SIGNAL PROCESSING FOR RAMAN SPECTRA FOR DISEASE DETECTION
Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10+3 to 10+7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression Analysis (LRA). It is found that PCA-LDA is the most popular (45%), ensued by PCA-ANN (33%) and SVM (22%). PCA-SVM yields the highest in accuracy (99.9%), followed by PCA-ANN (98%) and LRA (97%). PCA-LDA and SVM score the highest in both sensitivity-specificity.
Keywords: Raman Spectra, Surface Enhanced Raman Spectroscopy (SERS), Neural Network (NN), Support Vector Machine (SVM), Logistic Regression Analysis (LRA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA).
2. Fleischmann M, Hendra PJ, McQuillan AJ. Raman spectra of pyridine adsorbed at a silver electrode. Chem Phys Lett 1974;26:163-6.
3. Otto A. Surface enhanced raman spectroscopy (SERS). Surf Sci 1982;117:330.
4. Sigurdsson S, Philipsen PA, Hansen LK, Larsen J, Gniadecka M, Wulf HC. Detection of skin cancer by classification of Raman spectra. Biomed Eng IEEE Transactions 2004;51:1784-93.
5. Tsung-Heng T, Ting-Ting L, Yung-Ching H, Yu C, Tian-Jiun L, You-Hsuan L, et al. A multiscale approach for surface-enhanced Raman spectroscopy (SERS) spectrum representation and its application to bacterial discrimination. Proc Biomed Eng Inf China 2008;2:1247-52.
6. Vandenabeele P, Moens L. Introducing students to Raman spectroscopy. Anal Bioanal Chem 2006;385:209-11.
7. Movasaghi Z, Rehman S, Rehman IU. Raman spectroscopy of biological tissues. Appl Spectrosc Rev 2007;42:493-541.
8. Campion A, Kambhampati P. Surface-enhanced Raman scattering. Chem Soc Rev 1998;27:241-50.
9. Pichardo-Molina JL, Frausto-Reyes C, Barbosa-GarcÃa O, Huerta-Franco R, GonzÃ¡lez-Trujillo JL, RamÃrez-Alvarado CA, et al. Raman spectroscopy and multivariate analysis of serum samples from breast cancer patients. Lasers Med Sci 2007;22:229-36.
10. Zhou L, Sun Y, Li J, Boydston-White S, Masilamani V, Zhu K, et al. Resonance Raman and Raman spectroscopy for breast cancer detection. Technol Cancer Res Treat 2013;12:371-82.
11. Yan W, Shuang S, Dian Q, Anyu C, Zijian C, Yulu Y, et al. Preliminary study on early detection technology of lung cancer based on surface-enhanced Raman spectroscopy. Proc Biomed Eng Inf China 2010;1:2081-4.
12. Li X, Yang T, Li S, Yu T. Surface-enhanced Raman spectroscopy differences of saliva between lung cancer patients and normal people. Proc SPIE-OSA Biomedical Optics SPIE; 2011. p. 808722â€“5.
13. Harris A, Rennie A, Waqar-Uddin H, Wheatley S, Ghosh S, Martin-Hirsch D, Fisher S, et al. Raman spectroscopy in head and neck Cancer. Head Neck Oncol 2010;2:1-6.
14. Harris A, Garg M, Yang X, Fisher S, Kirkham J, Smith D, et al. Raman spectroscopy and advanced mathematical modeling in the discrimination of human thyroid cell lines. Head Neck Oncol 2009;1:1-6.
15. Lin D, Feng S, Pan J, Chen Y, Lin J, Chen G, et al. Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis. Opt Express 2011;19:13565-77.
16. Feng SY, Chen R, Lin J, Pan J, Chen G, Li Y, et al. Nasopharyngeal cancer detection based on blood plasma surface-enhanced Raman spectroscopy and multivariate analysis. Biosens Bioelectron 2010;25:2414-9.
17. Feng SY, Pan JJ, Wu YA, Lin D, Chen YP, Xi GQ, et al. Study on gastric cancer blood plasma-based on surface-enhanced Raman spectroscopy combined with multivariate analysis. Sci China: Life Sci 2011;54:828-34.
18. Duraipandian S, Zheng W, Ng J, Low JJH, Ilancheran A, Huang Z. In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques. Analyst 2011;136:4328-36.
19. Wang L, He D, Zeng J, Guan Z, Dang Q, Wang X, et al. Raman spectroscopy, a potential tool in diagnosis and prognosis of castration-resistant prostate cancer. J Biomed Opt 2013;18:87001-7.
20. Bispo JAM, de Sousa Vieira EE, Silveira JL, Fernandes AB. Correlating the amount of urea, creatinine, and glucose in urine from patients with diabetes mellitus and hypertension with the risk of developing renal lesions by means of Raman spectroscopy and principal component analysis. J Biomed Opt 2013;18:087004. Doi:10.1117/1.JBO.18.8.087004. [Article in Press]
21. Wang Y, Hua L, Liu J, Qu D, Chen A, Jiao Y, et al. Preliminary study on the quick detection of acquired immune deficiency syndrome by saliva analysis using surface enhanced Raman spectroscopic technique. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. Minneapolis, US: IEEE; 2009. p. 885â€“7.
