Machine-Learning-Driven Resistive Sensing of Artificial Breath Biomarkers From Lipid Metabolism: A Step Toward Noninvasive Healthcare

Exhaled breath contains hundreds of volatile organic compounds (VOCs) linked to various biochemical pathways, hence tracking their concentration levels useful for the early detection, substaging, and prognosis of health conditions. Elevated levels of acetone and isoprene, which are by-products from metabolic pathways of lipids, are correlated with specific medical disorders. This study demonstrates the potential of an indium-oxide-based sensor to detect trace amounts of these VOCs in human breath at parts-per-billion (ppb) levels in an artificial breath-like environment. A selectivity analysis was conducted to differentiate the sensor's response to the targeted VOCs from other gases typically present in breath, mitigating the risk of false positives. The sensor demonstrated stable and reproducible performance over ten weeks, validating its practicality for real-world use. In addition, this article explores various machine learning (ML) techniques to enhance VOC analysis and proposes an end-to-end framework for the automatic classification and prediction of VOC concentrations. Peak-based feature extraction is used to effectively profile VOCs, and Gaussian-noise-based data augmentation is used to improve the accuracy of classification and regression models. This comprehensive approach integrates advanced sensor technology with ML methods to offer a robust solution using noninvasive breath analysis for early detection and follow-up of diseases related to raised lipid metabolism rate.