Unlocking the Full Potential of SERS: Merging Direct and Indirect Approaches for Enhanced Analysis of Multiplex Plasticizer Analogs in Matrices

Rapid and accurate identification of harmful plasticizer analogs in their native matrix is crucial for contaminant monitoring across industries. Surface-enhanced Raman scattering (SERS) shows promise for detecting structurally similar analogs but faces challenges like subtle receptor signal changes and distortion with weakly adsorbing plasticizer analytes. We address these limitations by integrating direct and indirect SERS to capture intrinsic Raman signals and receptor-analyte interactions, achieving 100% classification accuracy eight plasticizer analogs and multiplex quantification of three major plasticizers extracted from canola oil with < 5% predictive errors at a limit of detection (LOD) of 0.01 mg L⁻¹, despite interference from other lipophilic contaminants such as adipates. Experimental SERS data and interaction energy calculations affirm that direct SERS detects alkyl chains, while indirect SERS identifies aromatic rings and carboxyl groups in eight plasticizer analogs with varying alkyl structures. A hybrid direct-indirect superspectrum was constructed, enabling the differentiation of eight analogs via Partial-Least-Squares Discriminant Analysis (PLS-DA) and further analysis using SHapley Additive exPlanations (SHAP) reveals equal contribution from all platforms, which highlights their complementary roles. These findings pave the way for broader applications in rapid, on-site detection of complex contaminants across industries.