# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
## 3 Results and Discussion
The similarity of the plot of regression coefficients with two components in Figure 9b with the difference between mean spectra of Figure 2c suggests that two factors are also able to correctly discriminate control from exposed samples according to the intensity of Raman peaks related to nucleic acids components. As for the prediction ability of the model, one exposed sample of the test set was misclassified, so determining accuracy and sensitivity values of 93% and 86%, respectively, as reported in Table 1 and visible in the scatter plot of the projected samples, shown as hollow circles in Figure 9a. The misclassified sample of the test set has been labelled for clarity. Figure 8: Scores plot (Factor 2 vs. Factor 1) of the developed PLS-DA model, showing the calibration (filled circles) and projected test set (hollow circles) samples for the control-like (blue circles) and exposed-like (red circles) spectra in (**a**). The root mean square error for cross-validation (RMSECV) is 0.40. Regression coefficients for the PLS model with two components in (**b**). Furthermore, the PLS model developed from the calibration samples of the MCF7 and MDA cells rightly discriminates metastatic from malignant cells according to Factor 1, as visible in Figure 10a with the filled circles. The regression coefficients of the PLS model with two components are plotted in Figure 10b: the spectral shape of such plot is analogous to that of Figure 3c, which reports the difference spectrum between MCF7 and MDA mean spectra. Therefore, it can be deduced that two LVs correctly discriminate metastatic cells from malignant ones. Furthermore, in this case, the prediction ability of the model was good but not perfect because one MCF7 sample of the test set was misclassified, as reported in Table 1. The obtained sensitivity value was 100%, whereas the accuracy and specificity values were 95% and 90%, respectively. The misclassified sample is also visible in Figure 10a as red hollow circles which have been labelled for clarification purposes. In a comparison of the results obtained by the PLS-DA classification with those of the PCA-LDA classification, it is evident that the latter has a better performance in terms of accuracy and specificity. Figure 9: Scores plot (Factor 2 vs. Factor 1) of the developed PLS-DA model about Raman spectra for control (blue circles) and exposed (red circles) MCF10A cells, showing the calibration (filled circles) and projected test set (hollow circles) samples in (**a**). The root mean square error for cross-validation (RMSECV) is 0.75. One misclassified sample of the test set is enclosed inside a circle and labelled in (**a**). Regression coefficients for the PLS model with two components in (**b**). The above performance parameters values are better than those obtained by [PERSON] et al., who used patient tissues measured by Raman spectroscopy associated with the PLS-DA model and full cross-validation to diagnose colorectal cancer with a sensitivity of 77.7%, a specificity of 91.0%, and an accuracy of 84.3% [39]. Surface-enhanced Raman spectroscopy combined with the Lasso-PLS-DA algorithm with full cross-validation was used by [PERSON] et al. for the identification of different tumour states in nasopharyngeal cancer [40]: they yielded a diagnostic sensitivity of 68% and a specificity of 84.0% for separating T2-T4 stage from T1 stage cancer. Larger values of classification parameters were achieved by [PERSON] et al., who declared 87.10% accuracy, 80% sensitivity and 91.89% specificity by using the PLS-DA model with test set validation to discriminate first derivative FTIR data in nucleic acids spectral range collected from serum samples of patients with lung cancer and healthy people [25]. Recently, high sensitivity and specificity values (more than 90%) were also reported for the discrimination of FTIR spectra measured for two different melanoma cell lines (primary IPC-298 and metastatic SK-MEL-30) by using the PLS-DA model with test set validation [41]. Figure 10: Scores plot (Factor 2 vs.