# Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
## 3 Results
### Classification by SVM
The respective spectral regions (in cm\({}^{-1}\)) included in the model are indicated in the columns above the number of spectra obtained per class. Label # spectra represents the size of the validation ds (the total number of spectra per each class included in the validation dataset), while the success rate is the ratio of correctly classified spectra and the size of the validation ds. films. Such a high classification rate indicated that the spectral profiles of each desiccation time are indeed unique and that the SVM algorithm is able to correctly classify a large number of data with respect to their spectral fingerprint. As our previous work showed that the 3- and 5-min films have distinct conformation signatures compared to the 10-40 min films, the classification ability of samples with respect to three different levels of B-form was tested. Seven classes representing respective vacuum times were replaced by three classes representing different levels of %B-form: class 1 (40%, 3 min films), class 2 (60%, 5 min films), and class 3 (75%, 10-40 min films). We note that %B-form was calculated using the integrated intensity ratios of conformation bands as shown in our previous work [20,31]. The first classification of samples with respect to %B-form (SVM1) was performed on the previously presented dataset with randomly chosen 294 and 75 spectra in the calibration and validation set, respectively. The SVM1 models were calculated with respect to three classes of B-form (see the previous paragraph) for three different spectral regions: (a) the base and phosphate region from 1800 to 935 cm\({}^{-1}\), (b) base region from 1800 to 1550 cm\({}^{-1}\) and (c) asymmetric PO\({}_{2}\) region from 1320 to 1155 cm\({}^{-1}\). The (a) and (b) models had 100% success rate (75 correct), whereas the (c) model misclassified three spectra, one from each class, yielding 96% success rate. Such high levels of success rates for all three SVM1 models, (a)-(c), confirms that the spectral characteristics, indicated by visual inspection as well as machine-determined, in the base and phosphate region are tightly related to conformational changes observed in the 900-750 cm\({}^{-1}\) region. However, note that the training of the SVM1 model was done on the calibration dataset that included a portion of spectra from all desiccation times (3-40 min films), the same as the validation dataset, which means that the calibration model already accounts for most of the variability in the spectra. In order to simulate a real-life application, it is important to evaluate models for predicting samples with an unexpected variability. This was achieved by building calibration datasets that include three different classes of B-form; however, only certain desiccation times are selected for calibration, representing a more realistic model, with an entire unknown class of samples added to the validation dataset. The SVM2-SVM5 models were all built in such a way: different combinations of desiccation times were chosen as the calibration datasets and tested for selected spectral regions. The first classification of samples with respect to B-form (classes 1-3) based on new samples added to the validation set was SVM2. The calibration set included 333 spectra of 3-, 5-, 10-, and 40-min films (all three classes), while the validation set included 36 spectra of 15, 20, and 25 min films (only class 3). The model was calculated for four spectral regions: (a) the base and phosphate region from 1800 to 935 cm\({}^{-1}\), (b) base region from 1800 to 1550 cm\({}^{-1}\), (c) asymmetric PO\({}_{2}\) region from 1320 to 1155 cm\({}^{-1}\), and (d) symmetric PO\({}_{2}\) region from 1155 to 990 cm\({}^{-1}\). The SVM2 models (a)-(c) had 100% success rate (36 correct), while the (d) model for the symmetric PO\({}_{2}\) region misclassified 1 spectrum, yielding 97% success rate.