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ቤተ መጻሕፍቲ ባይዱ
Discussion
(1)Compared with previous E-noses, the testing time for one test was less than ten minutes, which has the advantage of fast determination.
(2)The concentrations were measured by a BP neural network while the odor intensity was measured by a model prediction.the relative errors of the chemical concentrations and odor intensity were 9.71% and 5.31%respectively. (3)The overall results of the study indicate the potential of the E-nose as a device for the determination of chemical concentrations and odor intensity of aromatic hydrocarbon mixtures.
targets
20 groups single gases test respectively.5-200mg/m3,interval was 10mg/m3
test the stability of the sensor array
determinate concentration
210groups including 60 single,45 binary 105 ternary.5-200mg/m3 E-nose determination training database(BPNs) the best parameters of the neural network were ascertained and their codes were written into the final software system.
select suitable sensors
0.4μl evaporate working solution inject in Enose
20mg/m3 gas
sensor array
select the sensors can response in at least one solution
September 2015 Volume 5 number 96
A novel electronic nose for simultaneous quantitative determination of concentrations and odor intensity analysis of benzene, toluene an dethylbenzene mixtures
we can find All RSD values were less than 7%, which show that the experiment had good precision.
PART 2
The results show that the E-nose system could determine respective concentrations of aromatic hydrocarbon mixtures simultaneously and it had a high accuracy relative to GC-FID. the BP neural network used'logsig'and'purelin'as transfer functions and 'trainlm' as the training function and was composed of 210 groups of training data, a 5 dimension input layer and a 3 dimension output layer, 6 hidden layers and 20 neurons in every layer.
be composed of gas sensors,a temperatuer(25±0 .5℃) sensor a humidity sensor(45-50%).
A cylindrical glass container (volume of 17.3 L) with a hole (diameter of 4 cm) in its lid worked as the gas vessel
relative concentrations were same as the test data then,predication models were employed to predict the odor intensity and the results were compared with the sniffed values, then the optimum models were determined.
BP neural network
Materials and Methods
1.Selection and characterization of the sensor array 2.Concentration determination
1.Sensor array for E-nose
2.E-nose system setup 3.Database measurement method
VOCs
Gas sensor array
Signal pretreatment (converter)
pattern recognition system
result
As the most significant component of an artificial olfaction system,it 's composed of metal oxide sensors,Catalytic combustion type and electrochemical type sensors
3.Odor intensity determination
Sensor array for E-nose: working gases: benzene, toluene and ethylbenzene with a purity > 99.9% (J & K Chemical Technology, China) GC-FID analysis condition:gas chromatography (GC-2014, Shimadzu, Japan) with a flame ionization detector and a Rtx-5 capillary column (30 m×0.25 mm ID,0.5 μm film thickness).
RESULTS
PART 1：
fig.2 shows that suitable sensors are MC119, MQ6 , TGS2610, 2M008 and WSP2620 .so these 5 sensors are selected to comprise in a sensor array.
pattern recogniton LOREM system
PCA,SVM,PLS were most used for qualitative analysis of multiple VOCs; ICA,SVD were most applied in quantitative analysis of a single gas;
Shen Jiang, Jiemin Liu,*Di Fang, Luchun Yan and Chuandong Wu
Reporter：
2015.12.05
Introduction
The E-nose systerm
convert electrical signals to response values
Thank you for your attention!
PART : 3
Weber-Fecher law
So these three models were used to predict the odor intensity. The total ARE was 5.31%, the Pearson correlation coefficient was 0.947 and significance of paired-sample T-test was 0.175.
pridiction of odor intensity
the odor sensory method each compound tested was respectively injected into an olfactory-bag (3L volume and full of clean air), when all the compounds had completely evaporated, an odor sample was prepared by transferring a certain quantity of the gas from the previous olfactory-bag to a new bag by an injector. Then 6 sniffing panelists evaluated the testing gas according to OIRS the odor intensity select the relative predication models and confirm the contants
test modicate optimise
80 groups including 24 single,27 binary 29 ternary.5-200mg/m3 test data(in test database) GC-FID determinate the same samples' concentration comparative analysis of GC-FLD's and Enose's results.
