Use of computational techniques in Raman spectra analysis

Raman spectroscopy is a powerful analytical technique widely used across scientific fields due to its ability to generate unique spectral fingerprints for different materials in a non-destructive manner. By analyzing energy shifts caused by molecular vibrations, this method provides valuable insights into the structural and chemical properties of samples. However, raw Raman spectra often contain unwanted background signals, such as fluorescence, which can obscure key spectral features. To address this, preprocessing techniques like baseline correction and peak deconvolution are essential for enhancing spectral resolution and ensuring accurate interpretation. A variety of methods exist for these preprocessing steps, requiring careful selection to optimize spectral analysis. In this study, both visual and numerical comparisons—along with the interpretation of the χ² statistical test—were used to evaluate different approaches. Among them, the Asymmetric Least Squares method for baseline correction and the Pseudo-Voigt function for peak deconvolution proved to be the most effective. These techniques significantly improved the quality of Raman spectra, facilitating better identification of vibrational characteristics. By refining spectral preprocessing, this study contributes to more precise and reliable Raman spectroscopy applications, enhancing its use in fields such as materials science, chemistry, and biomedical research.