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To improve and simplify the prediction model of chlorophylls content of cucumber leaves, synergy interval partial least squares (SiPLS) and net analyte signal (NAS) were combined to search for optimized informative spectral wavelengths about chlorophylls content from NIR spectra of cucumber leaves, then spectral model was developed on the basis of chlorophylls contents. One hundred and ten cucumber leaves were selected to collect NIR spectra and chlorophylls content according to chemical analysis. The spectra were preprocessed by SNV method and divided into 29 intervals, among which 4 subsets, i.e. No 3, 4, 5, 15 were selected by SiPLS. Then NAS was used to characterize the net signals of chlorophyll from cucumber leaf spectra which were used for regression variables of NAS model. The NAS calibration model was obtained with the correlation coefficient Rc of 0.9472, root mean square error of calibration of 0.0795mg/g , the prediction coefficient Rp of 0.9250 and root mean square error of prediction of 0.0906mg/g. It proves that SiPLSNAS could determine optimal variables in NIR spectra and improve the accuracy of model. 