22. Duraipandian S, Zheng W, Ng J, Low JJH, Ilancheran A, Huang Z. Near-infrared-excited confocal Raman spectroscopy advances in vivo diagnosis of cervical precancer. J Biomed Opt 2013;18:067007. Doi:10.1117/1.JBO.18.6.067007. [Article in Press]
23. Widjaja E, Zheng W, Huang Z. Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines. Int J Oncol 2008;32:653-62.
24. Li SX, Zeng QY, Li LF, Zhang YJ, Wan MM, Liu ZM, et al. Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection. J Biomed Opt 2013;18:27008. Doi:10.1117/1.JBO.18.2.027008. [Article in Press].
25. Li X, Yang T, Lin J. Spectral analysis of human saliva for detection of lung cancer using surface-enhanced Raman spectroscopy. J Biomed Opt 2012;17:0370031. Doi:10.1117/1.JBO.17.3.037003. [Article in Press]
26. Kho KW, Malini O, Shen ZX, Soo KC. Surface enhanced Raman spectroscopic (SERS) study of saliva in the early detection of oral cancer. Proceeding of SPIE; Bellingham, WA: SPIE; 2005. p. 84â€“91.
27. Filik J, Stone N. Analysis of human tear fluid by Raman spectroscopy. Anal Chim Acta 2008;616:177-84.
28. Reyes-Goddard JM, Barr H, Stone N. Surface enhanced Raman scattering of herpes simplex virus in the tear film. Photodiagn Photodyn Ther 2008;5:42-9.
29. Jolliffe IT. Principal component analysis. 2nd ed. Encyclopedia of statistics in behavioral science. New York, US: Springer-Verlag; 2002.
30. Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas 1960;20:141â€“51.
31. Cattell RB. The scree tests for the number of factors. Multivariate Behav 1966;1:245â€“76.
32. Radzol ARM, Lee KY, Mansor W, Othman NH. Principal component analysis for detection of NS1 molecules from Raman spectra of saliva. Proceeding of 11th International Colloquium on Signal Processing and Its Applications; Kuala Lumpur, Malaysia: IEEE; 2015. p. 168â€“73.
33. Fisher RA. The use of multiple measurements in taxonomic problems. Annu Eugen 1936;7:179â€“88.
34. McLachlan GJ. Discriminant analysis and statistical pattern recognition. New Jersey: John Wiley; 2004.
35. Cervo S, Mansutti E, Del Mistro G, Spizzo R, Colombatti A, Steffan A, et al. SERS analysis of serum for detection of early and locally advanced breast cancer. Anal Bioanal Chem 2015;407:7503-9.
36. Twon Tawi FM, Lee KY, Mansor W, Radzol ARM. Automated detection of non-structural protein 1 in saliva from Raman spectrum with linear discriminant analysis. Aust J Basic Appl Sci 2014;8:27-32.
37. Muller DA, Young PR. The flavivirus NS1 protein: molecular and structural biology, immunology, role in pathogenesis and application as a diagnostic biomarker. Antiviral Res 2013;98:192-208.
38. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97.
39. Radzol ARM, Lee KY, Mansor W. Classification of salivary-based NS1 from Raman spectroscopy with support vector machine. Proceeding of 36th Annual International Conference of the IEEE Engineering in Medicice and Biology Society, Chicago, US: IEEE; 2014. p. 1835â€“8.
40. Radzol ARM, Lee KY, Mansor W. Model selection for PCA-linear SVM for automated detection of NS1 molecule from Raman spectra of the salivary mixture. Proceeding of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy: IEEE; 2015. p. 2824â€“7.
41. Yan B, Wen Z, Li Y, Li L, Xue L. An intraoperative diagnosis of parotid gland tumors using Raman spectroscopy and support vector machine. Laser Phys 2014;24. Available from: http://dspace.xmu.edu.cn/handle/2288/93743. [Last accessed on 10 Dec 2016].
42. Yan B, Li B, Wen Z, Luo X, Xue L, Li L. Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors. BMC Cancer 2015;15:1â€“9.
43. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:115-33.
44. Yang T, Li X, Yu T, Sun R, Li S. Spectral discrimination of serum from liver cancer and liver cirrhosis using Raman spectroscopy. Proceeding of SPIE-Clinical and Biomedical Spectroscopy and Imaging II; SPIE; 2011. p. 808720.
45. Chen X, Wang G, Tao Z, Liu J, Yao H, Huang S, et al. Raman spectral discrimination of thalassemia erythrocytes based on PCA arithmetic and BP network model. Zhongguo Jiguang/Chin J Lasers 2009;36:2448-54.