Laser photoacoustic spectroscopy applications in breathomics

Yury V. Kistenev (Login required)
Tomsk State University, Russia
Siberian State Medical University, Tomsk, Russia

Alexey V. Borisov
Tomsk State University, Russia
Siberian State Medical University, Tomsk, Russia

Victor V. Nikolaev
Tomsk State University, Russia
Institute of Strength Physics and Materials Science of Siberian Branch of the RAS, Tomsk, Russia

Denis A. Vrazhnov
Tomsk State University, Russia
Institute of Strength Physics and Materials Science of Siberian Branch of the RAS, Tomsk, Russia

Dmytry A. Kuzmin
Siberian State Medical University, Tomsk, Russia

Paper #3307 received 25 Nov 2018; accepted for publication 20 Mar 2019; published online 28 Mar 2019.

DOI: 10.18287/JBPE19.05.010303


The breathomics approach to express-diagnosis of bronchopulmonary diseases based on spectral analysis of volatile organic compounds in a patient’s exhaled air is discussed. The basic demands and possible technical solutions to laser photoacoustic spectroscopy equipment in a framework of breathomics are presented. An example of differential diagnostics of the set of bronchopulmonary diseases, including lung cancer (LC) patients (N = 9); patients with chronic obstructive pulmonary disease (COPD) (N = 12); patients with pneumonia (N = 11) and a control group of healthy volunteers using breath air analysis by laser photoacoustic spectroscopy and machine learning is presented.


Breathomics; exhaled air analysis; laser photoacoustic spectroscopy; optical parametric oscillator; machine learning; lung cancer

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1. A. G. Dent, T. G. Sutedja, and P. V. Zimmerman, “Exhaled breath analysis for lung cancer,” Journal of Thoracic Disease 5, 540–550 (2013). Crossref

2. M. P. Fernandes, S. Venkatesh, and B. G. Sudarshan, “Early Detection of Lung Cancer Using Nano-Nose - A Review,” Open Biomedical Engineering Journal 9(1), 228–233 (2015). Crossref

3. F. Kühnemann, F.Müller, G. von Basum, D. Halmer, A. Popp, S. Schiller, P. Hering, and M. Muertz, “CW-OPO based cavity-leak-out spectrometer for ultra-sensitive and selective mid infrared trace gas analysis,” Proceedings of SPIE 5337, 117–127 (2004). Crossref

4. V. Nikolaev, V. N. Ochkin, and S. N. Tskhai, “Fast recording of weak absorption spectra in optical cavity using tunable laser,” Laser Physics Letters 10(11), 115701 (2013). Crossref

5. H. W. A. Berkelmans, B. W. M. Moeskops, J. Bominaar, P. T. J. Scheepers, and F. J. M. Harren, “Pharmacokinetics of ethylene in man by on-line laser photoacoustic detection,” Toxicology and Applied Pharmacology 190(3), 206–213 (2003). Crossref

6. C. Popa, A. M. Bratu, C. Matei, R. Cernat, A. Popescu, and D. C. Dumitras, “Qualitative and Quantitative Determination of Human Biomarkers by Laser Photoacoustic Spectroscopy Methods,” Laser Physics 21(7), 1336–1342 (2011). Crossref

7. Y. V. Kistenev, А. А. Karapuzikov, “Methods of spectral analysis of exhaled air suitable for routine diagnostics of diseases of the respiratory system,” Advanced biomaterials and devices in biomedicine 2, 79–87 (2015).

