AUTOMATED EXTRACTION OF PRINCIPAL COMPONENTS OF NON-STRUCTURAL PROTEIN 1 FROM SERS SPECTRUM

Afaf Rozan Mohd Radzol, Yoot Khuan Lee, Wahidah Mansor, Faizal Mohd Twon Tawi Faculty

References

  1. [1] A. Otto, “Surface Enhanced Raman Spectroscopy(SERS),” Surface Science, vol. 117, no. 1–3, pp. 330–330, 1982.
  2. [2] T.-H. Tsai, T.-T. Liu, Y.-C. Huang, Y. Chen, T.-J. Liu,Y.-H. Lin, Y.-L. Wang, J.-K. Wang, and D.-W. Wang,“A Multiscale Approach for Surface-enhanced RamanSpectroscopy (SERS) Spectrum Representation and itsApplication to Bacterial Discrimination,” 2008International Conference on BioMedical Engineeringand Informatics, pp. 328–333, May 2008.
  3. [3] S. Sigurdsson, P. A. Philipsen, L. K. Hansen, J. Larsen,M. Gniadecka, and H. C. Wulf, “Detection of skincancer by classification of Raman spectra.,” IEEEtransactions on bio-medical engineering, vol. 51, no.10, pp. 1784–93, Oct. 2004.
  4. [4] A. T. Harris, A. Rennie, H. Waqar-Uddin, S. R.Wheatley, S. K. Ghosh, D. P. Martin-Hirsch, S. E.Fisher, A. S. High, J. Kirkham, and T. Upile, “Ramanspectroscopy in head and neck cancer.,” Head & neckoncology, vol. 2, pp. 26, Jan. 2010.
  5. [5] A. T. Harris, M. Garg, X. B. Yang, S. E. Fisher, J.Kirkham, D. A. Smith, D. P. Martin-Hirsch, and A. S.High, “Raman spectroscopy and advancedmathematical modelling in the discrimination of humanthyroid cell lines.,” Head & neck oncology, vol. 1, pp.38, Jan. 2009.
  6. [6] Y. Wang, S. Sun, D. Qu, A. Chen, Z. Cui, Y. Yao, Y.Jiao, X. Guo, and C. Liu, “Preliminary study on earlydetection technology of lung cancer based on surface-enhanced Raman spectroscopy,” 2010 3rd InternationalConference on Biomedical Engineering andInformatics, no. Bmei, pp. 2081–2084, Oct. 2010.
  7. [7] X. Li, T. Yang, S. Li, and T. Yu, “Surface-enhancedRaman spectroscopy differences of saliva between lungcancer patients and normal people,” Imaging, vol. 8087,pp. 808722–808722–5, 2011.
  8. [8] K. E. Shafer-peltier, A. S. Haka, M. Fitzmaurice, J.Crowe, J. Myles, R. R. Dasari, and M. S. Feld, “Ramanmicrospectroscopic model of human breast tissue :implications for breast cancer diagnosis in vivo,” pp.552–563, 2002.
  9. [9] X. Li, Y. Wang, X. Zhang, D. Wang, and J. Lin,“Detection and identification of Colon Cancer andRectum Cancer Using Fluorescence and RamanSpectrum.,” Conference proceedings : ... AnnualInternational Conference of the IEEE Engineering inMedicine and Biology Society. IEEE Engineering inMedicine and Biology Society. Conference, vol. 2, pp.1453–6, Jan. 2005.
