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Article

Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

1
Interdisciplinary Centre of Modern Technologies, Nicolaus Copernicus University in Toruń, 4 Wileńska St., 87-100 Toruń, Poland
2
“Raluca Ripan” Institute for Research in Chemistry, Babeş-Bolyai University, 30 Fântânele St., RO-400294 Cluj-Napoca, Romania
3
Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, 7 Gagarina St., 87-100 Toruń, Poland
4
Department of Lung Diseases, Provincial Polyclinic Hospital in Toruń, 4 Krasińskiego St., 87-100 Toruń, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Natalia Drabińska and Ben de Lacy Costello
Molecules 2021, 26(6), 1789; https://doi.org/10.3390/molecules26061789
Received: 24 February 2021 / Revised: 16 March 2021 / Accepted: 19 March 2021 / Published: 22 March 2021
Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs’ latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction. View Full-Text
Keywords: VOCs; NTD-GC-MS; breath; lung cancer; COPD; asthma; biomarkers VOCs; NTD-GC-MS; breath; lung cancer; COPD; asthma; biomarkers
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MDPI and ACS Style

Monedeiro, F.; Monedeiro-Milanowski, M.; Ratiu, I.-A.; Brożek, B.; Ligor, T.; Buszewski, B. Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles. Molecules 2021, 26, 1789. https://doi.org/10.3390/molecules26061789

AMA Style

Monedeiro F, Monedeiro-Milanowski M, Ratiu I-A, Brożek B, Ligor T, Buszewski B. Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles. Molecules. 2021; 26(6):1789. https://doi.org/10.3390/molecules26061789

Chicago/Turabian Style

Monedeiro, Fernanda, Maciej Monedeiro-Milanowski, Ileana-Andreea Ratiu, Beata Brożek, Tomasz Ligor, and Bogusław Buszewski. 2021. "Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles" Molecules 26, no. 6: 1789. https://doi.org/10.3390/molecules26061789

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