The aim of this study was to assess the classification accuracy of an e-Nose in detecting acute liver failure (ALF) in rats. Exhaled breath from 14 rats was repeatedly sampled by e-Nose (8 sensors) and an additional external CO2 sensor at three stages: healthy period; portacaval shunt; and during the development of ALF due to surgically induced complete liver ischemia. We performed principal component analysis (PCA) on the (grouped) sensor data in each stage and the classification accuracy of the first two principal components was assessed by the leave-one-out approach. In addition we performed gas chromatography-mass spectrometry (GC-MS) analysis of the exhaled breath from three rats. The first and second principal components from the PCA analysis of e-Nose data accounted for more than 95% variance in the data. Measurements in the ALF stage were contrasted with the measurements in the control stage. Leave-one-out validation showed classification accuracy of 96%. This accuracy was reached after 3h of ALF development, and was reached already after 2h when data of an external CO2 sensor were also included. GC-MS identified 2-butanol, 2-butanone, 2-pentanone and 1-propanol to be possibly elevated in the ALF stage. This is the first study to demonstrate that ALF in rats can be detected by e-Nose data analysis of the exhaled breath. Confirmation of these results in humans will be an important step forward in the non-invasive diagnosis of ALF.
Keywords: Breath print; GC–MS; Leave-one-out validation; Principal components analysis; Volatile organic compounds.
© 2013 Published by Elsevier B.V.