Xie Fei, Li Mu, Gao Jie, Liu Feifei, Zhu Rui, Xu Shufeng, Zhang Yungang
Key Laboratory of Intelligent Control and Neural Information Processing, Ministry of Education, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Hebei Provincial Key Laboratory for Research on Pathogenesis and Long-Term Management of Lung Cancer and Chronic Airway Diseases, Qinhuangdao 066004, China.
Anal Chem. 2025 Jul 15;97(27):14649-14657. doi: 10.1021/acs.analchem.5c02181. Epub 2025 Jul 1.
The detection of trace isoprene in breath provides a noninvasive method for lung cancer diagnosis. However, the presence of interfering components and the parts per billion (ppb) concentration levels of isoprene in breath complicate detection. In this study, we propose an optical sensor based on circular domain reconstruction filtering and convolutional neural network (CNN), enabling the real-time detection of breath isoprene using ultraviolet differential optical absorption spectroscopy (UV-DOAS) for the first time. First, we obtained the differential absorption spectra of isoprene using UV-DOAS and analyzed the impact of interfering components including water vapor (HO) on the spectral characteristics. Second, we proposed a novel circular domain reconstruction filtering method that effectively mitigates noise and removes interference from components including ammonia (NH) and nitric oxide (NO) by discretizing disturbance absorption features. By mapping the absorption features to the circular domain, the proposed filtering method eliminates discrete noise and interference, providing a novel perspective on trace gas detection and spectral analysis. Based on the filtered spectra, a CNN model was constructed to invert isoprene concentration. Laboratory results show that the sensor has a detection limit of 3.98 ppb·m and provides accurate and real-time breath isoprene sensing ranging from 21.32 to 1254.20 ppb. Test results from human samples further demonstrate the effectiveness of the sensor in detecting trace isoprene in breath. Our sensor not only shows potential for application in isoprene detection but also advances the use of broadband spectroscopy in breath analysis.
检测呼出气体中的痕量异戊二烯为肺癌诊断提供了一种非侵入性方法。然而，呼出气体中干扰成分的存在以及异戊二烯十亿分之一（ppb）级别的浓度水平使检测变得复杂。在本研究中，我们提出了一种基于圆形域重建滤波和卷积神经网络（CNN）的光学传感器，首次实现了利用紫外差分光学吸收光谱法（UV-DOAS）实时检测呼出气体中的异戊二烯。首先，我们利用UV-DOAS获得了异戊二烯的差分吸收光谱，并分析了包括水蒸气（HO）在内的干扰成分对光谱特征的影响。其次，我们提出了一种新颖的圆形域重建滤波方法，通过离散化干扰吸收特征，有效减轻噪声并消除包括氨（NH）和一氧化氮（NO）在内的成分的干扰。通过将吸收特征映射到圆形域，所提出的滤波方法消除了离散噪声和干扰，为痕量气体检测和光谱分析提供了新的视角。基于滤波后的光谱，构建了一个CNN模型来反演异戊二烯浓度。实验室结果表明，该传感器的检测限为3.98 ppb·m，能够对21.32至1254.20 ppb范围内的呼出气体异戊二烯进行准确实时检测。人体样本的测试结果进一步证明了该传感器在检测呼出气体中痕量异戊二烯方面的有效性。我们的传感器不仅在异戊二烯检测方面显示出应用潜力，还推动了宽带光谱在呼吸分析中的应用。
