A novel approach to discriminate transgenic from non-transgenic soybean oil using FT-MIR and chemometrics

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 SquaresDiscriminant 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.

Detection of Irradiated Food and Evaluation of the Given Dose by Electron Spin Resonance, Thermoluminescence, and Gas Chromatographic/Mass Spectrometric Analysis

“…Still regarding food, PCA was helpful to discriminate Coffea arabica (Moreira and Scarminio, 2013), extra virgin olive oils in relation to geographical origin (Melucci et al, 2016), in discrimination of different cheeses according to their fatty acid profiles and phytosterol contents (Kim et al, 2014), to investigate adulteration in olive oil (Mildner-Szkudlarz and Jeleń, 2008), in the discrimination between transgenic and nontransgenic soya oil (Luna et al, 2013b;Luna et al, 2015), in vinegar analysis (Geng et al, 2013;Casale et al, 2006), to characterize textural properties in meat products (Probola and Zander, 2007), in the evaluating growth models of Pseudomonas spp. in seasoned prepared chicken stored at different temperatures (Li et al, 2014), in the evaluation of green tea (Iorgulescu et al, 2016), besides being applied to the sensory evaluation of desserts prepared with egg products processed by freeze and spray drying (Jesús et al, 2013), among a lot of other examples.…”

Portable NIR Spectrometer for Prediction of Palm Oil Acidity

“…Although there are several methods available, there is no standard procedure to decide the best pre‐processing technique according to the equipment used for spectra acquisition and samples investigated, requiring a trial and error approach for specific applications. Unsupervised classification models such as principal component analysis (PCA), and supervised classification models such as k‐nearest neighbors (k‐NN), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA), were used to discriminate transgenic from non‐transgenic soybean oil for their quality parameters using FT–MIR spectroscopy (Luna, da Silva, Pinho, Ferré, & Boqué, ).…”

Evaluation of long-heating kinetic process of edible oils using ATR–FTIR and chemometrics tools

“…Although lots of reports have been published on the coupling the infrared spectroscopy with chemometrics for analysis of oils (Moros et al 2009;Le Dréau et al 2009a;Karoui et al 2010;Pinto et al 2010;Javidnia et al 2013;Luna et al 2015), limited researches have been done with IR on the heating oxidative products of edible oils (Le Dréau et al 2009a). Le Dréau et al heated some edible oils for 3 h at 130°C and the presence of four components in the oxidation process of the oils was proposed in their work with the aid of MCR-ALS as a chemometrics treatment (Le Dréau et al 2009a).…”