In the present paper, the over-fitting phenomenon in building PLS model using orthogonal signal correction (OSC) was studied through establishment of quantitative calibration models for the peanut oil content in blending edible oils, and for the dimethylsulfoxide concentration in water solution. The cross validation results and the predication results of PLS models using OSC and without using OSC were compared to evaluate the effectiveness of OSC for improving the performance of PLS1 model. The results show that the application of OSC to PLS modeling will lead to an over-fitting phenomenon. According to the principles of their algorithms, when OSC and PLS are used together, the signals which are not correlated to the interested property are removed twice from the raw spectra. This leads to deleting the parts of useful information in spectra, and to spoiling the predictive ability of PLS models to some extent.