To explore the influences of different spectral preprocessing methods on the terahertz (THz) spectrum, preprocessing methods such as smoothing, multivariate scattering correction, baseline correction and normalization, and combination of multiple scattering correction and normalization are used. In order to optimize the model and reduce the computation amount, principle component analysis is used to compress the THz spectrum to reduce data dimension. The backpropagation neural network (BPNN) and generalized regression neural network (GRNN) detection models are established based on the compressed data. Experimental results show that the effect of the GRNN model with multiple scattering correction and normalization correction is the best. The predicted correlation coefficient is 0.9967 and the predicted root mean square error is 0.0050. This experiment verifies the feasibility of the THz spectrum detection technology for the detection of the melamine in milk powder, and establishes a better GRNN detection model for melamine adulterated milk powder samples. This study is of great significance to promote the healthy development of milk powder industry.