Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools. (1st February 2019)

• Record Type: Journal Article
• Authors: de Oliveira Mendes, Thiago; Porto, Brenda Lee Simas; Almeida, Mariana Ramos; Fantini, Cristiano; Sena, Marcelo Martins
• Is Part Of: Food chemistry. Volume 273(2019)
• Page Start: 144
• Page End: 150
• Publication Date: 2019-02-01
• Subjects: Food quality control -- Egg -- Raman spectra -- Lipids -- Supervised classification -- Chemometrics; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2017.12.084
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Differentiation of conventional and ω3 enriched eggs by Raman spectra and PLS-DA. Fatty acids profiles were analyzed by gas chromatography. PLS-DA model discriminated among samples with almost 100% of success rate. Raman modes that most contributed to the discrimination were identified. Uncertainty estimation of the predicted values by bootstrap resampling. Abstract: This work developed an analytical method to differentiate conventional and omega-3 fat acids enriched eggs by Raman spectroscopy and multivariate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA). Forty samples of enriched eggs and forty samples of different types of common eggs from different batches were used to build the model. Firstly, gas chromatography was employed to analyze fatty acid profiles in egg samples. Raman spectra of the yolk extracts were recorded in the range from 3100 to 990 cm −1 . PLS-DA model was able to correctly classify samples with nearly 100% success rate. This model was validated estimating appropriate figures of merit. Predictions uncertainties were also estimated by bootstrap resampling. The most discriminant Raman modes were identified based on VIP (variables importance in projection) scores. This method has potential to assist food industries and regulatory agencies for food quality control, allowing detecting frauds and enabling faster and reliable analyzes.