Due to the quality differentiation and commercial concerns, rapid authentication and addressing the adulterants in olive oil is of great importance. The feasibility of identifying pure olive oil as well as classifying the binary blends of olive oils according to the adulterants in olive oils using low-field NMR spectroscopy and support vector machine (SVM) have been investigated. Based on the characterization of low-field NMR profiles of six types of vegetable oil and the binary blends of olive oils with three types of seeds oils (corn, soybean, and sunflower seed oils), SVM was employed to build the authentication and classification models. The result indicated that the difference of oils and the type of blends can be monitored by low-field NMR profiles. SVM classification models for identifying pure olive oils from blended ones were developed and an 84.92% classification accuracy was acquired when the adulteration ratio is above 10%. For the classification of binary blends of olive oils according to the seed oils, two SVM classification strategies have been developed and compared, and the SVM model with a suspected range of 10%–30% could provide an acceptable classification result. LF-NMR could be a novel screening method for the authentication of olive oil.

由于质量差异和商业问题，快速鉴定和解决橄榄油中的掺假问题非常重要。研究了使用低场NMR光谱和支持向量机（SVM）根据橄榄油中的掺假物鉴定纯橄榄油以及对橄榄油的二元混合物进行分类的可行性。基于六种植物油以及橄榄油与三种种子油（玉米，大豆和葵花籽油）的二元混合物的低场NMR谱表征，采用SVM建立了认证和分类模型。结果表明，可以通过低场NMR谱监测油的不同和共混物的类型。建立了从混合油中识别纯橄榄油的SVM分类模型，当掺假率大于10％时，分类精度达到84.92％。为了根据种子油对橄榄油的二元混合物进行分类，已开发并比较了两种SVM分类策略，可疑范围为10％–30％的SVM模型可以提供可接受的分类结果。LF-NMR可能是一种新颖的鉴定橄榄油的筛选方法。