In order to monitor chromium (Cr) content in soybean oil, laser induced breakdown spectroscopy (LIBS) was used to detect Cr content in this research. Pine wood chips was used to enrich heavy metal of Cr, and the spectra of pine wood chips were acquired in the wavelength range of 206.28~481.77 nm by a two-channel high-precision spectrometer. Then, uninformative variable elimination (UVE) method was used to select sensitive wavelength variables for heavy metal of Cr, and calibration model of Cr in soybean oil was developed with partial least squares (PLS) regression, the performance of the calibration model was compared to univariate and full PLS calibration models. The results indicate that the performance of UVE-PLS calibration model is better than that of univariate and full PLS calibration models, the correlation coefficient, root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) are 0.990, 0.045 mg·g-1, 0.050 mg·g-1 and 0.054 mg·g-1, respectively. After UVE variable selection, the number of wavelength variables in UVE-PLS calibration model is about 2% of wavelength variables in full PLS calibration model. This means UVE is an effective variable selection method which can select correlative variables for heavy metal of Cr.