A review on spectral data preprocessing techniques for machine learning and quantitative analysis

Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These perturbations not only significantly degrade measurement accuracy but also impair machine learning-based spectral analysis by introducing artifacts and biasing feature extraction. This review provides a systematic evaluation of critical spectral preprocessing methods-encompassing cosmic ray removal, baseline correction, scattering correction, normalization, filtering and smoothing, spectral derivatives, and advanced techniques like 3D correlation analysis-highlighting their theoretical underpinnings, performance trade-offs, and optimal application scenarios. The field is undergoing a transformative shift driven by three key innovations: context-aware adaptive processing, physics-constrained data fusion, and intelligent spectral enhancement. These cutting-edge approaches enable unprecedented detection sensitivity achieving sub-ppm levels while maintaining >99% classification accuracy, with transformative applications spanning pharmaceutical quality control, environmental monitoring, and remote sensing diagnostics.