Metal–insulator–metal metamaterial assisted signal enhanced spectroscopy for sugar concentration prediction in a simulated juice matrix

Weak spectral responses, overlapping bands, and background interference remain key challenges in spectroscopic quantitative analysis. Therefore, this study developed a cross-shaped metal–insulator–metal metamaterial (MIM-MM) platform to enhance Raman, near-infrared (NIR), and visible (VIS) spectroscopy. The optimized MIM-MM consisted of an Au/SiO 2 /Au multilayer structure with periodically arranged cross-shaped Au nanoarrays. With a simulated juice matrix as the validation model system, MIM-MM-enhanced spectra combined with the partial least squares regression (PLSR) model generally exhibit better predictive performance in predicting sugar concentration than unenhanced spectra. In mixed sugar systems, Raman fingerprint (FP) spectra showed the best quantitative analysis ability, with the PLSR model achieving an average RMSE of 0.6330 and an average R 2 of 0.9502. In addition, Raman high wavenumber (HW), NIR, and VIS spectra can provide supplementary information spectral information. Furthermore, the variable importance in projection (VIP) analysis method confirmed that the MIM-MM enhanced key spectral variables related to sugar molecular vibrations and optical responses. Overall, these results indicate that MIM-MM shows potential as a front-end signal enhancement platform for improving weak spectral responses and feature distinguishability in spectroscopic sugar prediction. This study provides a controlled feasibility evaluation of material-assisted spectral analysis in an aqueous multicomponent model system.