# Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
## 3 Results
### Classification by SVM  
In order to build the best automated model capable of resolving conformational signatures in DNA thin films, the SVM models were trained with different datasets and in different spectral range and then validated against unused spectra. For easier identification, the following nomenclature was chosen: the models with different calibration sets are denoted by numbers--SVM1, SVM2, etc.--while the models calculated for different spectral regions are denoted by letters (a), (b), etc. The results of all models, as well as their respective calibration and validation datasets are presented in Table 1.  
Figure 3: PC1 versus PC2 score plot in the decomposition model obtained using the first four PCs and data array containing wavenumbers in the 1800–650 cm\({}^{-1}\) range (entire spectral region captured by FTIR). The plot indicates clustering of the spectra with respect to time spent in a vacuum chamber. The initial classification model, SVM0, was built in order to test the ability of the algorithm to sort all 369 spectra with respect to the time spent in a vacuum chamber (seven classes representing 3-40 min films) in the entire spectral region, 1800-650 cm\({}^{-1}\). Out of each class of 3-40 min films, approximately 80% of samples were randomly chosen to form a calibration dataset (294 spectra), while the rest was used as a validation dataset (75 spectra). In this way, the model is trained on the calibration dataset that includes all respective desiccation times (all seven classes) and, even though the validation dataset introduces unused spectra, a great amount of variability is already introduced into the calibration dataset. Out of 75 spectra from the validation set, only two spectra were misclassified, yielding over 97% classification success rate: two 40 min films were misclassified as 25 min \begin{table}
\begin{tabular}{c c c c c} \hline
**SVM0** & & **\# Spectra** & **1800–650** & \\ \hline Calibration ds: 3–40 min & 3 min & 11 & 11 \\ Validation ds: 3–40 min & 5 min & 36 & 36 \\  & 10 min & 11 & 11 \\  & 15 min & 4 & 4 \\  & 20 min & 2 & 2 \\  & 25 min & 2 & 2 \\  & 40 min & 9 & 7 \\ Success rate: & & & 97\% (73/75) & \\ \hline
**SVM1** & & **\# Spectra** & **1800–935** & **1800–1550** & **1320–1155** \\ \hline Calibration ds: 3–40 min & Class 1 & 11 & 11 & 10 \\ Validation ds: 3–40 min & Class 2 & 36 & 36 & 35 \\  & Class 3 & 28 & 28 & 27 \\ Success rate: & & & 100\% & 96\% \\ \hline
**SVM2** & & **\# Spectra** & **1800–935** & **1800–1550** & **1320–1155** & **1155–990** \\ \hline Calibration ds: 3, 5, 10, 40 min & & & & \\ Validation ds: 15, 20, 25 min & Class 3 & 36 & 36 & 35 \\ Success rate: & & & 100\% & 100\% & 97\% \\ \hline
**SVM3** & & **\# Spectra** & **1800–935** & & \\ \hline Calibration ds: 3, 5, 40 min & & & & \\ Validation ds: 10, 15, 20, 25 min & Class 3 & 90 & 22 & \\ Success rate: & & & 24\% & \\ \hline
**SVM4** & & **\# Spectra** & **1800–935** & & \\ \hline Calibration ds: 3, 5, 15, 40 min & & & & \\ Validation ds: 10, 20, 25 min & Class 3 & 72 & 59 & \\ Success rate: & & & 82\% & \\ \hline
**SVM5** & & **\# Spectra** & **1800–935** & & \\ \hline Calibration ds: 3, 5, 10 min & & & & \\ Validation ds: 15, 20, 25, 40 min & Class 3 & 81 & 81 & \\ Success rate: & & & 100\% & \\ \hline
**SVM6** & & **\# Spectra** & **1800–935** & & \\ \hline Calibration ds: 3, 5, 10 min & & & & \\ Validation ds: Class 2 spectra & Class 2 & 3 & 3 & \\ from our DNA library & & & & \\ Success rate: & & & 100\% & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of SVM models and obtained validation results. The abbreviation used for datasets is ds. The SVM0 model was trained to classify spectra according to seven distinct desiccation times (seven classes), from 3 to 40 min, while SVM1-SVM6 models were trained on the spectra categorized with respect to the level of B form: class 1 (40%, 3 min film), class 2 (60%, 5 min film) and class 3 (75%, 10–40 min films).