Detection of volatile organic compounds in Brucella abortus-seropositive bison.

Brucellosis is of great public health and economic importance worldwide. Detection of brucellosis currently relies on serologic testing of an antibody response to Brucella infection, which suffers from cross-sensitivities to other antibody responses. Here we present a new method for identifying Brucella exposure that is based on profiling volatile organic compounds (VOCs) in exhaled breath. Breath samples from Brucella-seropositive bison and controls were chemically analyzed and demonstrated statistically significant differences in the concentration profiles of five VOCs. A point-of-care device incorporating an array of nanomaterial-based sensors could identify VOC patterns indicative of Brucella exposure with excellent discriminative power, using a statistical algorithm. We show that the patterns were not affected by the animals' environment and that the discriminative power of the approach was stable over time. The Brucella-indicative VOCs and collective patterns that were identified in this pilot study could lead to the development of a novel diagnostic screening test for quickly detecting infected animals chute-side, pen-side, or even remotely in populations of free-ranging ungulates. The promising preliminary results presented encourage subsequent larger scale trials in order to further evaluate the proposed method.

Field effect transistors based on polycyclic aromatic hydrocarbons for the detection and classification of volatile organic compounds.

We show that polycyclic aromatic hydrocarbon (PAH) based field effect transistor (FET) arrays can serve as excellent chemical sensors for the detection of volatile organic compounds (VOCs) under confounding humidity conditions. Using these sensors, w/o complementary pattern recognition methods, we study the ability of PAH-FET(s) to: (i) discriminate between aromatic and non-aromatic VOCs; (ii) distinguish polar and non-polar non-aromatic compounds; and to (iii) identify specific VOCs within the subgroups (i.e., aromatic compounds, polar non-aromatic compounds, non-polar non-aromatic compounds). We further study the effect of water vapor on the sensor array's discriminative ability and derive patterns that are stable when exposed to different constant values of background humidity. Patterns based on different independent electronic features from an array of PAH-FETs may bring us one step closer to creating a unique fingerprint for individual VOCs in real-world applications in atmospheres with varying levels of humidity.