Discussion
(1)Compared with previous E-noses, the testing time for one test was less than ten minutes, which has the advantage of fast determination.
(2)The concentrations were measured by a BP neural network while the odor intensity was measured by a model prediction.the relative errors of the chemical concentrations and odor intensity were 9.71% and 5.31%respectively. (3)The overall results of the study indicate the potential of the E-nose as a device for the determination of chemical concentrations and odor intensity of aromatic hydrocarbon mixtures.
targets
20 groups single gases test respectively.5-200mg/m3,interval was 10mg/m3
test the stability of the sensor array
determinate concentration
210groups including 60 single,45 binary 105 ternary.5-200mg/m3 E-nose determination training database(BPNs) the best parameters of the neural network were ascertained and their codes were written into the final software system.
select suitable sensors
0.4μl evaporate working solution inject in Enose
20mg/m3 gas
sensor array
select the sensors can response in at least one solution
September 2015 Volume 5 number 96
A novel electronic nose for simultaneous quantitative determination of concentrations and odor intensity analysis of benzene, toluene an dethylbenzene mixtures
we can find All RSD values were less than 7%, which show that the experiment had good precision.
PART 2
The results show that the E-nose system could determine respective concentrations of aromatic hydrocarbon mixtures simultaneously and it had a high accuracy relative to GC-FID. the BP neural network used'logsig'and'purelin'as transfer functions and 'trainlm' as the training function and was composed of 210 groups of training data, a 5 dimension input layer and a 3 dimension output layer, 6 hidden layers and 20 neurons in every layer.
be composed of gas sensors,a temperatuer(25±0 .5℃) sensor a humidity sensor(45-50%).
A cylindrical glass container (volume of 17.3 L) with a hole (diameter of 4 cm) in its lid worked as the gas vessel
relative concentrations were same as the test data then,predication models were employed to predict the odor intensity and the results were compared with the sniffed values, then the optimum models were determined.
BP neural network
Materials and Methods
1.Selection and characterization of the sensor array 2.Concentration determination
1.Sensor array for E-nose
2.E-nose system setup 3.Database measurement method
VOCs
Gas sensor array
Signal pretreatment (converter)
pattern recognition system
result
As the most significant component of an artificial olfaction system,it 's composed of metal oxide sensors,Catalytic combustion type and electrochemical type sensors
3.Odor intensity determination
Sensor array for E-nose: working gases: benzene, toluene and ethylbenzene with a purity > 99.9% (J & K Chemical Technology, China) GC-FID analysis condition:gas chromatography (GC-2014, Shimadzu, Japan) with a flame ionization detector and a Rtx-5 capillary column (30 m×0.25 mm ID,0.5 μm film thickness).
RESULTS
PART 1：
fig.2 shows that suitable sensors are MC119, MQ6 , TGS2610, 2M008 and WSP2620 .so these 5 sensors are selected to comprise in a sensor array.
pattern recogniton LOREM system
PCA,SVM,PLS were most used for qualitative analysis of multiple VOCs; ICA,SVD were most applied in quantitative analysis of a single gas;
Shen Jiang, Jiemin Liu,*Di Fang, Luchun Yan and Chuandong Wu
Reporter：
2015.12.05
Introduction
The E-nose systerm
convert electrical signals to response values
Thank you for your attention!
PART : 3
Weber-Fecher law
So these three models were used to predict the odor intensity. The total ARE was 5.31%, the Pearson correlation coefficient was 0.947 and significance of paired-sample T-test was 0.175.
pridiction of odor intensity
the odor sensory method each compound tested was respectively injected into an olfactory-bag (3L volume and full of clean air), when all the compounds had completely evaporated, an odor sample was prepared by transferring a certain quantity of the gas from the previous olfactory-bag to a new bag by an injector. Then 6 sniffing panelists evaluated the testing gas according to OIRS the odor intensity select the relative predication models and confirm the contants
test modicate optimise
80 groups including 24 single,27 binary 29 ternary.5-200mg/m3 test data(in test database) GC-FID determinate the same samples' concentration comparative analysis of GC-FLD's and Enose's results.