Crude oil samples are uniquely complex because of the number of compounds present that can only be resolved using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The FT-ICR MS technique has been redefined for examining the composition of crude oil and its products, which has led to a new field called "petroleomics". The chemical composition ultimately determines the chemical and physical properties and the behavior of petroleum and its products. "Petroleomics" predicts the properties and behavior of petroleum using its composition to solve production and processing problems. This paper correlates the chemical composition of crude oil with the total acid number (TAN), which enables the development of prediction models using partial least squares (PLS) and support vector machines (SVMs) as alternative multivariate calibration methods that allow for the application of FT-ICR MS analysis in direct measurements The prediction models using PLS and SVM demonstrated low prediction errors and superior performance in relation to the univariate method. These results support the development of robust models to predict crude oil properties based on the vast quantity of information provided by FT-ICR MS using PLS and SVM as multivariate calibration procedures.