Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject’s breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.

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Breath ammonia is an important biomarker linked to liver malfunction. Acetone is the most abundant compound in the breath, acts as major interference for selective detection of ammonia gas. Here, a novel method based on viscosity modulation of the silicone oil absorbent is reported for selectivity improvement of ammonia over acetone gas. ATD-GC-MS and T201 ammonia analyzer are used to measure the absorption of acetone and ammonia respectively into the silicone oil. The absorption of ammonia and acetone gas is measured in different absorbent viscosities at a constant flow rate (50 cc min−1). Absorption results of ammonia are 7.37%, 16.3%, and 17.1% and acetone absorption results are 35%, 68%, and 78% respectively into 500 cSt, 100 cSt, and 20 cSt viscous silicone oil at room temperature. More bubbles of smaller diameter are formed at a lower viscosity, increases the contact time of the gas with absorbent. Consequently, the absorption of acetone into silicone oil at lower viscosity increases as compared to ammonia. The absorption of acetone is about 4.6-fold higher than the ammonia. Hence, it proves to be an effective technique for enhancing selectivity. This novel concept can be incorporated with any sensor for portable breath ammonia sensing in the detection of liver dysfunction.