7 research outputs found
The new approach to a pattern recognition of volatile compounds: the inflammation markers in nasal mucus swabs from calves using the gas sensor array
This paper discusses the application of two approaches (direct and inverse) to the identification of volatile substances by means of a gas sensor array in a headspace over nasal mucus swab samples taken from calves with differing degrees of respiratory damage. We propose a unique method to visualize sensor array data for quality analysis, based on the spectra of cross mass sensitivity parameters. The traditional method, which requires an initial sensor array trained on the vapors of the individual substances (database accumulation)-with their further identification in the analyzed bio-samples through the comparison of the analysis results to the database-has shown unsatisfactory performance. The proposed inverse approach is more informative for the pattern recognition of volatile substances in the headspace of mucus samples. The projection of the calculated parameters of the sensor array for individual substances in the principal component space, acquired while processing the sensor array output from nasal swab samples, has allowed us to divide animals into groups according to the clinical diagnosis of their lung condition (healthy respiratory system, bronchitis, or bronchopneumonia). The substances detected in the gas phase of the nasal swab samples (cyclohexanone, butanone-2,4-methyl-2-pentanone) were correlated with the clinical state of the animals, and were consistent with the reference data on disease markers in exhaled air established for destructive organism processes
E-nose for the monitoring of plastics catalytic degradation through the released volatile organic compounds (VOCs) detection
The work presents the results of an artificial olfaction E-nose system application for detection of Volatile Organic Compounds (VOCs) β destruction products of OXO-biodegradable polyethylene films when exposed to Ultraviolet (UV) irradiation and heating. The E-nose system was based on eight piezoelectric quartz resonator sensors coated with polymeric sensing materials with various selectivity to analytes. The effect of prooxidants, materials based on variable valence metals, such as D2w, ferric stearate and ferric carboxylate, addition on polyethylene films (PE) photo destruction was studied. The dependence of the composition of VOCs mixtures emitted by PE films on the processing time, the power of the UV irradiation and on the nature of the modifying additive was established. In addition, the main emitted volatile compounds were identified and the dynamics of their formation for different catalysts during the plastics destruction were studied. The proposed E-nose system has proved to be an effective tool for assessing catalyst-prooxidants properties. Β© 2020 Elsevier B.V
Electronic taster applied for identification of a rainbow trout spoilage specifics
The research is aimed at the study of control possibilities of freshness degree and storage technology violation of frozen fish (Rainbow trout) using chemical piezo sensors array in the Β«e-noseΒ» system. There have been demonstrated the opportunities of qualitative and quantitative determination of priority highly volatile compounds-markers of native and modified state of fish and gills after a 2-minute measuring by sensors array with minimal sample preparation and without additional preparation or odour components concentration. We can fixate early signs of changes in the fish fillet of a trout after three days of storage mode non-compliance and subsequent freezing, using chemical sensors. We can evaluate the storage time of fresh fish in any conditions with an error of measurement of no more than 10 %. We have developed the method of fish odour simple analysis and of obtaining diagnostic information about fish freshness according to fillet and gills state. The application mode of chemical sensors and e-nose Β«MAG-8Β» allows acquiring objective information, and is highly sensitive, expressive and economically acceptable for laboratories and mobile monitoring stations of any level
