Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods

Abstract

Sesame oil (SO) is a high-quality oil that is more expensive than other edible oils, and therefore becomes a target of economically motivated adulteration. An approach based on excitation-emission matrix (EEM) fluorescence and chemometric methods was applied for the rapid classification and determination of the authenticity of SO. First, a five-factor alternating trilinear decomposition (ATLD) model roughly completed the characterization of the fluorescent components in the edible oil samples, providing meaningful chemical information. Then, four chemometric methods, including linear discriminant analysis (LDA), partial least squares–discriminant analysis (PLS-DA), support vector machine (SVM) and unfolded partial least-squares discriminant analysis (UPLS-DA), were used to establish the models for the classification of SO and other edible oils (Model 1), and determine the authenticity of SO and adulterated SOs (Model 2). All models achieved good classification results. The combination of the second-order calibration algorithm (ATLD) and the pattern recognition algorithm (LDA, PLS-DA, or SVM) not only achieved the characterization of the components in edible oils but also realized the rapid detection of adulterated SOs. The proposed method is rapid, accurate, requires a simple sample pre-treatment and can be used to determine the authenticity and adulteration of high-quality edible oils.