Abstract：
Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 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 NH3 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 NH3 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 NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.
摘要：
呼出气（EB）中的氨（NH3）一直是肾功能的生物标志物，准确测量NH3对肾脏疾病的早期筛查至关重要。在这项工作中,我们报告了一种结合紫外差分吸收光谱（UV-DOAS）和光谱重建拟合神经网络（SRFNN）的光学传感器，用于检测EB中的NH3。引入UV-DOAS来消除EB光谱中缓慢变化吸收的干扰，同时首次提出光谱重建拟合，通过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谱进行拟合，以消除噪声信号和呼出的一氧化氮的干扰。最后,神经网络的建立是为了在十亿分之一（ppb）的水平上检测EB中的NH3。实验室结果表明，检测范围为9.50-12425.82ppb，平均绝对百分比误差(MAPE)为0.83%，检测精度为0.42%。实验结果证明，该传感器可以检测呼吸NH3并识别模拟患者和健康人的EB。我们的传感器将作为一种新的有效系统，用于在医疗领域中高精度和稳定性地检测呼吸NH3。
