1
40
3
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1002/jrs.4924" target="_blank" rel="noreferrer noopener">http://doi.org/10.1002/jrs.4924</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
917-925
Issue
8
Volume
47
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Different Classification Algorithms And Serum Surface Enhanced Raman Spectroscopy For Noninvasive Discrimination Of Gastric Diseases
Publisher
An entity responsible for making the resource available
Journal of Raman Spectroscopy
Date
A point or period of time associated with an event in the lifecycle of the resource
2016
2016-08
Subject
The topic of the resource
atrophic gastritis; cancer; diagnosis; gastric; multivariate-analysis; quality; SERS; serum; Spectroscopy; surface enhanced Raman spectroscopy
Creator
An entity primarily responsible for making the resource
Li X Z; Yang T Y; Li S Q; Wang D L; Song Y T; Yu K D
Description
An account of the resource
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.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1002/jrs.4924" target="_blank" rel="noreferrer noopener">10.1002/jrs.4924</a>
Format
The file format, physical medium, or dimensions of the resource
Journal Article or Conference Abstract Publication
2016
atrophic gastritis
Cancer
Diagnosis
gastric
Journal Article or Conference Abstract Publication
Journal of Raman Spectroscopy
Li S Q
Li X Z
multivariate-analysis
quality
SERS
serum
Song Y T
Spectroscopy
surface enhanced Raman spectroscopy
Wang D L
Yang T Y
Yu K D
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1088/1054-660x/26/3/035702" target="_blank" rel="noreferrer noopener">http://doi.org/10.1088/1054-660x/26/3/035702</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
9-9
Issue
3
Volume
26
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Dublin Core
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Title
A name given to the resource
Raman Spectroscopy Combined With Principal Component Analysis And K Nearest Neighbour Analysis For Non-invasive Detection Of Colon Cancer
Publisher
An entity responsible for making the resource available
Laser Physics
Date
A point or period of time associated with an event in the lifecycle of the resource
2016
2016-03
Subject
The topic of the resource
colon cancer; colorectal-cancer; component analysis; diagnosis; guideline; hemoglobin; k nearest neighbour analysis; operation; Optics; Physics; principal; Raman spectroscopy; regression; serum; spectra; surveillance
Creator
An entity primarily responsible for making the resource
Li X Z; Yang T Y; Li S Q; Wang D L; Song Y T; Zhang S
Description
An account of the resource
This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm(-1) assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1088/1054-660x/26/3/035702" target="_blank" rel="noreferrer noopener">10.1088/1054-660x/26/3/035702</a>
Format
The file format, physical medium, or dimensions of the resource
Journal Article or Conference Abstract Publication
2016
colon cancer
colorectal-cancer
component analysis
Diagnosis
guideline
hemoglobin
Journal Article or Conference Abstract Publication
k nearest neighbour analysis
Laser Physics
Li S Q
Li X Z
operation
optics
Physics
principal
Raman spectroscopy
Regression
serum
Song Y T
spectra
surveillance
Wang D L
Yang T Y
Zhang S
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1088/1612-2011/12/11/115603" target="_blank" rel="noreferrer noopener">http://doi.org/10.1088/1612-2011/12/11/115603</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
11-11
Issue
11
Volume
12
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Study On Spectral Parameters And The Support Vector Machine In Surface Enhanced Raman Spectroscopy Of Serum For The Detection Of Colon Cancer
Publisher
An entity responsible for making the resource available
Laser Physics Letters
Date
A point or period of time associated with an event in the lifecycle of the resource
2015
2015-11
Subject
The topic of the resource
colon cancer; colorectal-cancer; discrimination; Optics; parameters; Physics; scattering sers; support; surface enhanced Raman spectroscopy; tumors; vector machine
Creator
An entity primarily responsible for making the resource
Li X Z; Yang T Y; Li S Q; Yao J; Song Y T; Wang D L; Ding J H
Description
An account of the resource
Surface enhanced Raman spectroscopy (SERS) has been recognized as an effective tool for the analysis of tissue samples and biofluids. In this work, a total of 27 spectral parameters were chosen and compared using SERS. Four parameters with the highest prediction ability were selected for further support vector machine (SVM) analysis. As a comparison, principal component analysis (PCA) was used on the same dataset for feature extraction. SVM was used with the above two data reduction methods separately to differentiate colon cancer and the control groups. Serum taken from 52 colon cancer patients and 60 healthy volunteers were collected and tested by SERS. The accuracy for Parameter-SVM was 95.0%, the sensitivity was 96.2%, and the specificity was 95.5%, which was much higher than the results using only one parameter, while for PCA-SVM, the results are 93.3%, 92.3%, and 92.9%, respectively. These results demonstrate that the SERS analysis method can be used to identify serum differences between colon cancer patients and normal people.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1088/1612-2011/12/11/115603" target="_blank" rel="noreferrer noopener">10.1088/1612-2011/12/11/115603</a>
Format
The file format, physical medium, or dimensions of the resource
Journal Article or Conference Abstract Publication
2015
colon cancer
colorectal-cancer
Ding J H
Discrimination
Journal Article or Conference Abstract Publication
Laser Physics Letters
Li S Q
Li X Z
optics
parameters
Physics
scattering sers
Song Y T
Support
surface enhanced Raman spectroscopy
tumors
vector machine
Wang D L
Yang T Y
Yao J