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Appl. Sci. 2017, 7(9), 900; doi:10.3390/app7090900

Distinguishing Different Cancerous Human Cells by Raman Spectroscopy Based on Discriminant Analysis Methods

1
Chongqing Key Laboratory of Multi-Scale Manufacturing Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
2
School of Electronic Information Engineering, Yangtze Normal University, Chongqing 408100, China
3
College of Instrumentation Science and Electrical Engineering, Jilin University, Changchun 130061, China
*
Authors to whom correspondence should be addressed.
Received: 20 July 2017 / Revised: 30 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
(This article belongs to the Special Issue Optics and Spectroscopy for Fluid Characterization)
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Abstract

An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623~1783 cm−1 were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination model, a linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were attempted comparatively in the work. The results showed that the QDA model is better than the LDA model. The optimal QDA model was generated with 12 principal components. The classification rates are 100% in the calibration and prediction set, respectively. From the experimental results, it is concluded that Raman spectroscopy combined with appropriate discriminant analysis methods has significant potential in human cell detection. View Full-Text
Keywords: Raman spectra; discriminant analysis; distinguish; human cells Raman spectra; discriminant analysis; distinguish; human cells
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Tang, M.; Xia, L.; Wei, D.; Yan, S.; Du, C.; Cui, H.-L. Distinguishing Different Cancerous Human Cells by Raman Spectroscopy Based on Discriminant Analysis Methods. Appl. Sci. 2017, 7, 900.

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