In the present paper, the authors used NIR to determine routine chemical components, namely total sugar, reducing sugar, nicotine, total nitrogen, starch and volatile alkali. Orthogonal signal correction (OSC) was employed as spectral pretreatment and the principal component regression (PCR) models for 6 chemical components were established with Monte Carlo cross-validation modeling strategy. RPD value for each model was calculated to evaluate the methods. The orientation of PCR projection is the largest variance direction and has no relationship with the concentration. OSC can not only get rid of uninformative concentration but also solve the problem of noise, baseline drift and stray light. Compared with conventional PCR, OSC-PCR sustains the accuracy of the predicting model and improves the stability of the model significantly. It proves that NIR coupled with OSC-PCR method can be applied to the determination of routine chemical components, which is of great significance in evaluation of tobacco quality and analysis of tobacco aroma components.