基于选择集成学习的表面增强拉曼光谱检波算法（英文）

Integrated peak detection algorithm for surface-enhanced Raman spectrum based on selective ensemble learning

文章提出了一种表面增强拉曼光谱的检波算法.该算法基于选择性学习和3种经典检波算法,构建了一个组合波峰检测器.结果表明,与传统算法相比,针对波峰提取,作者的算法有更好的正确率和检出率.证明作者的算法是一个可靠的模型,可以用于表面增强拉曼光谱和其他光谱分析的工程应用.

In this study, a peak detection algorithm was developed for a surface-enhanced Raman spectrum. The algorithm builds a matrix detector based on three traditional peak detection algorithms. An integrated peak detector is built after the threshold majorization based on a selective ensemble learning algorithm (SELA). The test results showed that the developed algorithm has lower false detection rates than two traditional algorithms. The proposed algorithm is thus a promising model for engineering applications of surface-enhanced Raman spectroscopy and other spectroscopic analyses.