Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

Oliveira, Luciana Fontes de; Mallafré-Muro, Celia; Giner, Jordi; Perea, Lídia; Sibila, Oriol; Pardo, Antonio; Marco, Santiago

doi:10.1016/j.cca.2021.12.019

Cited by 16 publications

(2 citation statements)

References 65 publications

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“…Under an input laser power of 1.2 W, we did not observe a considerable decrease in the reflectivity of the mirrors in this work. The sensitivity of this CERS instrument is almost comparable to other advanced methods with ppbv or even sub-ppbv sensitivities in trace-gas detection, such as gas chromatography–mass spectrometry, , electrochemical sensor, , photoacoustic spectroscopy, , and cavity-enhanced absorption spectroscopy. , Compared to these methods, the CERS instrument could simultaneously detect multiple gases without pretreatment of the sample gas, allowing for wide applications in many vital fields. In the following section, we measured ambient air, natural gas, and SF 6 reference gas samples as a demonstration of their real-world applications.…”

Section: Performance Testmentioning

confidence: 89%

Multiple Gas Detection by Cavity-Enhanced Raman Spectroscopy with Sub-ppm Sensitivity

Yang

Tan

Qu³

et al. 2023

Anal. Chem.

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Accurate and sensitive detection of multicomponent trace gases below the parts-per-million (ppm) level is needed in a variety of medical, industrial, and environmental applications. Raman spectroscopy can identify multiple molecules in the sample simultaneously and has excellent potential for fast diagnosis of various samples, but applications are often limited by its sensitivity. In this contribution, we report the development of a cavity-enhanced Raman spectroscopy instrument using a narrow-line width 532 nm laser locked with a high-finesse cavity through a Pound-Drever-Hall locking servo, which allows continuous measurement in a broad spectral range. An intracavity laser power of up to 1 kW was achieved with an incident laser power of about 240 mW, resulting in a significant enhancement of the Raman signal in the range of 200–5000 cm–1 and a sub-ppm sensitivity for various molecules. The technique is applied in the detection of different samples, including ambient air, natural gas, and reference gas of sulfur hexafluoride, demonstrating its capability for the quantitative measurement of various trace components.

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Section: Performance Testmentioning

confidence: 89%

Multiple Gas Detection by Cavity-Enhanced Raman Spectroscopy with Sub-ppm Sensitivity

Yang

Tan

Qu³

et al. 2023

Anal. Chem.

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“…Obviously, in terms of the accuracy of the obtained results, they do not match the chromatographic techniques; however, in cases requiring, for example, quick reaction of industrial facilities, staff sensor arrays are an excellent alternative. Their high application potential has been proven for processes such as monitoring of biofiltration [40][41][42][43][44][45][46], monitoring of methane reforming process [47], monitoring of sanitary conditions [48], monitoring and identification of air pollutants and hazardous substances [49][50][51][52], quality assessment of food products [53][54][55][56][57][58], detection of drugs and explosives [59][60][61], in medicine as a noninvasive diagnostics systems for analysis of breath or urine [62][63][64][65][66], to control indoor air quality [67][68][69] or in the perfume industry to confirm the authenticity of products [55,70].…”

Section: Chemical Compoundmentioning

confidence: 99%

Determination of Odor Air Quality Index (OAQII) Using Gas Sensor Matrix

2022

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This article presents a new way to determine odor nuisance based on the proposed odor air quality index (OAQII), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed OAQII. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model.

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Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

Li,

et al. 2024

J Sci Food Agric

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BACKGROUNDThe total volatile basic nitrogen (TVB‐N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB‐N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB‐N, in this article, a self‐developed electronic nose (e‐nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB‐N value was established to complete the freshness detection.RESULTSThe TVB‐N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short‐term memory (LSTM) models, and finally, the final TVB‐N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD‐LSTM model optimized by SSA in predicting TVB‐N. The coefficient of determination (R2), the root‐mean‐squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively.CONCLUSIONSThis method improves the performance of e‐nose in detecting the freshness of fish meal and provides a reference for the quality detection of e‐nose in other materials. © 2024 Society of Chemical Industry.

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Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

Cited by 16 publications

References 65 publications

Multiple Gas Detection by Cavity-Enhanced Raman Spectroscopy with Sub-ppm Sensitivity

Multiple Gas Detection by Cavity-Enhanced Raman Spectroscopy with Sub-ppm Sensitivity

Determination of Odor Air Quality Index (OAQII) Using Gas Sensor Matrix

Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

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