Gaining Quantitative Fidelity from Raman Spectra in Regimes of Large and Varying Fluorescence

Raman spectroscopy is attractive for probing complex mixtures, but in many real samples strong fluorescence overwhelms the Raman bands needed for quantitative analysis. This work asks a practical question: under large and varying fluorescence, is it better to invest in hardware-based shifted excitation Raman difference spectroscopy (SERDS) or in preprocessing of conventional Raman spectra? We construct a simulation framework that generates more than 12 million spectra of benzophenone-alanine mixtures embedded in fluorescent matrices. Six datasets emulate realistic fluorescence behaviors, including constant backgrounds, photobleaching, random intensity fluctuations, and changes in fluorescence shape. For each scenario we form paired libraries of conventional Raman and SERDS spectra and build partial least squares regression models on (i) raw spectra containing fluorescence and (ii) spectra after asymmetric least squares or discrete wavelet transform background removal and normalization. Across most cases with stable or smoothly varying fluorescence, conventional Raman combined with suitable preprocessing matches or modestly exceeds SERDS in predicting mixture composition. SERDS provides a clear advantage only when fluorescence intensity or spectral shape fluctuates strongly and in an uncorrelated fashion between measurements, and even then, depends on closely matched sampling volumes at the two excitation wavelengths. These results show that visually cleaner SERDS spectra do not automatically yield more accurate models. Instead, the optimal strategy depends on fluorescence statistics and the available preprocessing pipeline. The simulation framework and decision rules developed here offer practical guidance for designing Raman measurements in fluorescence-rich environments such as soils and other heterogeneous natural materials.