Detection of counterfeit sesame oil based on Raman spectroscopy and chemometric analysis

Abstract

Sesame oil is popular in China and many other Asian countries due to its pleasant flavor and high nutritional value. Sesame oil essence is often illegally added to other vegetable oils to counterfeit sesame oil. This study aimed to propose a rapid and cost-effective method, which could help to identify the sesame oil and counterfeit sesame oil. For this purpose, the counterfeit samples were prepared by adding essence to inferior oils (soybean oil, sunflower oil, canola oil, corn oil), all oil samples were examined and their Raman spectra served as data for the construction of the soft independent modeling of class analogy (SIMCA) models and the important variables for distinguishing between authentic and counterfeit sesame oil were selected by orthogonal partial least squares discriminant analysis. The results showed that SIMCA is a strong chemometric technique for classification of authentic sesame oils and the types of counterfeit sesame oil samples based on Raman spectra, and a total 100% correct classification rate of sesame oil could be achieved. The methods allowed the identified counterfeit sesame oil in commercial sesame oil samples, opening the door to further research into the counterfeit detection of other liquid foods.