Egg freshness is a critical factor that influences the egg’s nutritional value, safety, and overall quality; consequently, it is a priority for both producers and consumers. This review examines the factors that affect egg freshness, and it evaluates both traditional and modern methods for assessing egg freshness. Traditional techniques, such as the Haugh unit test and candling, have long been utilized; however, they have limitations, which are primarily due to their destructive nature. The review also highlights advanced non-destructive methods, including Vis-NIR spectroscopy, ultrasonic testing, machine vision, thermal imaging, hyperspectral imaging, Raman spectroscopy, and NMR/MRI technologies. These techniques offer rapid and accurate assessments while preserving the integrity of the eggs. Despite the current challenges related to calibration and implementation, integrating artificial intelligence (AI) and machine learning with these innovative technologies presents a promising avenue for the improvement of freshness evaluation. This development could revolutionize quality control processes in the egg industry, ensuring consistently high-quality eggs for consumers.

Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192–1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs.

The quality of meat emulsions during mixing is difficult to control because rapid, objective, and non-destructive measurement methods remain limited. This study investigated the effects of lean meat-to-fat ratio and mixing time on moisture content in meat batters using short-wave infrared (1,000-1,700 nm) hyperspectral imaging combined with chemometric modeling. Meat batters were prepared at three lean meat-to-fat ratios: Group A (6:2), Group B (5:3), and Group C (4:4), and mixed for 0, 1, 2, 5, 7, 10, and 15 min. Moisture content differed significantly among formulations, with Group A showing the highest value (63.4%), followed by Group B (57.2%) and Group C (52.4%). Mixing time also had a significant effect, with the highest moisture content observed at 0 min (61.8%) and stabilization at approximately 56–58% after 1–15 min, indicating progressive homogenization. Two-way ANOVA revealed significant main effects of lean meat-to-fat ratio and mixing time (p < 0.001), but no significant interaction. Important wavelengths were identified at 1,000-1,124, 1,190-1,230, near 1,400, and 1,600-1,700 nm, mainly related to water, fat, and protein absorptions. PLSR and Elastic Net emphasized lower water-related wavelengths, whereas RF highlighted mid- and high-wavelength regions associated with fat and protein. Among the tested models, Elastic Net with raw spectra showed the best prediction performance (R²P = 0.76, RMSEP = 2.54%), while the ensemble model also showed strong performance. In addition, hyperspectral imaging visualized moisture distribution and reduced sample heterogeneity with increased mixing time. These results suggest that SWIR hyperspectral imaging is an effective non-destructive tool for predicting moisture content and monitoring mixing uniformity in meat batters. Graphical Abstract