A novel approach to discriminate transgenic from non-transgenic soybean oil using FT-MIR and chemometrics. (January 2015)

• Record Type: Journal Article
• Authors: Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S.A.; Ferré, Joan; Boqué, Ricard
• Is Part Of: Food research international. Volume 67(2015)
• Page Start: 206
• Page End: 211
• Publication Date: 2015-01
• Subjects: Soybean oil -- FT-MIR -- Discrimination -- Transgenic food; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2014.11.011
• Languages: English
• ISSNs: 0963-9969

Abstract

Abstract: A methodology was developed to distinguish transgenic from non-transgenic soybean oils samples by using FT–MIR spectroscopy coupled with discrimination techniques, including Soft Independent Modeling of Class Analogies (SIMCA), Support Vector Machine–Discriminant Analysis (SVM-DA) and Partial Least Squares–Discriminant Analysis (PLS-DA). The discrimination success rate of these three methods was compared, and different types of preprocessing were investigated. Based on the results, the best option was PLS-DA with a 100% rate of discrimination, independent of the preprocessing method used. Highlights: FT-MIR-coupled with PLS-DA were used to discriminate soybean oil samples. Results show that FT-MIR coupled PLS-DA can provide reliable results. Compared to traditional method (PCR), this methodology is fast and reliable.