1 October 2011 Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes
Author Affiliations +
J. of Biomedical Optics, 16(10), 107006 (2011). doi:10.1117/1.3642010
Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of glaucoma is critical for the prevention of permanent structural damage and irreversible vision loss. Raman spectroscopy is a technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, we explored the potential of using Raman spectroscopy for detection of glaucomatous changes in vitro. Raman spectroscopic imaging was conducted on retinal tissues of dogs with hereditary glaucoma and healthy control dogs. The Raman spectra were subjected to multivariate discriminant analysis with a support vector machine algorithm, and a classification model was developed to differentiate disease tissues versus healthy tissues. Spectroscopic analysis of 105 retinal ganglion cells (RGCs) from glaucomatous dogs and 267 RGCs from healthy dogs revealed spectroscopic markers that differentiated glaucomatous specimens from healthy controls. Furthermore, the multivariate discriminant model differentiated healthy samples and glaucomatous samples with good accuracy [healthy 89.5% and glaucomatous 97.6% for the same breed (Basset Hounds); and healthy 85.0% and glaucomatous 85.5% for different breeds (Beagles versus Basset Hounds)]. Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue with a high specificity.
Qi Wang, Chenxu Yu, Sinisa D. Grozdanic, Matthew M. Harper, Helga Kecova, Tatjana Lazic, Nicolas Hamouche, "Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes," Journal of Biomedical Optics 16(10), 107006 (1 October 2011). http://dx.doi.org/10.1117/1.3642010
Submission: Received ; Accepted

Raman spectroscopy



Data modeling

Signal to noise ratio


Principal component analysis

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