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Water and chlorophyll content were analyzed in tomato leaves by near infrared (NIR) spectroscopy without any previous sample treatment. A total of 120 leaves were collected as experimental materials, a set of 80 samples was used to calibrate the instrument by partial leastsquares regression. In order to get a best model, four different mathematical treatments were used in spectrums processing: offset correction, smoothing, first and second derivative. Different preprocessing of spectra gives different performance of the prediction model; the original spectrums with smoothing treated spectra give the lowest RMSEP value and highest correlation coefficients value ,the best model of chlorophyll content has a root mean square error of prediction (RMSEP) of 8.92 and a calibration correlation coefficient value of 0.96297 and the best model of water content has a root mean square error of prediction (RMSEP) of 2.36 and a calibration correlation coefficient value of 0.99264.