In silico machine learning-enabled detection of polycyclic aromatic hydrocarbons from contaminated soil.

ABSTRACT: The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their modified derivatives in contaminated soil is challenging due to the chemical and microbial complexity of soil organic matter. To address these challenges, we developed an innovative analytical approach that combines Surface-enhanced Raman spectroscopy with a Raman spectral library constructed in silico using density functional theory (DFT)-calculated spectra. This method overcomes several limitations associated with traditional experimental libraries, including spectral background interference, solvent effects, and commercially unavailable or challenging to synthesize compounds. Our methodology employs a physics-informed machine learning pipeline that operates in two stages: the characteristic peak extrac

SUBMITTER: Ju Y 

PROVIDER: S-EPMC12088428 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature