Faced with the aggravating issue of meat fraud caused by the addition of low-cost animal blood food, the present work aims to develop FT-NIR-based tracing models for detecting beef adulterated with pig blood. A total of 210 samples were analyzed, including raw beef, beef adulterated with pig blood-based gel, and pure pig blood-based gel prepared. For spectrum denoising, the first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction algorithms were performed and compared. We built, optimized, and compared partial least squares (PLS), support vector machine (SVM), and extreme learning machine (ELM) models for identifying the adulterated beef and predicting adulteration levels. Results indicated that second derivative was the best preprocessed technique for all chemometrics modeling; ELM model achieved the optimal performance when the sin function was used, achieving 100% accuracy for identifying the adulterated beef, and all root mean square errors were less than 0.16% for predicting adulteration levels in training and test sets. These results suggest the optimal ELM models could be employed for rapidly checking beef adulterated with pig blood-based gel using FT-NIR technology.