Detection of fabricated eggs using Fourier transform infrared (FT-IR) spectroscopy coupled with multivariate classification techniques

Abstract

Fabricated eggs can lead to many severe human health problems due to the consumption of the harmful chemicals used to make them. Therefore, developing technology for detecting fabricated eggs is of high priority in terms of food safety. Fourier transform infrared (FT-IR) spectroscopy is widely used for non-destructive detection applications for its capacity to rapidly assess targets via optical properties. In this study, one-way analysis of variance (ANOVA) and principal component analysis (PCA) were performed to interpret FT-IR spectral data related to the chemical composition of fabricated eggs. As a result, particular wavenumbers were observed that were highly sensitive to the external (eggshell) and internal (albumen and yolk) constituents of fabricated and real chickens eggs. Based on the spectral interpretation, multivariate models using partial least-squares discriminant analysis (PLS-DA), partial least-squares regression (PLSR), support vector machine (SVM), and support vector regression (SVR) were built to detect the constituents of fabricated eggs. The PLS-DA and SVM models classified fabricated eggshells and fabricated albumen with 100% accuracy. Moreover, the PLSR and SVR predictive values exhibited coefficients of determination (Rp2) of 0.99. Hence, this study demonstrates that FT-IR spectroscopy combined with multivariate models can be potentially used as an efficient method to detect both surface and internal constituents of fabricated eggs.