Detection of Sesame Oil Adulteration Using Low-Field Nuclear Magnetic Resonance and Chemometrics

Abstract

Abstract Identification of edible oil adulteration is an essential task for oil quality control. In this study, the adulteration of sesame oil samples with soybean oil was detected by low-field nuclear magnetic resonance (LF-NMR) combining with chemometrics including principal component analysis (PCA), partial least squares (PLS) and principal component regression (PCR) methods. PCA analysis was applied for the classification of various adulteration ratios of sesame oil samples. PLS and PCR were used for the detection of adulteration ratios of sesame oil samples. A calibration data set was established and evaluated by a cross-validation method. The correlation coefficients between actual and predicted values for the calibration and validation data sets were 0.953 and 0.973 for PLS and 0.975 and 0.979 for PCR, respectively. Therefore, a combination of LF-NMR with chemometrics can be applied for detecting sesame oil adulteration in a fast and nondestructive manner.