Visible and short-wave near infrared (Vis/SWNIR) spectroscopy combined with chemometrics was investigated for the fast determination of soluble solids content (SSC) and pH values of rice vinegars. Two hundred and twenty-five samples (45 for each variety) were selected randomly for the calibration set, whereas, 75 samples (15 for each variety) for the validation set, and the remaining 25 samples for the independent set. After some preprocessing, partial least squares (PLS) analysis was implemented for calibration models with different wavelength bands including visible, SWNIR and Vis/SWNIR regions. The best PLS models were achieved with Vis/SWNIR (550-1000 nm) region. Furthermore, different latent variables (5, 6, 7, 8 LVs) were used as inputs of least squares-support vector machine (LS-SVM) to develop the LV-LS-SVM models with grid search technique and RBF kernel. The optimal models were obtained with 6 LVs and they outperformed PLS models. Moreover, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and an excellent prediction precision was achieved, and the effectiveness of the EWs was also validated. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.999, 0.189 and 0.051 for SSC, whereas 0.999, 0.008 and -1.7 x 10(-3) for pH, respectively. The overall results indicated that Vis/SWNIR spectroscopy combined with LS-SVM could be applied as a high precision and fast way for the determination of SSC and pH values of rice vinegars.