Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models
Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2015/619685 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850173610219536384 |
|---|---|
| author | Hui Chen Zan Lin Chao Tan |
| author_facet | Hui Chen Zan Lin Chao Tan |
| author_sort | Hui Chen |
| collection | DOAJ |
| description | Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis. |
| format | Article |
| id | doaj-art-cbcffd2a8d0c48d181c2d08a0ff2fa76 |
| institution | OA Journals |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-cbcffd2a8d0c48d181c2d08a0ff2fa762025-08-20T02:19:48ZengWileyJournal of Chemistry2090-90632090-90712015-01-01201510.1155/2015/619685619685Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric ModelsHui Chen0Zan Lin1Chao Tan2Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, ChinaKey Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, ChinaKey Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, ChinaNear-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.http://dx.doi.org/10.1155/2015/619685 |
| spellingShingle | Hui Chen Zan Lin Chao Tan Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models Journal of Chemistry |
| title | Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models |
| title_full | Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models |
| title_fullStr | Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models |
| title_full_unstemmed | Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models |
| title_short | Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models |
| title_sort | cancer discrimination using fourier transform near infrared spectroscopy with chemometric models |
| url | http://dx.doi.org/10.1155/2015/619685 |
| work_keys_str_mv | AT huichen cancerdiscriminationusingfouriertransformnearinfraredspectroscopywithchemometricmodels AT zanlin cancerdiscriminationusingfouriertransformnearinfraredspectroscopywithchemometricmodels AT chaotan cancerdiscriminationusingfouriertransformnearinfraredspectroscopywithchemometricmodels |