(335h) An “Artificial Nose” for the Non-Invasive Diagnosis of Anxiety in Alveolar Breath

Fitzgerald, J., Northeastern University
Fenniri, H., Northeastern University
An â??Artificial Noseâ? for the Non-invasive Diagnosis of Anxiety in Alveolar Breath

Jessica Fitzgerald and Hicham Fenniri

313 Snell Engineering Center, Northeastern University, 360 Huntington Avenue, Boston, MA 02115

In recent decades, artificial olfactory devices have been developed for disease diagnosis by evaluation of exhaled volatile organic compounds (VOCs) produced from multiple metabolic processes. These devices consist of a cross-reactive sensor array capable of interacting with multiple vapor analytes, a signal transduction mechanism, and response pattern recognition software. Our working hypothesis is that an increase in production of specific VOCs reflecting compromised metabolic processes found in patients suffering with a range of clinically significant anxiety symptoms could be detected with an e-nose. Such a fingerprint approach may also unveil possible biological pathways relevant to anxiety psychopathology.

This paper will present the design and fabrication of our barcoded resin-based (BCR) sensor array and its application for the detection and identification of VOCs. BCRs were prepared from a library of alkylated and fluorinated styrene monomers combined in a binary fashion. Upon interaction with an analyte, the vibrational signatures of the polymer array change, resulting in slight but detectable spectral variations for each BCR. The collective response (or analyte-specific patterns) will then be quantified using multivariate data analysis. This platform improves upon existing technologies as it dramatically increases sensitivity and information content using vibrational spectroscopy of a large library of sensory elements (encoded polymers)

Our current goal is to optimize a sensor array of BCRs for detecting clinically significant anxiety and stress VOCs with highest disease specificity in exhaled breath.