School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Department of Pulmonary and Critical Care Medicine, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China.
ACS Sens. 2024 Aug 23;9(8):4286-4294. doi: 10.1021/acssensors.4c01525. Epub 2024 Jul 30.
Ammonia (NH) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH with high accuracy and stability in the medical field.
呼气中的氨 (NH) 一直是肾功能的生物标志物，准确测量 NH 对于早期筛查肾脏疾病至关重要。在这项工作中，我们报告了一种光学传感器，该传感器结合了紫外差分光学吸收光谱 (UV-DOAS) 和光谱重建拟合神经网络 (SRFNN)，用于检测呼气中的 NH。UV-DOAS 用于消除呼气光谱中缓慢变化吸收的干扰，而光谱重建拟合则首次根据最小绝对偏差原理将原始光谱映射到正弦函数光谱上。然后，通过最小二乘法对正弦函数光谱进行拟合，以消除噪声信号和呼出一氧化氮的干扰。最后，建立神经网络以实现对 EB 中 NH 的纳克级 (ppb) 检测。实验室结果表明，检测范围为 9.50-12425.82 ppb，平均绝对百分比误差 (MAPE) 为 0.83%，检测准确率为 0.42%。实验结果证明，该传感器可以检测模拟患者和健康人的呼吸 NH 并识别 EB。我们的传感器将成为医疗领域中一种具有高精度和稳定性的新型有效呼吸 NH 检测系统。
