In order to reduce the time and cost for near infrared (NIR) model development and maintenance, the transfer of NIR spectra measured on four different portable spectrometers (one master and three target instruments) for predicting the lignin content of pulp wood is investigated in this work. Eighty-two wood samples were prepared by chipping and grinding, and their NIR spectra were recorded with four spectrometers. Calibration models for the determination of lignin in pulp wood have been developed by partial least squares (PLS) regression, while average Mahalanobis distances (AMD) and average differences of spectra (ADS) were used to quantify the spectral differences. Then piecewise direct standardization (PDS) has been applied, and compared to direct standardization (DS), slope/bias correction (SBC) and canonical correlation analysis (CCA). The accuracy of the models has been evaluated by comparing their prediction performance. The results indicated that the prediction performances of the three target instruments are greatly improved by using the three algorithms. The advantage of the PDS algorithm is that fewer samples are required for the transfer sets, which means lower model maintenance cost for practical applications. When it comes to window size setting procedure, it was found that if there are large spectral differences between the master and the target spectrometer, a large window size should be used and if the spectral difference is a significant lateral shift, an asymmetric window with appropriate window size is necessary to ensure a good transfer performance for the PDS algorithm.