With current advances in the nuclear magnetic resonance (NMR) instrumentation, miniaturized cryogen-free low field spectrometers denote a technological breakup. This analytical strategy is utilized in wide range of scientific areas and provide an excellent approach to obtain data related for the structural characterization and fraud implementations in valuable edible oils. Herein, we aimed to study the feasibility and efficiency of cryogen-free benchtop NMR spectroscopy (60 MHz) with chemometrics for the detection of cold pressed black cumin (Nigella sativa L.) seed oil (BCSO) adulteration. This very simple and effortless procedure based on the glycerol backbone signals of triglycerides was highly adequate to detect adulteration qualitatively and quantitatively. The most popular chemometrics models of principle component analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy analysis (SIMCA) and linear discriminant analysis (LDA) were built over the integrated data of NMR spectroscopy recorded from different oil samples. The partial least squares-regression (PLS-R) analysis models were also constructed to detect quantitative detection limits for the cheap refined cottonseed oils (CSOs) and sunflower oils (SFOs) in adulterated mixture sets (n = 144). The samples were acceptably classified in their own types with an accuracy of 100% by the SIMCA and LDA model. The PLS-R results revealed that the detection limits of adulterant were 0.03% for BCSO-CSO (R2 = 0.9999%) and 0.13% for BCSO-SFO binary mixtures (R2 = 0.9999%), respectively. Consequently, the low-field ¹H-NMR spectroscopy allied with multivariate data analyses is expected, in the coming years, to become even more powerful analytical application for the detection of food frauds.