血清SERS光谱分类分析方法比较  

Comparison of Classified Analysis Method of Serum SERS Spectroscopy

作　　者：邓悦 王亚平 张毅[2] Deng Yue;Wang Yaping;Zhang Yi(Department of Medical Physics,Public Basic College,Jinzhou Medical University,Jinzhou,121013;School of Physics and Optoelectronic Technology,Dalian University of Technology,Dalian,116024)

机构地区：[1]锦州医科大学公共基础学院医学物理教研室,锦州121013 [2]大连理工大学物理与光电工程学院,大连116024

出　　处：《数理医药学杂志》2020年第5期633-635,共3页Journal of Mathematical Medicine

基　　金：国家自然科学基金资助项目(11074029)。

摘　　要：随着拉曼光谱技术的迅速发展,对于血清的表面增强拉曼散射(SERS)光谱的研究也成为SERS光谱领域的研究热点,同时在血清SERS光谱的分类分析方法方面也已有了许多研究。本文主要对136例血清样品(35例健康人、58例乳腺病患者及43例肺癌患者)进行拉曼光谱采集,数据经过一定的预处理,最后应用主成分分析法(PCA)和层次聚类分析法(HCA)对采集的136例血清样品的SERS光谱数据进行分类分析及比较。初步可得出:两种分类分析方法对于血清样品的分类都是可行的。With the rapid development of Raman spectroscopy, the studies of surface enhanced Raman scattering(SERS) spectroscopy of serum have also become the hot topic in the field of SERS spectrum, while there have been a lot of research in terms of classified analysis of serum SERS spectra. In this paper, Raman spectra of 136 serum samples(35 cases of healthy people, 58 cases of breast patients and 43 cases of lung cancer patients) were acquired, and then a certain pretreatment of the data was done. At last, SERS spectra data of 136 cases of serum samples were classified and compared by the methods of principal component analysis and hierarchical clustering analysis. Preliminary Results: these two classification methods for serum samples classifying are possible.

关 键 词：表面增强拉曼散射光谱 血清 主成分分析 层次聚类分析 

分 类 号：O433.4[机械工程—光学工程]