Enhancing Raman Spectrum Similarity Matching Through Gradient Transformation

Reliable Raman spectral matching requires robust preprocessing to mitigate baseline distortions and fluorescence interference, particularly in spectra acquired with shorter excitation wavelengths like 532 nm. Traditional baseline correction methods, such as polynomial fitting and Savitzky-Golay filtering, effectively correct baseline distortions but may require careful parameter optimization to avoid introducing artifacts or attenuating subtle spectral features critical for accurate correlation-based comparison. In this study, we present a gradient-based transformation that enhances Raman spectral matching by amplifying sharp features while preserving the overall structural integrity of the spectra. This method leverages curvature analysis through second derivative computation, to emphasize informative peaks. To quantitatively assess performance, we computed Pearson correlation coefficients between baseline-corrected spectra and reference spectra for seven representative substances—Acetone, Ammonium Nitrate, Sulfur, Naphthalene, Sodium Sulfate, HMX, and Barite—under varying lighting and exposure conditions. The gradient-based method consistently yielded the highest correlation values, particularly for low-fluorescence materials such as Sulfur and Sodium Sulfate, demonstrating superior alignment with reference spectra compared to polynomial fitting and Savitzky-Golay filtering. By enhancing the clarity of spectral features and improving correlation with reference spectra, the gradient-based transformation facilitates more accurate automated tasks such as classification, clustering, and spectral library matching. This approach provides a computationally efficient enhancement to traditional preprocessing pipelines, offering robust performance for Raman spectroscopy applications.