Being a confirmed TB patient was directly proportional to e-nose 10-sensor responses. Principal component analysis clusters showed a clear distinction between TB and HC groups, with variances of 93%, 85%, 75% and 95% for blood, breath, sputum and urine samples, respectively. Overall accuracy, sensitivity and specificity of the artificial neural network (ANN) analysis for classifying samples were >99%. The e-nose successfully distinguished TB patients from HC participants for all measured biological samples with great precision. With urine samples gaining broader acceptance for clinical diagnosis, an e-nose-based ANN can be a very useful tool for low-cost mass screening and early detection of TB patients in developing countries.
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Mohamed et al. (2017) studied this question.