DFT-Based Theoretical Study on Label-Free SERS Detection of Type B Fumonisins: New Insights into Molecular-Substrate Interactions and Quantification Strategies

In this study, we employed density functional theory to investigate the interactions between type B fumonisins (FB1, FB2, and FB3) and silver-enhancing substrates in the surface-enhanced Raman scattering effect. Theoretical calculations of the molecular electrostatic potential reveal that the oxygen atoms at the terminal of the tricarboxylic acid structure in all three molecules exhibit the strongest electronegativity, suggesting these sites as potential active sites for molecular-substrate interactions. Molecular-Ag₂₀ vertex-binding/surface-binding complex models were constructed based on possible docking modes between the molecule and the substrate, and binding energies were calculated. The binding energy results confirm the stable existence of the complexes. By analyzing the frontier molecular orbitals and charge density difference maps of the molecules and complexes, charge transfer excitation near the active sites between the molecule and Ag₂₀ was confirmed. Analysis of the theoretical Raman spectra of vertex-binding/surface-binding complexes revealed selective enhancement phenomena associated with different docking modes. These findings provide a feasible strategy for the simultaneous quantification of the overall content of type B fumonisins and the individual components of FB1, FB2, and FB3 in a single detection. Finally, based on the characteristic frequency variations of the three molecules, a theoretically reproducible, highly sensitive, and high-throughput label-free detection strategy is proposed for analyzing type B fumonisins in complex sample systems. This study not only enhances our understanding of the physical mechanisms underlying molecular-substrate interactions in the SERS effect but also demonstrates the theoretical feasibility of using SERS for simultaneous quantification of different type B fumonisins in complex sample systems. Moreover, it provides a promising and potentially effective label-free sensing strategy for detecting substances with similar molecular structures.