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[Display omitted] • Resonance Raman spectroscopy is employed for diagnosis of diabetic retinopathy. • Statistical analysis of spectral data holds promise in early detection of the disease. • The study endorses carotenoid level in human serum as a biomarker for the pathosis. Diabetic retinopathy (DR) is a common health concern. Unfortunately, the metabolic pathway causing DR is yet to be understood. The carotenoid level in the human body is known to protect the health of the eyes. In this work, resonance Raman spectroscopy and multivariate analysis of the spectral data of human serum are reported as next-generation spectropathologic tools to detect retinal degeneration efficiently. The proposed technique shows promise by endorsing ocular carotenoids as a critical biomarker for such pathosis. Furthermore, the multivariate analysis of the spectral data distinguishes between two different stages of the disease. The machine learning algorithm is used to estimate a significant accuracy of 94% of the proposed model for the classification. As the carotenoid level can be controlled by dietary intake, we believe that the reported results also indicate a therapeutic role of the same in DR. [ABSTRACT FROM AUTHOR]

Highlights: [•] PCA of Raman data discriminates molecular structure of HEWL protein and fibrils. [•] SD of scores in PCA plots determines variability in the structure within a species. [•] Overall structure significantly differs for fibrils grown at different pH values. [•] Overall structure of fibrils grown at different pH values in Cu(II) has similarity. [•] Presence of Cu(II), inhibits the formation β-sheet component in HEWL fibrils. [ABSTRACT FROM AUTHOR]