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.
Поступила в редакцию 27 февраля 2017 г., после исправления -20 марта 2017 г.Обсуждается влияние температуры, влажности, режимов измерения, собственных характе-ристик пьезорезонаторов, природы и массы сорбента, природы и содержания аналита, типа проб на выходные данные массива пьезосенсоров, в том числе на параметры, используемые для иден-тификации веществ в смесях, и пути устранения или минимизации этого влияния. Показано, что аналитическая информация «электронного носа» на пьезосенсорах не более зависима от условий эксперимента, чем популярные, широко распространенные методы анализа. Описана информатив-ность выходных сигналов массива сенсоров, используемых для идентификации веществ. Установ-лены идентификационные параметры массива пьезосенсоров для обнаружения аминов, органиче-ских кислот, спиртов, этилацетата, ацетона в равновесной газовой фазе над водными растворами. Продемонстрировано влияние порядка расположения сенсоров в массиве на значения трехэле-ментных идентификационных параметров. Предложена схема применения идентификационных параметров, в том числе неселективных, для обнаружения органических веществ по совпадению не менее двух параметров. Доказана возможность применения данных параметров для идентифи-кации аминов, кислот, спиртов, кетонов в равновесной газовой фазе над водными растворами их смесей. Данный подход характеризуется высокой чувствительностью и специфичностью и может быть использован для идентификации веществ в равновесной газовой фазе над пробами с боль-шим содержанием воды (кровь, моча, лимфа, пот, соки, напитки).Ключевые слова: пьезосенсоры, электронный нос, вещества-маркеры, аналитический сиг-нал, идентификационные параметры, информативность.For citation: Analitika i kontrol' [Analytics and Control], 2017, vol. 21, no. 2, pp. 72-84 DOI: 10.15826/analitika.2017.21.2.001 Informative nature of the electronic nose output signals based on the piezoelectric sensors Avenue,19, Voronezh, 394036, Russian Federation *Corresponding author: Anastasiia A. Shuba, Submitted 27 February 2017, received in revised form 20 March 2017 T.A. Kuchmenko and A.A. Shuba* Voronezh State University of Engineering Technologies, faculty of ecology and chemical technology, RevolutionThe purpose of this research was assessing the influence of the various factors on the output signals of the static "electronic nose" based on the piezoelectric sensors, and determining the informative nature of these signals for the identification and determination of the marker-substances related to the pathogenic processes in the equilibrium gas phase over the aqueous solutions. Individual substances contained in bio samples in the presence of pathogenic and neoplastic processes, such as ammonia, amines, carboxylic acids, ethanol, 1-butanol, acetone, ethyl acetate, phenol, hydrogen sulfide and water were selected as the marker-substances. The selective coating of sensors was chosen based on the results of the numerous studies for the living systems of different nature in order to determine the deviations from the norm, which in...
Development of electronic technologies for precise identification of fruit crop cultivars in agricultural production provides an effective means for assuring product quality and authentication. The capabilities of discriminating between grape (Vitis vinifera L.) cultivars is essential for assuring certification of varieties sold in world markets. Machine olfaction, based on electronic-nose (e-nose) technologies, is readily available for rapid identification of fruit and vegetative agricultural products. This technology relies on detection of and discrimination between volatile organic compound (VOC) emissions from plant parts. It may be used in all stages of agricultural production to facilitate crop maintenance, cultivation, and harvesting decisions prior to marketing. An experimental e-nose device was constructed and tested in combination with five chemometric methods, including PCA, LDA, QDA, SVM, and ANN, as rapid, non-destructive tools for identification and classification of grape cultivars. An e-nose instrument equipped with nine metal oxide semiconductor (MOS) sensors was utilized to identify and classify five grape cultivars based on leaf VOC emissions using supervised and non-supervised methods. Grape leaf samples were first identified as belonging to specific cultivar types using PCA analyses, which are non-supervised classification methods, with the first two principal components (PC-1 and PC-2) accounting for 89% of the total variance. Four supervised statistical methods were further tested, including DA, QDA, SVM, and ANN, and provided effective discrimination accuracies of 98%, 99%, 92%, and 99%, respectively. These findings confirmed the suitable applicability of an MOS e-nose sensor array with supervised methods for accurate identification of grape cultivars, which is useful for authentication of vine cultivar types for commercial markets.
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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