Volume 112, Issue 6 pp. 975-979
Article

Identification of Upper Respiratory Bacterial Pathogens With the Electronic Nose

Stephen Y. Lai MD, PhD

Stephen Y. Lai MD, PhD

Department of Otorhinolaryngology–Head and Neck Surgery, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, Pasadena, California, U.S.A.

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Olivia F. Deffenderfer BS

Olivia F. Deffenderfer BS

Cyrano Sciences, Inc., Pasadena, California, U.S.A.

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William Hanson MD

William Hanson MD

Department of Anesthesia, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, U.S.A.

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Marguerite P. Phillips RN, BSN

Marguerite P. Phillips RN, BSN

Department of Anesthesia, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, U.S.A.

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Erica R. Thaler MD

Corresponding Author

Erica R. Thaler MD

Department of Otorhinolaryngology–Head and Neck Surgery, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, Pasadena, California, U.S.A.

Erica R. Thaler, MD, Assistant Professor, Department of Otorhinolaryngology–Head and Neck Surgery, University of Pennsylvania Health System, 5 Silverstein/Ravdin, 3400 Spruce Street, Philadelphia, PA 19104, U.S.A.Search for more papers by this author
First published: 02 January 2009
Citations: 71

Presented at the Eastern Section Meeting of the Triological Society, Philadelphia, PA, January 26, 2002.

Abstract

Objective To use an electronic nose to identify common upper respiratory bacterial pathogens.

Study Design Controlled in vitro analysis.

Methods Swabs of bacteria were obtained from in vitro samples. The specimens were vaporized and analyzed over the organic semiconductor-based electronic nose (Cyranose 320). Data from the 32-element sensor array were subjected to principal component analysis for depiction in two-dimensional space and differences in odorant patterns were assessed by calculating Mahalanobis distances.

Results The electronic nose was able to distinguish between control swabs and bacterial samples. Furthermore, calculation of the Mahalanobis distances among the various bacteria demonstrated distinct odorant classes (Mahalanobis distance ⩾3). This demonstrates that the electronic nose could differentiate among various common bacterial pathogens of the upper respiratory tract, including Staphylococcus aureus, Streptococcus pneumoniae, Haemophilus influenza, and Pseudomonas aeruginosa.

Conclusions The electronic nose represents a novel method to identify potential upper respiratory infections and to discriminate among common upper respiratory bacterial pathogens. This technology could provide a rapid means to identify organisms causing upper respiratory infections.

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