Abstract
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 asexperimental materials, a set of 80 samples was used to calibrate the instrument by partial leastsquaresregression. In order to get a best model, four different mathematical treatments were used inspectrums processing: offset correction, smoothing, first and second derivative. Differentpreprocessing of spectra gives different performance of the prediction model; the original spectrumswith smoothing treated spectra give the lowest RMSEP value and highest correlation coefficientsvalue ,the best model of chlorophyll content has a root mean square error of prediction (RMSEP) of8.92 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 coefficientvalue of 0.99264.
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