Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles hence focus many areas study, from cellular functions to cancer diagnostics drug treatment. Due heterogeneity sensitivity secondary structure there is a need for tools capable rapid reliable quantification DNA conformation in diverse environments. In this work, second paper series that addresses conformational transitions thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: principal component analysis (PCA), support vector machine (SVM) learning algorithm, regression (PCR), order quantify categorize different hydrated states. By complementing technique multivariate statistical methods, demonstrate ability our sample preparation automated spectral protocol rapidly efficiently determine based on vibrational signatures 1800–935 cm−1 range. Furthermore, assess impact small hydration-related changes spectra detection how avoid discrepancies by careful sampling.