KEYWORDS: Nose, Prototyping, Sensors, Principal component analysis, Semiconductors, Signal processing, Chemical analysis, Data transmission, System identification, Statistical analysis
The paper describes a principle of operation of odor nuisance monitoring network, which is being designed in the tri-city agglomeration. Moreover, it presents the preliminary results of an investigation on ambient air quality with respect to odour nuisance in a vicinity of the municipal landfill. The investigation was performed during spring-winter season using a prototype of electronic nose and the Nasal Ranger field olfactometers. The prototype was equipped with a set of six semiconductor sensors by FIGARO Co. and one PID-type sensor. The field olfactometers were used to determine mean concentration of odorants, which amounted from 2.2 to 30.2 ou/m3 depending on the place of measurement. In case of the investigation with the electronic nose prototype a classification of the ambient air samples with respect to the place of sampling was performed utilizing kNN algorithm supported with a cross-validation method. Correct classification of the ambient air samples was at the level of 66.7%. Performed investigation revealed that discrimination of the ambient air samples differing in concentration of odorants and place of origin was possible.
KEYWORDS: Nose, Sensors, Prototyping, Semiconductors, Principal component analysis, Chemical analysis, Statistical analysis, Data analysis, Signal processing, Control systems
The paper presents the results of investigation on ambient air odor quality in a vicinity of the industrial sewage treatment plant being a part of the crude oil processing plant. The investigation was performed during spring-winter season using a prototype of electronic nose and the Nasal Ranger field olfactometers. The prototype was equipped with a set of six semiconductor sensors by FIGARO Co. and one PID-type sensor. The field olfactometers were used to determine mean concentration of odorants, which amounted from 2.2 to 20.2 ou/m3 depending on the place of measurement. In case of the investigation with the electronic nose prototype a classification of the ambient air samples with respect to the place of sampling was performed utilizing the kNN (where k=3) algorithm supported with a cross-validation method. Correct classification of the ambient air samples was at the level of 47.9%. Performed investigation revealed that evaluation of the ambient air samples with respect to odor was possible using the electronic nose instrument.
The paper presents the results of investigation on quality evaluation of agricultural distillates using a
prototype of electronic nose instrument and a commercial electronic nose of Fast/Flash GC type–
HERACLES II. The prototype was equipped with TGS type semiconductor sensors. HERACLES II included two chromatographic columns with different polarity of stationary phase and two FID detectors. In case of the prototype volatile fraction of the agricultural distillate was prepared via barbotage process, whereas HERACLES II analysed the headspace fraction. Classification of the samples into three quality classes was performed using: quadratic discriminant function (QDA), supported with cross-validation method. Over 95% correct classification of the agricultural distillates into particular quality classes was observed for the analyses with HERACLES II. The prototype of electronic nose provided correct classification at the level of 70%.
The paper presents and compares the results of investigation on classification of atmospheric air samples
collected in a vicinity of municipal landfill with respect to their odour nuisance. The research was conducted
using a prototype of electronic nose instrument and a commercial electronic nose of Fast/Flash GC type –
HERACLES II. The prototype was equipped with six semiconductor sensors of TGS type. Classification of
the air samples with respect to the place of collection relative to the landfill was performed using quadratic
discriminant function (QDA) supported with cross-validation method. More than 80% of the samples were
correctly classified employing the analysis with HERACLES II. The prototype of electronic nose provided
correct classification of 50% of the samples.
Results of impedance measurements of humidity sensors with epoxy resins containig quarternary ammonium salts are presented in this paper. The humidity sensitive membranes were prepared from polyethyleneimine (PEI). PEI was cross-linked using 1,4-butanediol diglycidyl ether (BDDGE) and glycidyl trimethyl ammonium chloride (GTMAC) was added as a humidity sensitive epoxy monomer.
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