Paleczek Anna, Grochala Justyna, Grochala Dominik, Słowik Jakub, Pihut Małgorzata, Loster Jolanta E, Rydosz Artur
AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland.
Department of Prosthodontics and Orthodontics, Dental Institute, Faculty of Medicine, Jagiellonian University Medical College, ul. św. Anny 12, Kraków 31-008, Poland.
ACS Sens. 2024 Dec 27;9(12):6630-6637. doi: 10.1021/acssensors.4c02198. Epub 2024 Nov 22.
In this paper, the first e-nose system coupled with machine learning algorithm for noninvasive measurement of total cholesterol level based on exhaled air sample was proposed. The study was conducted with the participation of 151 people, from whom a breath sample was collected, and the level of total cholesterol was measured. The breath sample was examined using e-nose and gas sensors, such as TGS1820, TGS2620, TGS2600, MQ3, Semeatech 7e4 NO2 and 7e4 H2S, SGX_NO2, SGX_H2S, K33, AL-03P, and AL-03S. The LGBMRegressor algorithm was used to predict cholesterol level based on the breath sample. Machine learning algorithms were developed for the entire measurement range and for the norm range ≤200 mg/dL achieving MAPE 13.7% and 8%, respectively. The results show that it is possible to develop a noninvasive device to measure total cholesterol level from breath.
本文提出了首个基于呼出气体样本、结合机器学习算法用于无创测量总胆固醇水平的电子鼻系统。该研究有151人参与，收集了他们的呼气样本并测量了总胆固醇水平。呼气样本使用电子鼻和气体传感器进行检测，这些传感器包括TGS1820、TGS2620、TGS2600、MQ3、Semeatech 7e4 NO2和7e4 H2S、SGX_NO2、SGX_H2S、K33、AL - 03P和AL - 03S。LGBMRegressor算法用于基于呼气样本预测胆固醇水平。针对整个测量范围以及正常范围≤200 mg/dL开发了机器学习算法，分别实现了13.7%和8%的平均绝对百分比误差（MAPE）。结果表明，开发一种从呼出气体中测量总胆固醇水平的无创设备是可行的。
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