  10. [10] W. Yan, H. Lin, L. Jinghua, Q. Dian, C. Anyu, J. Yi, G.Xun, L. Chunwei, H. Wen, and W. Hong, “PreliminaryStudy on the Quick Detection of Acquired ImmureDeficiency Syndrome by Saliva Analysis using SurfaceSpot PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9spot 1 202.33 641.87 3.91 0.74 1.52 0.02 -4.12 4.29 1.29spot 2 283.54 -105.36 67.24 150.50 98.76 -71.03 16.57 -1.79 0.45spot 3 341.08 -108.01 6.54 41.25 -29.17 149.48 53.50 57.66 25.91spot 4 -574.94 -12.80 -141.31 9.60 73.78 38.41 5.49 1.57 -89.92spot 5 -389.13 -50.26 -53.17 47.07 -40.73 -13.67 -120.90 35.15 87.42spot 6 -172.45 -52.33 -14.73 -11.34 -108.30 -99.36 88.77 64.13 -23.85spot 7 565.70 -134.08 -28.70 -147.73 70.30 -34.41 -38.63 46.41 2.85spot 8 392.77 -103.88 18.92 16.73 -94.40 9.51 -69.29 -82.73 -81.74spot 9 -57.82 -50.38 -64.21 -39.66 6.64 -5.72 73.00 -115.99 85.01spot 10 -591.08 -24.77 205.51 -67.16 21.61 26.76 -4.39 -8.70 -7.43Variance 175154.99 52457.15 8347.96 6108.61 4866.90 4549.59 4146.93 3469.56 3437.88Proportion of Total Variance (%) 66.72 19.98 3.18 2.33 1.85 1.73 1.58 1.32 1.31Cummulative of Total Variance (%) 66.72 86.70 89.88 92.20 94.06 95.79 97.37 98.69 100.00192Enhanced Raman Spectroscopic technique,” Aids, pp.885–887, 2009.
  11. [11] M. Sattlecker, C. Bessant, J. Smith, and N. Stone,“Investigation of support vector machines and Ramanspectroscopy for lymph node diagnostics.,” TheAnalyst, vol. 135, no. 5, pp. 895–901, May 2010.
  12. [12] M. F. S. A. A. T. M. D. A. F. Vincent Deubel,“Enzyme-Linked Immunosorbent Assay Specific toDengue Virus Type 1 Nonstructural Protein NS1Reveals Circulation of the Antigen in the Blood duringthe Acute Phase of Disease in Patients ExperiencingPrimary or Secondary Infections.” American Societyfor Microbiology, pp. 376–381, 2002.
  13. [13] B. D.Lindenbach and C. M. Rice, "Molecular biologyof flaviviruses," in Advances in Virus Research. vol.Volume 59, ed: Academic Press, pp. 23-61, 2003.
  14. [14] M. G. Jacobs, P. J. Robinson, C. Bletchly, J. M.Mackenzie, and P.R. Young, "Dengue virusnonstructural protein 1 is expressed in a glycosyl-phosphatidylinositol-linked form that is capable ofsignal transduction," FASEB Journal, vol. 14, pp. 1603-1610, 2000.
  15. [15] R. Sahak, “Classification Of Infant Cry Signal WithAsphyxia Using Support Vector Machine With FeatureOptimization Methods,” Unpublished, no. January, pp.2012, 2012.
  16. [16] Orozco J. and Garcia C. A. R., “Detecting Pathologiesfrom Infant Cry Applying Scaled Conjugate GradientNeural Networks”, Proceeding of ESANN, 249 – 354,2003.
  17. [17] R. Sahak, W. Mansor, L. Y. Khuan, A, Zabidi, F.Y. A.Rahman, “An Investigation into Infant Cry and ApgarScore Using Principal Component Analysis”, 5th SignalProcessing and Its Application Colloqium, pp. 209-214,2008.
  18. [18] B. Williams, Mr Brett , Andrys, Onsman, Ted,“Exploratory factor analysis: A five-step guide fornovices,” vol. 8, no. 3, pp. 1–13, 2010.
  19. [19] D. Garc, “Fault detection using Principal ComponentAnalysis ( PCA ) in a Wastewater Treatment Plant (WWTP ).”
  20. [20] H. Hasan, N. Tahir, and U. T. Mara, “Feature Selectionof Breast Cancer Based on Principal ComponentAnalysis,” pp. 242–245, 2010.

Important Links:

Go Back