Study of the chemical composition of the smell of the dairy-vegetable extract of lupine on the βPiezoelectronic noseβ
A great popularity in the food industry in recent years has received lupins due to a unique combination of technological and food properties and accessibility. Lupine is characterized by a high proportion of proteins, its seeds are dominated by readily soluble protein fractions: 20.65% albumins, 50.5% globulins with a high content of essential amino acids. Enrichment of the milk-vegetable extract with native components of lupine is carried out during extraction with intensification by its low-frequency mechanical oscillations. The high content of proteins in the lupine during processing leads to a negative technological change in the organoleptic properties of products - odor. The measurement of the composition of the volatile fraction of odor in the equilibrium gas phase over the samples was carried out in the NIL on the experimental analyzer of smells "MAG 8" with the methodology "electronic nose" (manufactured by LLC Sensorika - New Technologies, Voronezh). To establish the differences in the composition (qualitative and quantitative) of the volatile fraction of smell, the change in the total content of volatile components in the equilibrium gas phase over the samples was traced. According to the shape of the "visual fingerprint" of the maximum responses of all sensors in the array, there are no significant differences in the chemical composition of the equilibrium gas phase over the samples. The native smell of milk whey remained unchanged, but became more qualitatively softer with a tasting assessment due to the fact that 50% of the composition of the volatile fraction of odor was changed by the method of pasteurization. The study of the smell of the native and pasteurized milk and vegetable extract allows us to conclude that the chosen method of pasteurization can be recommended in food technology using a milky-vegetable extract enriched with lupine proteins
βElectronic noseβ signals correlation evaluation for nasal mucus and exhaled breath condensate of calves with the clinical and laboratory indicators
Submitted 18 November 2019, received in revised form 2 December 2019ΠΠΎΡΡΡΠΏΠΈΠ»Π° Π² ΡΠ΅Π΄Π°ΠΊΡΠΈΡ 18 Π½ΠΎΡΠ±ΡΡ 2019 Π³., ΠΏΠΎΡΠ»Π΅ ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ 2 Π΄Π΅ΠΊΠ°Π±ΡΡ 2019 Π³.In this article the βelectronic noseβ system (SNT LLC, Russia) with an array of 8 differently selective piezoelectric sensors (nanobio array) was used to assess the health and functioning of the respiratory organs in young cattle by the fraction of volatile compounds over bioassays (exhaled breath condensate and nasal mucus). The sorption of the volatile fraction of substances vapors from the two types of bioassays was studied for 80s with the frontal effortless injection of vapors into the near-sensor space of the detection cell of the βelectronic noseβ at 20 Β± 1 .C with the subsequent fixation of the spontaneous desorption for 120s -total measurement time 200s . The simplest analytical signals of the βelectronic noseβ (S. ΠΈ S neg) recorded and calculated in the software for the samples were proposed suitable for assessing the health of the respiratory organs in calves. A significant correlation was found between the analytical signals of the βelectronic noseβ and the established informative indicators of bovine respiratory diseases: increased activity of aspartate aminotransferase, alanine aminotransferase, creatinine in samples of exhaled breath condensate, respiratory failure index, and leukocyte count. The samples of nasal mucus were better used for the health assessment of the respiratory system using the nanobio array of sensors. Despite the initial small number of samples, the approach is universal and could be extended to the studies of other animals.ΠΠ±ΡΡΠΆΠ΄Π°Π΅ΡΡΡ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΡ
Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΌΠ°ΡΡΠΈΠ²Π° ΡΠ΅Π½ΡΠΎΡΠΎΠ² (ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ ΠΌΠ½ΠΎΒΠ³ΠΎΠΌΠ΅ΡΠ½ΡΡ
Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΡΠ΅Π½ΡΠΎΡΠΎΠ² β Β«Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠΏΠ΅ΡΠ°ΡΠΊΠΎΠ²Β») Π² ΠΏΠ°ΡΠ°Ρ
Π±ΠΈΠΎΠΏΡΠΎΠ± ΡΠ΅Π»ΡΡ (Π½ΠΎΡΠΎΠ²Π°Ρ ΡΠ»ΠΈΠ·Ρ, ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°Ρ Π²ΡΠ΄ΡΡ
Π°Π΅ΠΌΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄ΡΡ
Π°) Ρ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ, ΡΠ²ΡΠ·Π°Π½Π½ΡΠΌΠΈ Ρ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΈΠ»ΠΈ Π½Π°Π»ΠΈΡΠΈΠ΅ΠΌ Π²ΠΎΠ·Π±ΡΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ, ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°ΡΡΠΈΠΌ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΡΠ΅ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΈΠ³Π½Π°Π»Ρ ΠΌΠ°ΡΡΠΈΠ²Π° ΡΠ΅Π½ΡΠΎΡΠΎΠ² Π΄Π»Ρ ΡΠ°Π·Π½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² Π±ΠΈΠΎΠΏΡΠΎΠ± Π½Π΅ ΠΊΠΎΡΡΠ΅Π»ΠΈΡΡΡΡ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΠ±ΠΎΠΉ, Π½ΠΎ ΠΏΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΄ΠΎΠ»ΠΈ ΡΡΡΠ΄Π½ΠΎ Π΄Π΅ΡΠΎΡΠ±ΠΈΡΡΡΡΠΈΡ
ΡΡ Π²Π΅ΡΠ΅ΡΡΠ² (ΠΊΠΈΡΠ»ΠΎΡΡ, ΡΠ°Π·Π²Π΅ΡΠ²Π»Π΅Π½Π½ΡΠ΅ Π°Π»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅, ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π°ΠΌΠΈΠ½Ρ) Π² ΠΎΠ±ΡΠ΅ΠΉ ΡΠΌΠ΅ΡΠΈ Π»Π΅Π³ΠΊΠΎΠ»Π΅ΡΡΡΠΈΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΎΡΠ΅Π½Π΅Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π·Π°ΠΌΠ΅Π½Ρ ΠΏΡΠΎΠ± ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠ° Π²ΡΠ΄ΡΡ
Π°Π΅ΠΌΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄ΡΡ
Π° Π½Π° ΠΏΡΠΎΠ±Ρ Π½ΠΎΡΠΎΠ²ΠΎΠΉ ΡΠ»ΠΈΠ·ΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΎΡΠ±ΠΈΡΠ°Π΅ΡΡΡ Π±ΡΡΡΡΠΎ ΠΈ ΠΌΠ΅Π½Π΅Π΅ ΡΡΠ°Π²ΠΌΠ°ΡΠΈΡΠ½ΠΎ, Π΄Π»Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π·Π΄ΠΎΡΠΎΠ²ΡΡ Π²Π΅ΡΡ
Π½ΠΈΡ
Π΄ΡΡ
Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΡΠ΅ΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π½ΡΠΊΠ° ΠΊΡΡΠΏΠ½ΠΎΠ³ΠΎ ΡΠΎΠ³Π°ΡΠΎΠ³ΠΎ ΡΠΊΠΎΡΠ°. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠ°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ ΠΎΡΠΊΠ»ΠΈΠΊΠ°ΠΌΠΈ ΠΌΠ°ΡΡΠΈΠ²Π° ΡΠ΅Π½ΡΠΎΡΠΎΠ² Π΄Π»Ρ ΠΏΡΠΎΠ± Π½ΠΎΡΠΎΠ²ΠΎΠΉ ΡΠ»ΠΈΠ·ΠΈ ΠΈ Π±ΠΈΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡ ΠΊΠ»Π΅ΡΠΎΠΊ ΡΠ΅ΡΠΏΠΈΡΠ°ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΠΊΡΠ° Π² ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠ΅ Π²ΡΠ΄ΡΡ
Π°Π΅ΠΌΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄ΡΡ
Π° (Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π°ΡΠΏΠ°ΡΡΠ°ΡΠ°ΠΌΠΈΠ½ΠΎΡΡΠ°Π½ΡΡΠ΅ΡΠ°Π·Ρ, Π°Π»Π°Π½ΠΈΠ½Π°ΠΌΠΈΠ½ΠΎΡΡΠ°Π½ΡΡΠ΅ΡΠ°Π·Ρ ΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΊΡΠ΅Π°ΡΠΈΠ½ΠΈΠ½Π°), ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠΌ Π΄ΡΡ
Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ, ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ΠΌ Π»Π΅ΠΉΠΊΠΎΡΠΈΡΠΎΠ² Π² ΠΊΡΠΎΠ²ΠΈ. ΠΡΠΊΠ»ΠΈΠΊΠΈ ΡΠ΅Π½ΡΠΎΡΠΎΠ² Π΄Π»Ρ ΠΏΡΠΎΠ± Π½ΠΎΡΠΎΠ²ΠΎΠΉ ΡΠ»ΠΈΠ·ΠΈ ΡΠ²Π»ΡΡΡΡΡ Π±ΠΎΠ»Π΅Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ Π΄Π»Ρ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠΏΠΈΡΠ°ΡΠΎΡΠ½ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ Ρ ΡΠ΅Π»ΡΡ.This work was financially supported by the Russian Science Foundation (grant number 18-76-10015).Π Π°Π±ΠΎΡΠ° Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π³ΡΠ°Π½ΡΠ° Π ΠΠ€ β 18-76-10015