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Myopia, a global public health problem, is recognized by the World Health Organization as the leading cause of visual impairment in uncorrected people. At present, a large number of reports focus on the pathological manifestations of retinal level myopia. However, corneal histological changes that may be associated with myopia have not been thoroughly investigated. Raman spectroscopy is a rapid and non-destructive analytical technique with the advantages including label-free, non-invasive and highly specific, providing detailed information at the molecular level. Important components of all the human tissue (proteins, nucleic acids, lipids, etc.) have corresponding Raman spectral characteristic peaks, which contribute to the study of myopia at the molecular level. In this study, a microscopic confocal Raman system (MCRS) was built to collect Raman spectrum of corneal stromal samples, which was obtained through femtosecond laser small incision corneal stroma lens extraction (SMLIE). One hundred fifty-nine corneal stromal Raman spectrum data were collected (54 low myopia, 69 moderate myopia and 36 high myopia). Ten characteristic peaks and corresponding components were further identified. K-nearest neighbor (KNN) was used with principal component analysis (PCA) to classify the samples and the classifications were validated by k-fold cross-validation. Three types of samples with different degrees of myopia were differentiated under the PCA-KNN model with an accuracy of 93.1%. Two characteristic peaks (1099 cm-1 and 2940 cm-1) show greatly contribution to the classification results. The results provide that Raman spectroscopy combined with PCA-KNN analysis can effectively distinguish the degree of myopia and is expected to explore the potential causes of myopia.
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