NMR spectroscopy in conjugation with multivariate statistical analysis for distinguishing plant origin of edible oils

Abstract

NMR spectroscopy in combination with multivariate statistical analysis provides the promising feasibility for discriminating food origins. In this study, this technique was successfully employed to analyze various refined edible oils from seven plant-origins including sunflower seed, rapeseed, sesame, soybean, peanut, corn and olive oils. A variety of fatty acids in edible oils including oleic acid, linoleic acid, linolenic acid, saturated fatty acids and the corresponding iodine values were quantified via a single NMR measurement, and principal component analysis was used to distinguish their plant origins. The compositional differences both in major and minor components were derived from the pair-wise orthogonal partial least squares discriminant analysis models between the other edible oils and olive oil with highest nutritional value and favorable flavor. The results suggested that olive oil and rapeseed oil seem to provide the more nutritional benefit to human, and the minor components such as terpenes and tyrosol also induce health benefits and desirable flavor. The further analysis of different-producing-region sesame oils indicated that the altitude gives the great contribution to their compositional differences, and the climatic conditions also cause the compositional variations.