"Electronic nose" signals correlation evaluation for nasal mucus and exhaled breath condensate of calves with the clinical and laboratory indicators

Кучменко, Т. А.; Шуба, А. А.; Умарханов, Р. У.; Черницкий, А. Е.; Therapy, Quality

doi:10.15826/analitika.2019.23.4.014

Cited by 2 publications

(5 citation statements)

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“…These sorbents were chosen among 50 phases due to them having hypersensitivity to various classes of highly volatile organic compounds (alcohols, aldehydes, acids, ketones, amines, and arenes) [ 36 , 37 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ], including volatile biomarkers of respiratory pathologies.…”

Section: Methodsmentioning

confidence: 99%

“…The metabolite profiles help understand the disease’s pathogenesis, and they can be used as biomarkers for diagnosis [ 29 , 30 , 31 , 32 , 33 ]. The most perspective direction in BRD diagnosis using metabolomics involves noninvasive methods when researching the metabolic profiles of exhaled breath gas [ 34 ], exhaled breath condensate [ 35 ], and nasal secretions [ 28 , 36 ]. One of such diagnostic methods is the analysis of the composition of the gaseous phases of biological samples (exhaled breath condensate, nasal secretions) using an array of piezoelectric sensors with the methodology of an “electronic nose” (e-nose) [ 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

“…The most perspective direction in BRD diagnosis using metabolomics involves noninvasive methods when researching the metabolic profiles of exhaled breath gas [ 34 ], exhaled breath condensate [ 35 ], and nasal secretions [ 28 , 36 ]. One of such diagnostic methods is the analysis of the composition of the gaseous phases of biological samples (exhaled breath condensate, nasal secretions) using an array of piezoelectric sensors with the methodology of an “electronic nose” (e-nose) [ 36 , 37 ]. Traditionally, the determination of BRD volatile markers in the gas phase over biological samples using an e-nose is carried out on the basis of sensor signals using pattern recognition algorithms or multivariate regression.…”

Section: Introductionmentioning

confidence: 99%

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Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Кучменко

Шуба

Умарханов

et al. 2021

Veterinary Sciences

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The paper demonstrates a new approach to identify healthy calves (“healthy”) and naturally occurring infectious bronchopneumonia (“sick”) calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14–42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 “healthy” and 20 “sick”) in the training sample. The remaining ten calves (five “healthy” and five “sick”) were included in the test sample. It was possible to divide calves into “healthy” and “sick” groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA–LDA) with an accuracy of 100%. The adequacy of the PCA–LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Кучменко

Шуба

Умарханов

et al. 2021

Veterinary Sciences

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“…The maximum changes in the vibration frequency of sensors, ∆Fmax (Figure 1c), were calculated from the sensors' chrono-frequency-grams registered with the device software, which were then used to calculate the parameters of sorption efficiency A(i/j) (where I, j are the numbers of sensors in the array) [37,38]. More details on A(i/j) parameter estimation is given in our recent work [39].…”

Section: Data Treatmentmentioning

confidence: 99%

“…The approach based on the calculation of A(i/j) sensor parameters of sorption efficiency has shown its effectiveness and reliability for solving identification tasks for individual highly volatile molecules in gas mixtures [29,[37][38][39]. The fundamental difference of the proposed approach to substance identification from the one proposed earlier for the analysis of equilibrium gas phases is that the referent values for substance identification are determined taking into account wide limits of substance concentrations (from 1 ppm to 10 ppm) and the number of parameters for which the calculated value falls into the reference values for the substance.…”

Section: Sensors Efficiency Parameters A(i/j) For Individual Volatile...mentioning

confidence: 99%

The New Approach to a Pattern Recognition of Volatile Compounds: The Inflammation Markers in Nasal Mucus Swabs from Calves Using the Gas Sensor Array

et al. 2021

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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.

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"Electronic nose" signals correlation evaluation for nasal mucus and exhaled breath condensate of calves with the clinical and laboratory indicators

Cited by 2 publications

References 0 publications

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

The New Approach to a Pattern Recognition of Volatile Compounds: The Inflammation Markers in Nasal Mucus Swabs from Calves Using the Gas Sensor Array

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