The authors studied the temperature influence on short-wave near-infrared spectra of ethanol aquatic solution and utilized four methods to establish the transfer partial least squares (PLS) calibration model: direct transfer calibration, global calibration, orthogonal signal correction (OSC) and generalized least squares weighting (GLSW). The PLS models were built at four temperatures: 15, 25, 35 and 40 ℃. The results showed that direct calibration provided high prediction bias: significantly high positive prediction bias for a temperature lower than calibration temperature and negative bias for higher temperatures. By using the global correction, OSC and GLSW, the systematic errors could be reduced. However, the global correction needed more calibration samples and built a more complex model. The OSC and GLSW methods provided better predictions using fewer latent variables. By using the GLSW method, prediction bias less than 0.1% and RMSEP less than 0.9% were obtained. The absolute prediction error of GLSW method was less than 1.5%. Additionally, the GLSW provided smaller prediction error at every researched temperature using fewer latent variables than OSC. Thus, GLSW was superior to OSC and could establish more robust transfer calibration model.