In the present paper,a new model-based method was proposed for temperature prediction and correction.First,a temperature prediction model was obtained from training samples;then,the temperature of test samples were predicted;and finally,the correction model was used to reduce the nonlinear effects of spectra from temperature variations.Two experiments were used to verify the proposed method,including a water-ethanol mixture experiment and a ternary mixture experiment.The results show that,compared with classic method such as continuous piecewise direct standardization(CPDS),our method is efficient for temperature correction.Furthermore,the temperatures of test samples are not necessary in the proposed method,making it easier to use in real applications.