Paper
17 September 2014 Detection of human serum proteins using Raman and SERS spectroscopy
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Proceedings Volume 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014); 923021 (2014) https://doi.org/10.1117/12.2068870
Event: Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 2014, Wuhan, China
Abstract
The use of normal Raman (NR) spectroscopy and surface enhanced Raman scattering (SERS) spectroscopy to analyze the biochemical information of human serum proteins and hence distinguish between normal and primary hepatic carcinoma (PHC) serum samples was investigated. The serum samples were obtained from patients who were clinically diagnosed with PHC (n=20) and healthy volunteers (n=20). All spectra were collected in the spectral range of 400-1800 cm-1 and analyzed through the multivariate statistical methods of principal component analysis (PCA). The results showed that both NR and SERS combined with PCA had good performance in distinguishing the human serum proteins between PHC patients and healthy volunteers with high sensitivity and specificity of 100%. And we can get more detail information of component and conformation of human serum proteins by considering NR and SERS spectrum. Our results support the concept again that serum protein Raman and SERS spectroscopy combined with PCA analysis both can become noninvasive and rapid diagnostic tools to detect the primary hepatic carcinoma.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiuyong Ruan, Fadian Liao, Juqiang Lin, Nenrong Liu, Jinyong Lin, Yongyi Zeng, Ling Li, Zufang Huang, and Rong Chen "Detection of human serum proteins using Raman and SERS spectroscopy", Proc. SPIE 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 923021 (17 September 2014); https://doi.org/10.1117/12.2068870
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Cited by 1 scholarly publication and 1 patent.
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KEYWORDS
Surface enhanced Raman spectroscopy

Proteins

Raman spectroscopy

Principal component analysis

Silver

Statistical analysis

Cancer

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