• Combining FT-NIR with multivariate algorithms to solve ZSS adulteration issues. • Use characteristic wavelength selection algorithms to optimize FT-NIR raw spectra. • Employ machine learning models to achieve differentiation of ZSS and adulterants. • Based on optimized FT-NIR, use the PLSR model to predict ZSS adulteration ratio.