Assessment of integrated freshness index of different varieties of eggs using the visible and near-infrared spectroscopy

This study aimed to determine the integrated freshness index (IFI) of eggs using Vis-NIR spectroscopy and optimized support vector regression, which gave the first insight into the freshness quality of eggs from the biochemical essence of quality changes. In this work, Vis-NIR transmission spectra of brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm. Standard normal variables (SNV) were used to normalize the spectral data, and the Shuffled Frog Leaping Algorithm (SFLA) and Competitive Adaptive Reweighted Sampling (CARS) were used to choose the optimal wavelengths. The quantitative analysis model of IFI was developed using a support vector regression (SVR) that was optimized using Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After conducting a comparative analysis, it was determined that the GA-SVR model based on 63 wavelengths screened by the SFLA best predicted IFI with a training set coefficient of determination (R-c (2)) of 0.900, root means square error (RMSEC) of 0.005, a prediction set coefficient of determination (R-p (2)) of 0.816, root mean square error (RMSEP) of 0.012 and relative analysis error (RPD) of 2.077. The results demonstrate that the model can be used to simultaneously perform nondestructive detection of two distinct egg IFI variants, suggesting broader applicability and enhanced model reliability.

Automated summary

This study used Vis-NIR spectroscopy and optimized support vector regression to determine the integrated freshness index (IFI) of eggs, providing insights into the freshness quality of eggs from the biochemical essence of quality changes. Brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm using Vis-NIR transmission spectra. The model developed using GA-SVR based on 63 wavelengths selected by SFLA showed the best prediction performance for IFI.