The near-infrared spectroscopy(NIRS)with GA-BP neural network model was used for rapid prediction of gross calorific value of coal.The prediction model was non-linear

so the classical linear principal component analysis(LPCA)method was not applicable for processing spectral data.The nonlinear polynomial kernel principal component analysis(P-KPCA)method was proposed for extracting spectral feature and filtering abnormal samples in this paper.The extracted principal components had high feature concentration

obvious dimension reduction effect

and good correlation with output variable.After eliminating the abnormal spectra

prediction accuracy was greatly improved.The results showed that P-KPCA provided effectively processed spectral data for the rapid prediction model.