be rebuilt, requiring more effort. 9,[17][18][19] However, to the best of our knowledge, most existing E-noses always fail to achieve precise quantitative analysis of gas mixtures.…”

Section: Introductionmentioning

confidence: 99%

A novel electronic nose for simultaneous quantitative determination of concentrations and odor intensity analysis of benzene, toluene and ethylbenzene mixtures

et al. 2015

View full text Add to dashboard Cite

Aromatic hydrocarbons (benzene, toluene, ethylbenzene etc.) are part of the main components of air pollution and odor nuisance. However, previous studies on simultaneous detection of aromatic mixtures and odor intensity analysis by an electronic nose (E-nose) are limited. The aim of this study is to develop a novel E-nose system to simultaneously determine chemical concentrations and odor intensity of benzene, toluene and ethylbenzene mixtures. The system consists of a sensor array with 5 gas sensors, a signal converter and a pattern recognition system which is based on a Back Propagation (BP) neural network. 300 groups of aromatic hydrocarbon mixtures (benzene, toluene and ethylbenzene) with different concentrations were determined by sensor array and gas chromatography (GC) to build, test and optimize the BP neural network. Then the optimum structure and functions of the BP network were verified by about 50 runs of contrast tests. The results showed that the average relative error of concentrations measured by the E-nose system was 9.71% relative to the results of GC. Furthermore, six odor intensity prediction models were used to convert the concentrations of the aromatic mixtures to their odor intensity. Based on the comparison with sensory analysis, the Weber-Fechner law model, the vector model and the simplified extended vectorial model were adopted to predict the odor intensity of single, binary and ternary compounds respectively.

show abstract

Relationship between odour intensity assessed by human assessor and TGS sensor array response

Cited by 19 publications

References 7 publications

Portable e-nose based on polymer/CNT sensor array for protein-based detection

Portable e-nose based on polymer/CNT sensor array for protein-based detection

An Electronic Nose for Amine Detection Based on Polymer/SWNT-COOH Nanocomposite

A novel electronic nose for simultaneous quantitative determination of concentrations and odor intensity analysis of benzene, toluene and ethylbenzene mixtures

Contact Info

Product

Resources

About