The potential of visible and near-infrared reflectance spectroscopy (vis-NIRS) was investigated for its ability to nondestructively detect soluble solids contents (SSC) and pH in orange juices. A total of 104 orange juice samples were used for vis-NIRS at 325-1075 nm using a field spectroradiometer. Wavelet packet transform, standard normal variate transformation (SNV), and Savitzky-Golay first-derivative transformation were applied for the preprocessing of spectral data. The chemometrics of partial least-squares (PLS) regression analysis was performed on the processed spectral data. The evaluation of SSC and pH in orange juices by PLS regression with SNV showed the highest accuracy of the three preprocessing methods. The correlation coefficient (r), standard error of prediction, and the root-mean-square error of prediction for SSC were 0.98, 0.68, and 0.73, respectively, whereas those values for pH were 0.96, 0.06, and 0.06, respectively. The "fingerprint" representing features of orange juices or reflecting sensitivity to some elements at a certain band was proposed on the basis of regression coefficients. It is very useful in the field of food chemistry and further research on other materials. It is concluded that the vis-NIRS technique combined with chemometrics is promising for the fast and nondestructive detection of chemical components in orange juices or other materials.