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Abstract: The potential of near infrared (NIR) spectroscopy for non-destructive determination of quality parameters including oil and protein contents in shell-intact cottonseed was investigated. Linear partial least squares (PLS) and nonlinear least-squares support vector machine (LS-SVM) methods were used to develop the calibration models to determinate the protein and oil contents. Moreover, as variable selection techniques, the Monte Carlo uninformative variable elimination (MC-UVE) and the successive projections algorithm (SPA) were applied to improve the predictive ability of the model. Finally, the MC-UVE–LS-SVM models show the best prediction performance. The coefficient of determination (R 2), residual predictive deviation (RPD) and root mean squares error of prediction (RMSEP) were 0.959, 4.871 and 0.977 for protein, and 0.950, 4.429 and 0.834 for oil, respectively. The results indicate that NIR technology could be very useful for the rapid quality analysis of shell-intact cottonseed avoiding the need of grinding. Furthermore, the variable selection of MC-UVE can provide more robust and accurate calibration models than SPA. [Copyright &y& Elsevier]