An Advanced Chemometric Model Applied to Ratiometric Surface-Enhanced Raman Scattering for Monitoring Blood Glucose

Abstract

An advanced chemometric model based on ratiometric surface-enhanced Raman scattering (SERS) was developed for the quantification of glucose in serum samples. In the absence of glucose, it was mainly the SERS signal of silver nanoparticles (AgNPs)-o-phenylenediamine (OPD). When glucose was added, glucose oxidase catalyzed glucose to produce hydrogen peroxide (H₂O₂), which oxidized OPD to produce 2,3-diaminophenazine (DAP) in the presence of AgNPs. The generated DAP exhibited a new strong SERS signal and changed the Raman peak ratio between DAP and OPD. Without using any internal standard, the advanced chemometric model mitigated the fluctuations in SERS intensity and achieved accurate concentration predictions for glucose in serum samples with recoveries in the ranges of 92.8–104.8%. The accuracy of the quantitative results obtained using the proposed method is comparable with that of the reference method — glucometer. The proposed sensor showed high sensitivity and selectivity in detecting glucose with a limit of detection (LOD) of 0.28 μM. Additionally, the presented SERS sensor demonstrated great promise in determining H₂O₂-related metabolites in real serum samples.