The prediction accuracy of calibration models for near-infrared (NIR) spectroscopy typically relies on the morphology and homogeneity of the samples. To achieve non-homogeneous tobacco samples for non-destructive and rapid analysis, a method that can predict tobacco filament samples using reliable models based on the corresponding tobacco powder is proposed here. First, as it is necessary to establish a simple and robust calibrated model with excellent performance, based on full-wavelength PLSR (Full-PLSR), the key feature variables were screened by three methods, namely competitive adaptive reweighted sampling (CARS), variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV), and variable combination population analysis-genetic algorithm (VCPA-GA). The partial least squares regression (PLSR) models for predicting the total sugar content in tobacco were established based on three optimal wavelength sets and named CARS-PLSR, VCPA-IRIV-PLSR and VCPA-GA-PLSR, respectively. Subsequently, they were combined with different calibration transfer algorithms, including calibration transfer based on canonical correlation analysis (CTCCA), slope/bias correction (S/B) and non-supervised parameter-free framework for calibration enhancement (NS-PFCE), to evaluate the best prediction model for the tobacco filament samples. Compared with the previous two transfer algorithms, NS-PFCE performed the best under various wavelength conditions. The prediction results indicated that the most successful approach for predicting the tobacco filament samples was achieved by VCPA-IRIV-PLSR when coupled with the NS-PFCE method, which obtained the highest determination coefficient (Rp2 = 0.9340) and the lowest root mean square error of the prediction set (RMSEP = 0.8425). VCPA-IRIV simplifies the calibration model and improves the efficiency of model transfer (31 variables). Furthermore, it pledges the prediction accuracy of the tobacco filament samples when combined with NS-PFCE. In summary, calibration transfer based on optimized feature variables can eliminate prediction errors caused by sample morphological differences and proves to be a more beneficial method for online application in the tobacco industry.