## 3 Results and Discussion
### Comparison of Discrimination Models  
The Figure 2b gives representative peaks of the A549 cells. Proteins have strong Raman peaks at 1003 cm\({}^{-1}\) (phenylalanine), 1449 cm\({}^{-1}\) (CH\({}_{2}\) deformation), and 1658 cm\({}^{-1}\) (Amide I). The main Raman peaks associated nucleic acids correspond to 1176 cm\({}^{-1}\) (cytosine, guanine), 1339 cm\({}^{-1}\) (G (DNA/RNA)) and 1580 cm\({}^{-1}\) (pyrimidine ring of nucleic acids). Lipids have strong Raman peaks at 1304 cm\({}^{-1}\) (CH\({}_{2}\) deformation) and 1449 cm\({}^{-1}\) (CH\({}_{2}\) bending mode in malignant tissue) [28; 29; 30]. Raman spectra distinguish the cancer cells coming from different organs, different malignancy degree are all based on the difference of the cellular biochemical components. The basic biochemical components of human cells are consistent, with only a few differences in the chemical composition, so Raman band assignment of the 8 cell lines are similar (Figure 3). Therefore, the identification of 8 types of cells by Raman spectroscopy must be carried out by the means of Chemometrics. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), two classic discriminant methods, were employed in classification of the eight human cells based on Raman data. LDA and QDA are two of the best-known discriminant analysis approaches, which have been successfully used for the appraisement in various fields [30; 31]. The boundaries that separate groups or classes of samples are calculated using LDA and QDA. Linear boundaries, where a straight line or hyperplane divides the variable space into regions, and quadratic boundaries, where a quadratic curve divides the variable space into regions, were generated by LDA and QDA, respectively. LDA fits a multivariate normal density to each group, with a pooled estimate of covariance. It does not take into account different variance structures for the two classes. QDA fits multivariate normal densities with covariance estimates stratified by group. It allows for discriminating classes which have significantly different class-specific covariance matrices and forms a separate variance model for each class. QDA classifier focuses on finding a transformation of the input features which is able to optimally distinguish between the different classes in the dataset [32; 33; 34; 35; 36; 37; 38]. Figure 4: 3-dimensional (3D) space with the top three PCs for the eight different human cells. Figure 5 shows the identification results of LDA and QDA models with 12 PCs. Sample numbers of the eight cell lines for LDA and QDA models with 12 PCs, is shown in Table 2. For the LDA model, the classification rate by cross-validation was 91.21%. A few samples from several groups were wrongly classified in the prediction set; for the QDA model, the classification rate was 100%, which is better than LDA model. The classification results of LDA and QDA models influenced by the number of PCs are presented in Figure 6. As shown in Figure 6, the optimal LDA model was obtained when 15 PCs were used; the optimal QDA model were generated when 12 PCs were employed, and QDA consistently gives a relatively high identification rate. In order to get a discrimination model for human cell types with good performance, LDA and QDA models were attempted comparatively. Identification results from two models in the calibration set and prediction set, is shown in Table 3. Contrasting to the LDA model, the QDA model obtains a comparatively better performance. It indicated that the quadratic information was helpful to improve the classification performance in the prediction set. Investigated between LDA and QDA models, the LDA adopted hyperplane to classify the samples, while the QDA used higher complexity hypersurface as separator [39]. Because of preferable generalization in its theory, QDA results in a better result than LDA model in prediction set. Quadratic discriminant method is stronger in the level of self-learning and self-adjust than linear discriminant method.