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Calibration of a Sensor Array (an Electronic Tongue) for Identification and Quantification of Odorants from Livestock Buildings

View Article: PubMed Central

ABSTRACT

This contribution serves a dual purpose. The first purpose was to investigate the possibility of using a sensor array (an electronic tongue) for on-line identification and quantification of key odorants representing a variety of chemical groups at two different acidities, pH 6 and 8. The second purpose was to simplify the electronic tongue by decreasing the number of electrodes from 14, which was the number of electrodes in the prototype. Different electrodes were used for identification and quantification of different key odorants. A total of eight electrodes were sufficient for identification and quantification in micromolar concentrations of the key odorants n-butyrate, ammonium and phenolate in test mixtures also containing iso-valerate, skatole and p-cresolate. The limited number of electrodes decreased the standard deviation and the relative standard deviation of triplicate measurements in comparison with the array comprising 14 electrodes. The electronic tongue was calibrated using 4 different test mixtures, each comprising 50 different combinations of key odorants in triplicates, a total of 600 measurements. Back propagation artificial neural network, partial least square and principal component analysis were used in the data analysis. The results indicate that the electronic tongue has a promising potential as an online sensor for odorants absorbed in the bioscrubber used in livestock buildings.

No MeSH data available.


Related in: MedlinePlus

Calibration curve of ammonium from 5 × 10-6 to 10-3 M at pH 6. PLS-1, full cross validation for 22 samples and two PCs were used and six electrodes were sufficient. Concentration of n-butyrate was below 10-4 M.
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f3-sensors-07-00103: Calibration curve of ammonium from 5 × 10-6 to 10-3 M at pH 6. PLS-1, full cross validation for 22 samples and two PCs were used and six electrodes were sufficient. Concentration of n-butyrate was below 10-4 M.

Mentions: Samples having ammonium concentrations equal to and higher than 5 × 10-6 M (23 samples including one outlier) could be modelled reasonably. PLS-1, full cross validation and two principal components were used and six electrodes (no. 2, 5, 6, 7, 8, 9) were sufficient. The principal components accounted for 92% and 93% of total validated variance of X and Y, respectively. Slope, correlation (r), RMSEP and RPD of the calibration curve were 0.93, 0.95, 0.26 and 3.35, respectively (Fig. 3). The model is an acceptable model, since the RPD is greater than 2.5, and it has a good slope and correlation. For modelling ammonium using BPNN, 23 samples in triplicates (69 samples) were split into train, test and validation sets, i.e. 33, 18 and 18, respectively. The BPNN used 6, 3, 1 nodes. Six electrodes were sufficient (no. 2, 5, 6, 7, 8, 9). Slope, correlation, RMSEP and RPD of the calibration curve were 0.92, 0.98, 0.18 and 4.40, respectively (Fig. 4). It is noticed that slope, correlation, RMSEP and RPD showed an improvement in the BPNN model compared to the PLS-1 model.


Calibration of a Sensor Array (an Electronic Tongue) for Identification and Quantification of Odorants from Livestock Buildings
Calibration curve of ammonium from 5 × 10-6 to 10-3 M at pH 6. PLS-1, full cross validation for 22 samples and two PCs were used and six electrodes were sufficient. Concentration of n-butyrate was below 10-4 M.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756715&req=5

f3-sensors-07-00103: Calibration curve of ammonium from 5 × 10-6 to 10-3 M at pH 6. PLS-1, full cross validation for 22 samples and two PCs were used and six electrodes were sufficient. Concentration of n-butyrate was below 10-4 M.
Mentions: Samples having ammonium concentrations equal to and higher than 5 × 10-6 M (23 samples including one outlier) could be modelled reasonably. PLS-1, full cross validation and two principal components were used and six electrodes (no. 2, 5, 6, 7, 8, 9) were sufficient. The principal components accounted for 92% and 93% of total validated variance of X and Y, respectively. Slope, correlation (r), RMSEP and RPD of the calibration curve were 0.93, 0.95, 0.26 and 3.35, respectively (Fig. 3). The model is an acceptable model, since the RPD is greater than 2.5, and it has a good slope and correlation. For modelling ammonium using BPNN, 23 samples in triplicates (69 samples) were split into train, test and validation sets, i.e. 33, 18 and 18, respectively. The BPNN used 6, 3, 1 nodes. Six electrodes were sufficient (no. 2, 5, 6, 7, 8, 9). Slope, correlation, RMSEP and RPD of the calibration curve were 0.92, 0.98, 0.18 and 4.40, respectively (Fig. 4). It is noticed that slope, correlation, RMSEP and RPD showed an improvement in the BPNN model compared to the PLS-1 model.

View Article: PubMed Central

ABSTRACT

This contribution serves a dual purpose. The first purpose was to investigate the possibility of using a sensor array (an electronic tongue) for on-line identification and quantification of key odorants representing a variety of chemical groups at two different acidities, pH 6 and 8. The second purpose was to simplify the electronic tongue by decreasing the number of electrodes from 14, which was the number of electrodes in the prototype. Different electrodes were used for identification and quantification of different key odorants. A total of eight electrodes were sufficient for identification and quantification in micromolar concentrations of the key odorants n-butyrate, ammonium and phenolate in test mixtures also containing iso-valerate, skatole and p-cresolate. The limited number of electrodes decreased the standard deviation and the relative standard deviation of triplicate measurements in comparison with the array comprising 14 electrodes. The electronic tongue was calibrated using 4 different test mixtures, each comprising 50 different combinations of key odorants in triplicates, a total of 600 measurements. Back propagation artificial neural network, partial least square and principal component analysis were used in the data analysis. The results indicate that the electronic tongue has a promising potential as an online sensor for odorants absorbed in the bioscrubber used in livestock buildings.

No MeSH data available.


Related in: MedlinePlus