Double pulse laser-induced breakdown spectroscopy(DP-LIBS) was used to detect lead(Pb) content in soybean oil.For nine prepared soybean oil samples in the experiment,cylindrical paulownia wood with a certain size was used to enrich Pb in samples,and LIBS spectrum was collected by an Ava-Spec two-channel high precision spectrometer.According to LIBS spectrum of the sample and the National Institute of Standards and Technology(NIST) atomic spectrum database,the characteristic spectral line intensties of Ca Ⅱ 393.284 nm,Ca Ⅱ 396.752 nm,N Ⅱ 399.399 nm and Pb Ⅰ 405.685 nm were selected as independent variables,and multivariate linear regression was used to develop a quantitative analysis model for Pb content.The methods of variance analysis and t-test were used to verify the feasibility of analysis model.The results indicated that the average relative error of Pb was about 16%,and the fitting degree R2 was 0.981 8 in the direct calibration method.While in the multiple linear regression model,the average relative error of Pb was 7.25%,and the fitting degree R2 was 0.997 1,the relative errors of three validated samples were within a reasonable range.It is showed that the multivariate calibration analysis model could make full use of the effective information in the spectrum and reduce the influence of matrix effect,thereby improving the accuracy of LIBS analysis.