Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array

Abstract

A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

Integration of omics technologies for the identification of predictive biomarkers in type 2 diabetes: a comprehensive analysis of recent literature

“…These sensors include1-butyl-3-methylimidazolium tetrafluoroborate [C4mim] [BF4]based flexible sensors that integrate with waste and human health management the e-skin & monitoring system; these have exhibited photosensitivity for sensitivity and selectivity of 9.58% and 11.35% towards acetone, one of several VOCs that could signal diabetes on breath (Tao et al, 2017). Quartz crystal microbalance sensors are being developed for stringently detecting acetone among ppb-level VOCs with 96% sensitivity and 99% specificity for diabetes (Rodríguez-Torres et al, 2023).…”