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.

Ammonia is an essential biomarker for noninvasive diagnosis of liver malfunction. Therefore, selective detection of ammonia is essential for medical application. Here, we demonstrate a portable device to selectively detect sub-ppm ammonia gas. The presented gas sensor is composed of a Pt coating on top of an ultrathin Indium nitrite (InN) epilayer with a lower detection limit of 0.2 ppm, at operating temperature of 200 °C, and detection time of 1 min. The sensor connected with the external filter of nonpolar 500 CS silicone oil to diagnose liver malfunction. The absorption of 0.7 ppm acetone and 0.4 ppm ammonia gas in 10 cc silicone oil is 80% (0.56 ppm) and 21.11% (0.084 ppm), respectively, with a flow rate of 10 cc/min at 25 °C. The absorption of acetone gas is 6.66-fold higher as compared to ammonia gas. The percentage variation in response for 0.7 ppm ammonia and 0.7 ppm acetone with and without silicone oil on InN sensor is 17.5% and 4%, and 22.5%, and 14% respectively. Furthermore, the percentage variation in response for 0.7 ppm ammonia gas with silicone oil on InN sensor is 4.3-fold higher than that of 0.7 ppm acetone. The results show that the InN sensor is suitable for diagnosis of liver malfunction.

A platinum coating on an ultrathin InN resistive gas sensor was fabricated for the selective sensing of ammonia and acetone gas for liver malfunction and diabetes applications by using a temperature modulation technique. We use a cyclic temperature profile in the InN gas sensor heater, where the temperature varies from 200 • C to 0 • C and from 0 • C to 200 • C in increments of 50 • C. Therefore, a different current variation response is measured on an InN epilayer for 5 ppm ammonia and 5 ppm acetone gas, respectively, because ammonia and acetone gas behave differently in the cyclic temperature profile. When ammonia and acetone gas are exposed in background air, the variation in the response of the current in different temperature regions is given as 2.5% for acetone gas and 7.85% for ammonia gas between 100 • C to 150 • C, and 12% for acetone gas and 8.85% for ammonia gas between 150 • C to 200 • C. Therefore, the temperature region 100 • C to 150 • C is suitable for selectively sensing ammonia gas for determining liver malfunction. The temperature range between 150 • C to 200 • C is suitable for selectively sensing acetone gas for the diabetes monitoring applications.

An ultrathin InN resistive gas sensor is used for selective sensing of ammonia gas by using (∼5nm) pentacene film coating on the InN epilayer for liver malfunction applications. In order to distinguish between ammonia and acetone gases in the exhaled breath, we used two physically different characteristics of InN epilayer such as bare InN epilayer and thin pentacene film atop InN epilayer. On the bare InN epilayer, current variation responses for 8ppm ammonia and 8ppm acetone were 0.6% and 0.57% respectively. On bare InN epilayer, the current variation response of ammonia gas is only 1.05 times the current variation response of acetone gas. On pentacene coated InN epilayer current variation responses for 8ppm ammonia and 8ppm acetone gases are 4.7% and 1.9% respectively. The current variation response for ammonia is 2.47 folds higher than acetone on the pentacene coated InN epilayer. A thin film pentacene atop InN epilayer is very effective to filter out the acetone gas. Therefore, pentacene coated atop ultrathin InN epilayer is useful for the selective sensing of the ammonia gas in the exhaled breath for liver malfunction applications.

The traces of sub-ppm ammonia in the human breath is useful for the detection of the liver condition in humans. In this work, we have fabricated two InN based gas sensors of different thickness of the sensing film T 1 (40 nm) and T 2 (60 nm) respectively by RF-MOMBE. The current variation response of 2 ppm NH 3 gas on T 1 is approximately 10 fold higher than T 2 . The current variation response for 0.5 ppm on T 1 is 0.04%, while T 2 showed a negligible response. The change at the surface of InN epilayer will be more significant on the total conductivity of the thin layer as compared to the thick layer. Hence, the lower thickness of the InN epilayer has a higher response as compared to higher thickness. Therefore, lower thickness (40 nm) is suitable for the diagnosis of liver malfunction.