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Detection of glioma by surface‐enhanced Raman scattering spectra with optimized mathematical methods
Author(s) -
Sun Jiaojiao,
Fang Hao,
Zhang Zhiqiang,
Chen Mingli,
Tian Jingjing,
Chen Liang,
Zou Xiang,
Yin Huancai,
Yin Jian
Publication year - 2019
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.5634
Subject(s) - raman spectroscopy , raman scattering , support vector machine , glioma , laser , biological system , analytical chemistry (journal) , cross validation , chemistry , materials science , pattern recognition (psychology) , artificial intelligence , computer science , optics , chromatography , physics , biology , cancer research
This work aimed to establish a fast and accurate method to detect glioma by combining surface‐enhanced Raman scattering (SERS) and mathematical analysis. At first, 785‐nm laser was selected as the optimum laser to acquire Raman spectra of human brain tissue. Second, it was verified that Raman data in the range of 1,200–1,600 cm −1 could improve the performance of classifier. Based on the analytical results of 1,200–1,600 cm −1 data, the sensitivity and specificity of partial least square (PLS) analysis and back‐propagation neural network (BPNN) were as high as 100%, whereas the sensitivity and specificity of support vector machine (SVM) were 96% and 100%, respectively. Among them, PLS was more potential in the detection of glioma, because of its lower computational cost compared with SVM and BPNN. Moreover, the correlation between observed Raman peaks and 2‐hydroxyglutarate (2HG; 512, 790, 1,204, 1,302, and 1,463 cm −1 ) was observed, suggesting 2HG as a potential marker of glioma using its Raman spectroscopic signatures. After all, SERS combining with mathematical analysis could be a promising tool for the accurate detection of glioma.