## Methods
### Data Analysis  
Analyses were divided into three age classes, namely, adults (>2 years), sub-adults (12-24 months), and juveniles (<12 months). Mean VOC values were obtained from measurements of two Tenax cartridges. If dissimilarities were noted in the VOC samples from the first two analyzed Tenax cartridges, a third VOC sample was analyzed and two similar cartridges of the three were then chosen for statistical analysis. If there were additional dissimilarities noted in the third sample, that animal was removed from the analysis. For each animal age and each kind of VOC sample analyzed in this study, statistical analysis was performed on compounds that were found in more than 80% of the animals. Tentative biomarkers were identified by applying a two-tailed statistical t-test for two samples assuming unequal variance (using QI Macros 2017 toolbox for Excel) with an \(\alpha\) value of 0.05 to determine statistically significant differences. A second statistical analysis based on animal sex and location, performed on the initial list of significant VOC, disregarded the compounds that could be affected by these factors. Discriminant function analysis was used to build classification models for MTBC prediction [26]. DFA is a linear supervised pattern-recognition algorithm that finds new orthogonal variables called canonical variables as a combination of the input variables (compound abundance in this study) to minimize the distance between samples from the same group and maximize the distance between samples from different groups (MTBC-positive and MTBC-negative animals in this study). The prediction parameters of the DFA models were estimated through leave-one-out cross-validation. The accuracy, sensitivity, and specificity of prediction were calculated in function of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, as follows: accuracy = (TP + TN)/(TP + TN + FP + FN); sensitivity = TP/(TP + FN); specificity = TN/(TN + FP). The prediction accuracy of the DFA classification models built was assessed by calculating the AUC of the ROC curve built with the first canonical variable, which is the most discriminative in the DFA models aiming to classify the two groups [27]. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-0817/9/5/346/s1](http://www.mdpi.com/2076-0817/9/5/346/s1)]([http://www.mdpi.com/2076-0817/9/5/346/s1](http://www.mdpi.com/2076-0817/9/5/346/s1)), Figure S1: Most abundant chromatographic peaks of breath volatile organic compound (VOC) samples collected from one animal under six conditions over three days. Figure S2: Most abundant chromatographic peaks of fecal volatile organic compound (VOC) samples collected from one animal under three conditions over three days. **Author Contributions:** Conceptualization, P.N., R.I., and J.R.; methodology, P.N., R.J., A.B., J.V., K.d.J.B.-S., I.B., and M.J.T. ; formal analysis, R.I., T.G.W., K.d.J.B.-S., F.I. ; investigation, P.N., R.I., T.G.W., J.A.B., J.V., K.d.J.B.-S., I.B., and M.J.T. ; resources, J.R., J.V., and R.I.; writing--original draft preparation, P.N., R.I., and T.G.W. ; writing--review and editing, P.N., R.I., T.G.W., J.A.B., and J.V. ; project administration, J.R.; funding acquisition, J.R., and J.V. All authors read and agreed to the published version of the manuscript. **Funding:** This research was funded by United States Department of Agriculture, Animal and Plant Health Services, Wildlife Services, National Feral Swine Program, the United States Department of Agriculture, Animal and Plant Health Services, Veterinary Services, and the SaBio Instituto de Investigacion en Recurso Cinegeticos (IREC) (MINECO; AEI/FEDER, UE; AGL2016-76358-R). **Acknowledgments:** We would like to acknowledge [PERSON] for assistance with VOC analysis and we express our appreciation to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their efforts in facilitating and conducting the field and animal work.