In order to improve the detecting precision and robustness in determination of the content of nicotine in tobacco by near infrared spectroscopy, a predictive model is established by genetic algorithm combined least square support vector regression (GA-LSSVR). The practical use of support vector machine is limited because of its set of parameters to be defined by the user. For this reason, a genetic algorithm is utilized to approach LSSVR parameter optimization in the calibration model. To highlight the superiority of GA-LSSVR algorithm, it is compared with traditional LSSVR and genetic algorithm-partial least square. The correlation coefficient R2 and root mean square error of prediction (RMSEP) for the test set are used as evaluation parameters for the model. The optimal model is obtained with R2 of 0.9766 and RMSEP of 0.1065. Generally, in the context of performance and robustness, the results demonstrate that GA-LSSVR is a good method for the analysis and modelling of near infrared data.