Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System

Abstract

Blood glucose level is an important health indicator. Non-invasive, easy-to-use glucose detection and monitoring methods and tools are desperately needed, especially for patients with diabetes. In this work, we developed a new method to quantitively identify and analyze the blood glucose level by measuring the biomarkers in breath with an electronic nose (E-Nose) system based on a metal oxide (MOX) gas sensor array. Advanced machine-learning models have been studied and developed to precisely predict the blood glucose level based on the measurement of 41 participants for 10 days. The testing result shows that the E-Nose system and proposed analysis models identify blood glucose levels at an accuracy of 90.4% and a small average error of 0.69 mmol/L in blood glucose concentration. This study indicates that the E-Nose system enabled with machine learning is an efficient and precise method to achieve low-cost and non-invasive disease diagnosis.