This research aims to examine fat from various vegetable oils using n-alkane profiles, as well as chemometrics and machine learning. Unsaponifiable vegetable oils (coconut, peanut, palm and soybean oils) were separated and analysed using gas chromatography-mass spectrometry (GC-MS) to investigate the n-alkane profiles of each fat. The authenticity of the detected n-alkane profiles was determined by comparing to the retention time of C₇-C₄₀ n-alkane standards. The test designs were developed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Random Forest (RF). Both PCA and HCA appeared to provide a clear distinction between each of the vegetable oil tests. Based on the PLS-DA and RF determination, tetracosane (C₂₄) and octadecane (C₁₈) are proposed as the key n-alkane markers for separating lard from vegetable oils. These findings suggest that additional work may be required to achieve and determine the different characteristics across oils in numerous statistical applications, notably chemometrics and machine learning.