Abstract: Abstract: Soluble protein is an important indicator for the research of plant physiochemical and resistance physiology. Rapid, accurate and non-destructive detection of soluble protein content in crops is critical for dynamic monitoring of growth state and selecting varieties with strong resistance. 2 soybean varieties, Qihuang35 and Zhonghuang13, were planted and treated with different copper and salt stresses. The near infrared spectra of stressed soybean leaves were obtained by the AOTF (acousto-optic tunable filter) near infrared spectrometer. The soluble protein contents of soybean leaves were measured by coomassie brilliant blue method. Chemometric methods were applied to build multivariate calibration models for the rapid and nondestructive determination of soluble protein content in soybean leaves based on near infrared spectra. Several partial least squares (PLS) models with different preprocessing methods like Savitzky-Golay smoothing (SG), first derivative (1-Der), second derivative (2-Der), standard variable normalization (SNV) and multiplicative scatter correction (MSC) were developed and compared. Then successive projections algorithm (SPA), random frog (RF) and genetic algorithm (GA) were employed to select effective wavelengths with spectral data preprocessed by SG. 11, 10 and 43 of effective wavelengths were selected by SPA, RF and GA respectively. These selected effective wavelengths were used as the inputs of partial least squares (PLS) to develop SPA-PLS, RF-PLS and GA-PLS models. Results showed that the best prediction results for the determination of soluble protein content were achieved by SPA-PLS model using SG spectra with prediction determination coefficient (R2p) of 0.746, root mean squares error of prediction (RMSEP) of 1.894 mg/g and ratio of prediction to deviation (RPD) of 2.061. The overall results indicated that a strong correlation was existed between near infrared spectra and soluble protein content, and near infrared spectroscopy technology combined with SPA-PLS models was a feasible method for the rapid and nondestructive detection of soluble protein content in soybean leaves. This study provides an effective approach for dynamic monitoring of soybean growth state and fast selecting soybean varieties with strong resistance.