Different Classification Algorithms And Serum Surface Enhanced Raman Spectroscopy For Noninvasive Discrimination Of Gastric Diseases

Different Classification Algorithms And Serum Surface Enhanced Raman Spectroscopy For Noninvasive Discrimination Of Gastric Diseases

Li X Z; Yang T Y; Li S Q; Wang D L; Song Y T; Yu K D

Journal of Raman Spectroscopy

2016

2016-08

In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre-operation and post-operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA-SVM, PCA-LDA and PCA-CART, respectively. Copyright (c) 2016 John Wiley & Sons, Ltd.

atrophic gastritis; cancer; diagnosis; gastric; multivariate-analysis; quality; SERS; serum; Spectroscopy; surface enhanced Raman spectroscopy

Journal Article or Conference Abstract Publication

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917-925

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