## 4 Discussion  
RS is a rapid and noninvasive technique, bringing many advantages. It takes relatively less time and does not require complicated operations during the detection, compared with other current detecting techniques. By using tears as detecting subjects, DCDRS can directly provide Raman signal without enhancement methods, which is rather convenient compared to other RS methods, such as surface-enhanced RS. [PERSON] et al. used RS with tear samples to distinguish different ocular diseases, including conjunctivitis, plepharitis, meibomian gland cyst, cataract and glaucoma [42]. However, the detailed analysis and discrimination of glaucoma with RS still remain. In this study, we used DCDRS to obtain Raman spectra for POAG and PCAG tear samples for the first time. According to the results, the characteristics of the spectra showed significant differences between glaucoma patients and normal individuals. Based on the characteristic peaks of Raman spectra, amino acids (tyrosine, tryptophan, phenylalanine and hydroxyproline), proteins (amide I and amide III) and lipids produced strong Raman signals and played important roles in the detection of glaucoma. Because these substances are closely related to the metabolism of the eye, glaucoma may lead to content and structure changes in tears at the molecular level. Based on their rich biochemical composition and easy collection, tears can be a potential option for the detection of glaucoma and its diagnosis through RS. For the comparison between the glaucoma and the normal groups, all the Raman peaks from amino acids of the glaucoma groups had significantly higher intensities than those from the normal group, revealing that there was more content of amino acids in the glaucoma tears than the normal ones. This is consistent with a previous study, which reported enriched metabolic pathways of amino acids, including phenylalanine, tyrosine and tryptophan [44]. Compared with normal individuals, eight Raman peaks of PACG patients had significant disparities, which can be attributed to various molecular information, including ring torsion mode (447 cm\({}^{-1}\)), ring breathing mode (760 and 853 cm\({}^{-1}\)) and twisting mode. In contrast, for the POAG group, five Raman bands, including bands at 447 (ring torsion mode of phenylalanine) and 1313 cm\({}^{-1}\) (CH3 CH2 twisting mode of collagens and lipids), were statistically different from the normal group. The changes in amide III and proteins/lipids content in tears correspond to the changes in glaucomatous retinal tissues from previous reports [28,29]. Comparing POAG with PACG groups, the intensities of all the different Raman peaks (853, 880, 1270 and 1313 cm\({}^{-1}\)) of the POAG group were lower than those from the PACG group. For these two sub-groups, the reasons for the different contents of amino acids, proteins and lipids need to be investigated further. Furthermore, the intensity of the Raman peak at 1270 cm\({}^{-1}\) showed significantly differences across all three groups. When using tears as detection samples, this peak can be considered as a marker in the discrimination of glaucoma. Considering the high dimensionality of the Raman spectra, we used the dimension-reduction technique PCA to extract PCs from the Raman spectra data. It not only reduced the dimension of data but also minimized the information loss, reducing sensitivity of LDA to data distribution. LDA was used to build the classifier based on the PCs. The total accuracy of the PCA-LDA-based SVM model reached 93.2% and 90.9% for two different experimental settings, which both perform efficiently. The sensitivities of the classification of POAG, PACG and normal groups were 88.9%, 89.5% and 100%, respectively; the specificity values of the three groups were 97.8%, 94.4% and 97.8%, respectively. We created ROC curves based on the 30% test dataset classification results and calculated AUC values. An AUC value between 0.7 and 0.8 means the classifier is considered acceptable, and a value between 0.8 and 0.9 is considered excellent.