Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study

• Main Authors: Florina-Dorina Covaciu, Camelia Berghian-Grosan, Ioana Feher, Dana Alina Magdas
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Applied Sciences
• Subjects: oils; FAMEs; GC-FID; Raman spectroscopy; linear discriminant analysis (LDA); artificial neural network (ANN)
• Online Access: https://www.mdpi.com/2076-3417/10/23/8347

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil.