A portable laser Raman spectrometer was used to scan 208 adulterated flaxseed oil (FSO) samples of extra virgin olive oil (EVOO). Combined with principal component analysis (PCA) and partial least squares regression (PLSR), the quantitative detection of EVOO adulterated flaxseed oil was realized. Partial least squares regression (PLSR) calibration models were developed and validated internally (using cross validation, leave-one-out method) and externally (using independent sample sets). The correlation coefficient (R2), root mean square error (RMSE) and relative analysis error (RPD) were used to evaluate the prediction performance of the system. The experimental results show that the root mean square error of the prediction system is 2.31% and the correlation coefficient of the prediction set is 0.995. The root mean square error of cross validation is 2.24%, the correlation coefficient of cross validation set is 0.995, and the relative analysis error of the system is 14.19. The results show that the system has excellent predictive performance in quantitative detection of EVOO adulterated flaxseed oil. The system can be used for simple, rapid, and nondestructive quantitative detection of EVOO adulteration.