Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

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Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile and perfume industry as well as for air pollution control. The aim of this study was to test whether exhaled breath analysed by an artificial nose could identify and discriminate between different lung diseases. A total of 76 individuals were tested: 25 bronchial asthma, 19 lung cancer, 10 pneumonia, 6 chronic obstructive pulmonary disease (COPD) patients and 16 healthy volunteers. Exhaled air was collected in plastic bags and immediately analysed using an electronic nose instrument (9185, Nordic Sensors AB) containing 14 different odour sensors. Multifactor logistic regression analysis was used to determine correlation between the amplitudes of sensor responses and the clinical diagnoses of patients and to calculate sensitivity and specificity of the method for each diagnosis. For diagnostics of asthma the sensitivity was found to be 84% and specificity — 86%. For lung cancer, the sensitivity was 74% and specificity, 95%; for pneumonia 90% and 98%, but for COPD, 33% and 97%, respectively. We conclude that an artificial nose is able to discriminate among different lung diseases with sufficiently good accuracy. This method may be further developed to implement it in clinical medicine for express diagnostics of acute and chronic lung diseases.

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  • Frishman G. Tzanani N. Amirav A. (2001). Electrolyzer operated gas cylinder free GC-FID. Field Anal. Chem. Technol.5 107-115.

  • Furey T.S. Cristianini N. Bednarski D.W. Schummer M. Haussler D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformation16 906-914.

  • Haraszthy V.I. Gerber D. Clark B. Moses P. Parker C. Sreenivasan P.K. Zambon J.J. (2008). Characterization and prevalence of Solo-bacterium moorei associated with oral halitosis. J. Breath Res.2 DOI: 10.1088/1752-7155/2/1/017002.

  • Kermany B.G. Schiffman S.S. Nagle H.T. (1999). Using neural networks and genetic algorithms to enhance performance in an electronic nose. IEEE Trans. Biomed. Eng.46 429-439.

  • Kurata H. Awano S. Yoshida A. Ansai T. Takehara T. (2008). Volatile sulphide compound levels in mouth air with plaque-related periodontal disease are associated with a change in the proportion of periodontopathic bacteria in saliva. J. Breath Res.2 DOI: 10.1088/1752-7155/2/1/017006.

  • Machado R. Laskowski D. Deffenderfer O. Burch T. Zheng S. Mazzone P.J. Mekhail T. Jennings C. Stoller J.K. Pyle J. Duncan J. Dwelk R.A. (2005). Detection of lung cancer by sensor array analyses of exhaled breath. Amer. J. Respir. Crit. Care Med.171 1286-1291.

  • Phillips M. Cataneo R.N. Cummin A.R. Gagliardi A.J. Gleeson K. Greenberg J. Maxfield R.A. Rom W.N. (2003). Detection of lung cancer with volatile markers in the breath. Chest123 2115-2123.

  • Risby T.H. Sehnert S.S. (1999). Clinical application of breath biomarkers of oxidative stress status. Free Radic. Biol. Med.27 1182-1192.

  • Saad S. Greenman J. (2008). Tongue biofilm areal density and tongue coating index J. Breath Res.2 DOI: 10.1088/1752-7155/2/1/017008.

  • Skeldon K.D. McMillan L.C. Wyse C.A. Monk S.D. (2006). Application of laser spectroscopy for measurement of ethane in patients with lung cancer. Respir. Med.100 2 300-306.

  • Smith D. Turner C. Španel P. (2007). Volatile metabolites in the exhaled breath of healthy volunteers: Their levels and distributions. J. Breath Res.1 DOI: 10.1088/1752-7155/1/1/014004.

  • Watanabe N. (1998). Polymorphic CYP genes and disease predisposition-what have the studies shown so far? Toxicol. Lett.102-103 167-171.

  • Wold S. Ebensen K. Geladi P. (1987). Principal component analysis. Chemom. Intell. Lab. Syst.2 37-52.

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