Prospects for Clinical Application of Electronic-Nose Technology to Early Detection of Mycobacterium tuberculosis in Culture and Sputum

• Published in: Journal of Clinical Microbiology Vol. 44; no. 6; pp. 2039 - 2045
• Main Authors: Fend, Reinhard, Kolk, Arend H.J, Bessant, Conrad, Buijtels, Patricia, Klatser, Paul R, Woodman, Anthony C
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 01.06.2006
• Subjects: Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Culture Media; Discriminant Analysis; Electronics; Humans; Mycobacteriology and Aerobic Actinomycetes; Mycobacterium tuberculosis; Mycobacterium tuberculosis - growth & development; Mycobacterium tuberculosis - isolation & purification; Neural Networks, Computer; Odorants - analysis; Principal Component Analysis; Pseudomonas aeruginosa; Sensitivity and Specificity; Sputum - microbiology; Tuberculosis, Pulmonary - diagnosis; Tuberculosis, Pulmonary - microbiology
• ISSN: 0095-1137; 1098-660X; 1098-5530
• DOI: 10.1128/JCM.01591-05

Ziehl-Neelsen (ZN) staining for the diagnosis of tuberculosis (TB) is time-consuming and operator dependent and lacks sensitivity. A new method is urgently needed. We investigated the potential of an electronic nose (EN) (gas sensor array) comprising 14 conducting polymers to detect different Mycobacterium spp. and Pseudomonas aeruginosa in the headspaces of cultures, spiked sputa, and sputum samples from 330 culture-proven and human immunodeficiency virus-tested TB and non-TB patients. The data were analyzed using principal-component analysis, discriminant function analysis, and artificial neural networks. The EN differentiated between different Mycobacterium spp. and between mycobacteria and other lung pathogens both in culture and in spiked sputum samples. The detection limit in culture and spiked sputa was found to be 1 x 10⁴ mycobacteria ml⁻¹. After training of the neural network with 196 sputum samples, 134 samples (55 M. tuberculosis culture-positive samples and 79 culture-negative samples) were used to challenge the model. The EN correctly predicted 89% of culture-positive patients; the six false negatives were the four ZN-negative and two ZN-positive patients. The specificity and sensitivity of the described method were 91% and 89%, respectively, compared to culture. At present, the reasons for the false negatives and false positives are unknown, but they could well be due to the nonoptimized system used here. This study has shown the ability of an electronic nose to detect M. tuberculosis in clinical specimens and opens the way to making this method a rapid and automated system for the early diagnosis of respiratory infections.