RESUMO
Currently, urine samples for bacterial or fungal infections require a long diagnostic period (48 h). In the present work, a point-of-care device known as an electronic nose (eNose) has been designed based on the "smell print" of infections, since each one emits various volatile organic compounds (VOC) that can be registered by the electronic systems of the device and recognized in a very short time. Urine samples were analyzed in parallel using urine culture and eNose technology. A total of 203 urine samples were analyzed, of which 106 were infected and 97 were not infected. A principal component analysis (PCA) was performed using these data. The algorithm was initially capable of correctly classifying 49% of the total samples. By using SVM-based models, it is possible to improve the accuracy of the classification up to 74% when randomly using 85% of the data for training and 15% for validation. The model is evaluated as having a correct classification rate of 74%. In conclusion, the diagnostic accuracy of the eNose in urine samples is high, promising and amenable for further improvement, and the eNose has the potential to become a feasible, reproducible, low-cost and high-precision device to be applied in clinical practice for the diagnosis of urinary tract infections.
