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Determination of diabetic blood glucose value from breath odor using QCM sensor based Electronic Nose

机译:使用基于QCM传感器的电子鼻根据呼吸异味确定糖尿病血糖值

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In this study, it is aimed to determine the glycemia (blood sugar) level of the diabetics by analyzing the acetone odor participated in their breath (exhalation) with the help of QCM (Quartz Crystal Microbalnce) sensor based Electronic Nose. The level of acetone concentration in humans' breath is as low as 0, 1–10 ppm. In order low level acetone concentration to be sensed by the sensors, it is needed to concentrate the volatile organic compounds. Thus, a condenser containing chemical absorbent ingredients is used in the experiment mechanism. Thanks to this, high concentration is gained by detaining the breath sample that is carrying low acetone concentration within the condenser. The QCM sensor data is compared against glycemia (blood sugar) data in the study. The minimum error rate is 23,76% when the glycemia value is applied to Artificial Neural Network.
机译:在这项研究中,其目的是借助基于QCM(石英晶体微平衡)传感器的电子鼻,通过分析参与其呼吸(呼气)的丙酮气味来确定糖尿病患者的血糖(血糖)水平。人的呼吸中丙酮的浓度低至0、1-10 ppm。为了通过传感器感应到较低的丙酮浓度,需要浓缩挥发性有机化合物。因此,在实验机构中使用了含有化学吸收剂成分的冷凝器。因此,通过将冷凝器中携带低丙酮浓度的呼气样品滞留在呼吸器中,可以获得高浓度。在研究中将QCM传感器数据与血糖(血糖)数据进行了比较。当将血糖值应用于人工神经网络时,最小错误率为23.76%。

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