Abstract: In order to solve the problem of spectral inconsistency of the same leaf sample collected by different NIR spectroscopies and to improve the transfer effects of NIR prediction models for chemical component analyses in tobacco, a Q-factor spectral transformation (QFST) method was established. According to the principle of inter-class separability, QFST classified many original variables into several comprehensive factors, which were used to reorganize the spectral matrixes. The QFST method derived the transformation relationship matrix by a generalized inverse matrix to realize the spectral transfer between the master and slave instruments. The QFST method was compared with the spectral space transformation (SST) and piecewise direct standard (PDS) methods, which were the common model transfer methods. The results showed that: 1) For 70 different chemical indexes of tobacco, the prediction accuracies of the QFST and SST methods showed no major difference, and the overall prediction effects of both methods were better than the PDS method. 2) The R² of conventional chemical components such as total plant alkaloids, reducing sugars, total sugars, total nitrogen and chlorine were all above 0.9 after using the three model transfer methods. For amino acids and Amadori compounds, which are present at lower mass fractions in tobacco, the QFST method was better than the SST method in model transfer prediction.