• • Sequential IR-Raman detection achieved complete identification of mixed explosives: IR identified organic components (m-dinitrobenzene, p-nitroaniline) but missed inorganic oxidizers (KNO3, NH4NO3); Raman confirmed both, yielding 100% component coverage.
• • CNN models delivered high classification accuracy: 96.54% for IR and 96.29% for Raman spectra, with per-sample inference times of 0.044 s and 0.042 s, respectively, enabling real-time field screening.
• • The 1D-CNN preprocessing model effectively denoised raw spectra while preserving key feature peaks, standardizing inputs for the 2D-CNN classifier and reducing reliance on manual spectral interpretation.
• • The method demonstrated robustness across powder and tablet forms, with consistent spectral profiles despite minor peak shifts and intensity variations, confirming its practical utility for varied sample morphologies.