Smart Decimation Algorithm for Efficient Spectroscopic Data Reduction: Methodology and Raman Case Study

Abstract

Spectroscopic techniques such as Raman, UV–Vis, and FTIR generate large datasets that challenge storage, processing, and real-time analysis. Conventional reduction strategies, particularly uniform 1-of-N decimation, often distort narrow peaks and compromise            chemically relevant information. In this study, we present and evaluate a Smart  Decimation framework for spectroscopic data reduction, integrating Interest Level Analysis (ILA) with an adaptive Sample Selection Module (SSM) to allocate sampling density         proportionally to spectral variability. Using a synthetic Raman-like spectrum with five  representative vibrational peaks, Smart Decimation reduced the dataset size by ~80% (from 5000 to ~1000 points) while preserving spectral fidelity. Compared with uniform decimation,  cross-correlation improved from 0.91 to 0.98, mean peak position error decreased from ±3.0 cm⁻¹ to ±0.5 cm⁻¹, and intensity error was reduced from 12% to 2%. Validation  with experimental Raman spectra of TiO₂ confirmed that Smart Decimation consistently preserved sharp vibrational bands with minimal distortion. Additional benchmarking against PCA-based and wavelet compression methods demonstrated that Smart Decimation achieved the best balance of accuracy, simplicity, and real-time applicability. These results establish Smart Decimation as a scalable and practical solution for modern spectroscopic workflows, with potential applications in portable Raman spectrometers, pharmaceutical quality control, biomedical diagnostics, and large-scale spectral libraries.