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Single‐cell Raman spectrum extraction from clinic biosamples
Journal of Raman Spectroscopy ( IF 2.5 ) Pub Date : 2020-09-06 , DOI: 10.1002/jrs.5984
Xinxin Han 1, 2 , Yihui Wu 2, 3 , Mingbo Chi 2 , Sujun Gao 4 , Quan Wang 5
Affiliation  

Raman spectra of clinical samples are often affected by both the substrate and background fluorescence. The overlay of the substrate spectrum, biological Raman spectrum, and the fluorescence spectrum significantly affects the identification of biometrics. In this paper, we propose a specific‐scale analysis algorithm and a zero‐order Savitzky–Golay filtering algorithm combining local minima to separate the substrate and background fluorescent, respectively. The specific‐scale analysis algorithm based on wavelet transform can realize the linear separation of the substrate spectrum through multiresolution analysis. The zero‐order Savitzky–Golay filter algorithm combining local minima is an empirical algorithm. It estimates the background fluorescence by building a smooth curve that passes through local minima. We tested our algorithms with simulated spectra and with the Raman spectra of clinical biosamples recorded on glass. The analysis results of three gastric cancer cell lines indicated that the classification error of the processed spectrum decreased significantly.

中文翻译:

从临床生物样品中提取单细胞拉曼光谱

临床样品的拉曼光谱通常受底物和背景荧光的影响。底物光谱,生物拉曼光谱和荧光光谱的重叠显着影响生物特征的鉴定。在本文中,我们提出了一种特定尺度的分析算法和一个零阶Savitzky-Golay滤波算法,结合局部极小值分别分离底物和背景荧光。基于小波变换的比尺度分析算法可以通过多分辨率分析实现底物光谱的线性分离。结合局部极小值的零阶Savitzky-Golay滤波算法是一种经验算法。它通过建立一条穿过局部极小值的平滑曲线来估计背景荧光。我们用模拟光谱和玻璃上记录的临床生物样品的拉曼光谱测试了我们的算法。三种胃癌细胞系的分析结果表明,加工光谱的分类误差明显降低。
更新日期:2020-11-12
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