基于循环域光谱重建过的实时检测痕迹呼吸异烯,结合卷积神经网络的循环域光谱重建过
1Key Laboratory of Intelligent Control and Neural Information Processing, Ministry of Education, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Analytical chemistry
|July 1, 2025
概括
这项研究引入了一种新的光学传感器,用于实时检测呼吸中的微量异二烯,这是一种潜在的肺癌生物标志物. 传感器使用循环域重建过和卷积神经网络 (CNN) 进行准确的分析.
科学领域:
- 分析化学 分析化学
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
背景情况:
- 呼吸中的微量异烯是肺癌诊断的潜在的非侵入性生物标志物.
- 检测挑战包括低度 (百亿分之一) 和呼吸中的干扰化合物.
- 现有的方法需要复杂的样本处理,缺乏实时功能.
研究的目的:
- 开发和验证一种新的光学传感器,用于实时,非侵入式检测呼吸中的微量异烯.
- 为了应对干扰成分和低分析剂度所带来的挑战.
- 使用紫外线差异光学吸收光谱学 (UV-DOAS) 进行呼吸分析.
主要方法:
- 使用紫外差异光学吸收光谱学 (UV-DOAS) 来获得异二烯差异吸收光谱.
- 开发了一种循环域重建过方法,以减轻噪音并消除水蒸气,氨和氧化的干扰.
- 构建了一个卷积神经网络 (CNN) 模型,用于从过的光谱中准确的异烯度反转.
主要成果:
- 实现了光学传感器的3.98ppb·m的检测极限.
- 在21.32至1254.20ppb的度范围内,已经证明了精确和实时的呼吸异二烯传感.
- 使用人类呼吸样本验证传感器的有效性.
结论:
- 拟议的光学传感器可以使用UV-DOAS首次实时检测呼吸异烯.
- 循环域重建过和CNN有效地克服了复杂的呼吸矩阵中的检测挑战.
- 该传感器显示出非侵入性肺癌诊断的巨大潜力,并扩大了宽带光谱在呼吸分析中的应用.
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