一种有效的无标记时间分辨光谱分析物检测算法
An efficient label-free analyte detection algorithm for time-resolved spectroscopy
摘要：
时间分辨光谱技术在从物理化学到生物医学的许多领域中发挥着重要的分析工具。通常，分析物的无标记检测由专家通过经典的降维方法（例如主成分分析（PCA）和非负矩阵因子分解（NMF））的辅助手动进行。这种对未知分析物检测的专家分析的基本依赖严重阻碍了这些技术的适用性和通量。为此，在本文中，我们制定了这个检测问题作为一个无监督学习问题，并提出了一种新的机器学习算法的无标记分析物检测。为了证明所提出的解决方案的有效性，我们考虑的问题，检测的氨基酸在液相色谱耦合拉曼光谱（LC-Raman）。
英文摘要：
Time-resolved spectral techniques play an important analysis tool in many contexts, from physical chemistry to biomedicine. Customarily, the label-free detection of analytes is manually performed by experts through the aid of classic dimensionality-reduction methods, such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). This fundamental reliance on expert analysis for unknown analyte detection severely hinders the applicability and the throughput of these such techniques. For this reason, in this paper, we formulate this detection problem as an unsupervised learning problem and propose a novel machine learning algorithm for label-free analyte detection. To show the effectiveness of the proposed solution, we consider the problem of detecting the amino-acids in Liquid Chromatography coupled with Raman spectroscopy (LC-Raman).
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