Statistical analysis of Raman Spectra of biofuels: The case of myristic acid conformers

Biofuels derived from microalgae offer a sustainable alternative to fossil fuels, but their application is hindered by high production costs. Optimizing photobioreactors for biofuel production requires precise characterization of algal biomass, particularly its organic components. Raman spectroscopy is a powerful tool for this purpose, but the challenge lies in differentiating the spectral contributions of individual compounds and identifying their conformers in complex mixtures. In this study, we employ Raman spectroscopy and statistical analysis to distinguish conformers of fatty acids, using myristic acid as a model. Benchmark calculations of Raman spectra show that the dispersion corrected B3LYP-D3 DFT method in conjunction with the 6-311++G** basis set provides an optimal balance between accuracy and computational efficiency. The inclusion of solvent (water) effects ensures that experimental conditions are realistically modeled. Statistical techniques streamline the analysis of large spectral datasets and enable the classification of conformers into three sets, namely chain, v-shaped, and twisted structures. By isolating key spectral regions, we identify decisive features—such as CH${}_2$/CH${}_3$ vibrations at about 2900 cm⁻¹ and backbone motions, below 1200 cm⁻¹, that distinguish these conformers. This approach offers a robust framework for the rapid analysis of molecular spectra and the identification of fatty acids in algal biomass.