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.

中文翻译：

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使用 E-Nose 系统和呼出气样本的机器学习进行无创总胆固醇水平测量


本文提出了第一个电子鼻系统结合机器学习算法，用于基于呼出空气样本的无创总胆固醇水平测量。该研究是在 151 人参与的情况下进行的，从他们身上收集了呼吸样本，并测量了总胆固醇的水平。使用电子鼻和气体传感器检查呼吸样本，例如 TGS1820、TGS2620、TGS2600、MQ3、Semeatech 7e4 NO2 和 7e4 H2S、SGX_NO2、SGX_H2S、K33、AL-03P 和 AL-03S。LGBMRegressor 算法用于根据呼吸样本预测胆固醇水平。为整个测量范围和标准范围 ≤200 mg/dL 开发了机器学习算法，分别实现了 MAPE 13.7% 和 8%。结果表明，可以开发一种无创设备来测量呼吸中的总胆固醇水平。