Near infrared (NIR) spectroscopy was applied for the fast and nondestructive determination of malondialdehyde (MDA) content in oilseed rape leaves.A total of 90 leaf samples were collected, the calibration set was composed of 60 samples, and the prediction set was composed of 30 samples.Different preprocessing methods were used before the calibration stage, including smoothing, standard normal variate, first and second derivative, and detrending.Then partial least squares (PLS) models were developed for the prediction of MDA content in oilseed rape leaves.The latent variables selected by PLS and effective wavelengths selected by successive projections algorithm (SPA) were used as the inputs of least square-support vector machine (LS-SVM) to develop LV-LS-SVM and SPA-LS-SVM models.The correlation coefficients (r) and root mean square error of prediction (RMSEP) were used as the model evaluation indices.Excellent results were achieved by LV-LS-SVM model, and the prediction results by LS-SVM model using detrending spectra were r=0.999 9 and RMSEP=0.530 2, and those by LS-SVM model using 2-Der spectra were r=0.999 9 and RMSEP=0.395 7.The results showed that NIR spectroscopy could be used for determination of MDA content in oilseed rape leaves, and an excellent prediction precision was achieved.This study supplied a new approach to the dynamic and continuous field monitoring of growing status of oilseed rape.