8. J. A. de Gouw, S. Te Lintel Hekkert, J. Mellqvist, C. Warneke, E. L. Atlas, F. C. Fehsenfeld, A. Fried, G. J. Frost, F. J. M. Harren, J. S. Holloway, B. Lefer, R. Lueb, J. F. Meagher, D. D. Parrish, M. Patel, L. Pope, D. Richter, C. Rivera, T. B. Ryerson, J. Samuelsson, J. Walega, R. A. Washenfelder, P. Weibring, and X. Zhu, “Airborne Measurements of Ethene from Industrial Sources Using Laser Photo-Acoustic Spectroscopy,” Environmental Science & Technology 43 (7), 2437–2442 (2009). Crossref

9. F. G. C. Bijnen, J. Reuss, and F. J. M. Harren, “Geometrical optimization of a longitudinal resonant photoacoustic cell for sensitive and fast trace gas detection,” Review of Scientific Instruments 67(8), 2914 (1996). Crossref

10. C.-M. Lee, K. V. Bychkov, V. A. Kapitanov, A. I. Karapuzikov, Y. N. Ponomarev, I. V. Sherstov, and V. A. Vasiliev, “High-sensitivity laser photoacoustic leak detector,” Optical Engineering 46(6), 064302 (2007). Crossref

11. A. Miklós, P. Hess, and Z. Bozóki, “Application of acoustic resonators in photoacoustic trace gas analysis,” Review of scientific instruments 72(4), 1937–1955 (2001). Crossref

12. V. Zéninari, R. Vallon, C. Risser, and B. Parvitte, “Photoacoustic detection of methane in large concentrations with a Helmholtz sensor: Simulation and experimentation”, International Journal of Thermophysics 37(1), 7 (2016). Crossref

13. Y. V. Kistenev, A. I. Karapuzikov, N. Y. Kostyukova, M. K. Starikova, A. A. Boyko, E. B. Bukreeva, A. A. Bulanova, D. B. Kolker, D. A. Kuzmin, K. G. Zenov, and A. A. Karapuzikov, “Screening of patients with bronchopulmonary diseases using methods of infrared laser photoacoustic spectroscopy and principal component analysis,” Journal of Biomedical Optics 20(6), 065001 (2015). Crossref

14. Yu V. Kistenev, A. V. Borisov, D. A. Kuzmin, O. V. Penkova, N. Y. Kostyukova, and A. A. Karapuzikov, “Exhaled air analysis using wideband wave number tuning range IR laser photoacoustic spectroscopy,” Journal of Biomedical Optics 22(1), 017002 (2017). Crossref

15. J. Li , W. Chen, and B. Yu, “Recent Progress on Infrared Photoacoustic Spectroscopy Techniques,” Applied Spectroscopy Reviews 46, 440–471 (2011). Crossref

16. D. D. Arslanov, M. P. P. Castro, N. A. Creemers, A. H. Neerincx, M. Spunei, J. Mandon, S. M. Cristescu, P. Merkus, and F. J. M. Harren, “Optical parametric oscillator-based photoacoustic detection of hydrogen cyanide for biomedical applications,” Journal of Biomedical Optics 18(10), 107002 (2013). Crossref

17. A. A. Karapuzikov, I. V. Sherstov, D. B. Kolker, A. I. Karapuzikov, Y. V. Kistenev, D. A. Kuzmin, M. Y. Shtyrov, N. Y. Dukhovnikova, K. G. Zenov, A. A. Boyko, M. K. Starikova, I. I. Tikhonyuk, I. B. Miroshnichenko, M. B. Miroshnichenko, Y. B. Myakishev, and V. N. Lokonov, “LaserBreeze gas analyzer for noninvasive diagnostics of air exhaled by patients,” Physics of Wave Phenomena 22(3), 189–196 (2014). Crossref

18. L. Pomerantsev, O. Y. Rodionova, “Concept and role of extreme objects in PCA/SIMCA,” Journal of Chemometrics 28(5), 429–438 (2014). Crossref

19. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” Journal of Machine Learning Research 5, 27–72 (2004).

20. A. V. Borisov, Y. V. Kistenev, D. A. Kuzmin, V. V. Nikolaev, A. V. Shapovalov, and D. A. Vrazhnov, “Development of classification rules for a screening diagnostics of lung cancer patients based on the spectral analysis of metabolic profiles in the exhaled air,” Proceedings of the Scientific-Practical Conference “Research and Development - 2016”, 573–580 (2016). Crossref

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