Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System

Abstract

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12-24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

Keywords: Mycobacterium bovis; Sus scrofa; bovine tuberculosis; chemical gas sensors; diagnosis; feces; gold nanoparticles; organic ligands; volatile organic compounds.

Figure 1

Surface Electron Microscopy (SEM) images of gold nanoparticles functionalized with: (a) 2-Mercaptobenzoxazole and (b) 4-Methoxy-α-toluenethiol.

Figure 2

(a) Measurement setup: A—computer, B—data acquisition system, C—power source, D—thermal desorption unit, E—sensor test chamber, F—N₂ gas bottle, and G—Tenax TA storage tube. (b) Inner view of the sensor test chamber with the chemical gas sensors. (c) Schematic representation of the thermal desorption unit, illustrating released fecal VOCs transported by the N₂ gas flow.

Figure 3

Sensor response (current) as a function of sampling time showing all measurement cycles of the sensors that responded to the fecal VOC samples, when the electronic nose was exposed to one of the samples from wild boar #2 (see Table 1): (a) Sensor S5, (b) Sensor S6, (c) Sensor S8, and (d) Sensor S9. Sensor details are found in Table 3.

Figure 4

(a) Exposure of sensor S9 to the fecal VOC samples of a bTB negative wild boar (blue curve—boar #2 in Table 1) and a bTB positive wild boar (red curve—boar #12 in Table 1). (b) Parameters used to calculate sensor response features, indicated on the filtered signal of sensor S9 to the fecal VOC sample of wild boar #12. Sensor details are found in Table 3.

Figure 5

Classification models built for: (a) adult animals, (b) sub-adult animals, and (c) juvenile animals. Projection of the test samples on the classification models for: (d) adult animals, (e) sub-adult animals, and (f) juvenile animals. CV1 and CV2: canonical variables of the DFA model, calculated as a linear combination of sensor features, as described in Ionescu et al. [26]. Legend: control—bTB negative animals, target—bTB positive animals, filled symbols—samples used in the training phase, and empty symbols—samples used in the testing phase.