PCA-LDA-based multivariate diagnostic algorithms were subsequently rendered on the composite NIR AF/Raman spectra measured to extract the diagnostic information associated with underlying spectroscopy modalities (i.e., confocal Raman, NIR AF, and the composite NIR AF/Raman) for discrimination between dysplasia and normal cervical tissues. The composite NIR AF/Raman spectra were mean centered to eliminate the common variance. Hotelling and Q-residuals were employed to remove the spectra with unusual line shape variations due to noncontact measurements and probe handling variations.19Figure 2 shows the diagnostically significant PC loadings [PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, ()], representing the variations in the major tissue Raman peaks (e.g., 854, 937, 1095, 1253, 1311, 1445, and ) together with PC1 (99.93%, ), typically exhibiting the broad tissue NIR AF features. LDA classifier was further employed on the diagnostically significant PCs, and the cross-validated classification results for each spectrum belonging to normal and precancer tissue categories were obtained (Fig. 3). The PC1 constituting the tissue AF features rendered a diagnostic accuracy of 59.6% [sensitivity of 49.8% (123/247) and specificity of 69.4% (689/993)]. A diagnostic accuracy of 84.1% [sensitivity of 81.0% (200/247) and specificity of 87.1% (865/993)] could be achieved using PC4, PC5, and PC8, primarily describing the significant tissue Raman spectral features (e.g., 854, 937, 1095, 1253, 1311, 1445, and ). The PCs representing tissue AF (PC1) as well as Raman features (PC4, PC5, and PC8) were combined together to assess the diagnostic ability of the composite NIR AF/Raman spectroscopy modality, achieving a diagnostic accuracy of 82.3% [sensitivity of 76.9% (190/247) and specificity of 87.7% (871/993)]. In addition, the diagnostic performance of the three spectroscopic modalities was further evaluated by splitting the total dataset into a training set (70% of the total dataset) and a testing set (30% of the total dataset). The redeveloped PCA-LDA models using the PCs representing tissue Raman, the composite NIR AF/Raman, and NIR AF features provided predictive accuracies of 86.1% (95% CI: 83.6 to 88.7%), 85.7% (95% CI: 80.5 to 90.8%), and 59.8% (95% CI: 52.6 to 66.9%) for cervical precancer classification, respectively.