论文标题
时间分辨光谱法的有效无标签分析物检测算法
An efficient label-free analyte detection algorithm for time-resolved spectroscopy
论文作者
论文摘要
从物理化学到生物医学，时间分辨光谱技术在许多情况下都具有重要的分析工具。通常，专家通过经典减少维度降低方法（例如主成分分析（PCA）和非负矩阵分解（NMF））手动执行分析物的无标签检测。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吞吐量。因此，在本文中，我们将该检测问题作为一个无监督的学习问题，并提出了一种新型的机器学习算法，用于无标签的分析物检测。为了显示提出溶液的有效性，我们考虑了检测液相色谱中氨基酸的问题，并结合拉曼光谱法（LC-Raman）。
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).
