Prediction of egg freshness during storage using electronic nose

Area of Science:

• Food Science
• Analytical Chemistry
• Sensory Science

Background:

• Egg quality and freshness are critical for consumer satisfaction and food safety.
• Traditional methods for assessing egg freshness can be time-consuming and subjective.
• Objective and rapid methods are needed to monitor egg quality during storage and distribution.

Purpose of the Study:

• To evaluate a fast gas chromatography electronic nose (GC e-nose) for discriminating hen egg freshness.
• To predict storage time, sensory attributes, and internal quality changes using GC e-nose data.
• To assess the feasibility of using GC e-nose for quality control in the egg supply chain.

Main Methods:

• Hen eggs were stored at 20°C for 20 days with sampling at multiple time points.
• Gas chromatography electronic nose (GC e-nose) was used to analyze volatile profiles.
• Multivariate analyses including Principal Component Analysis (PCA), Discriminant Factor Analysis (DFA), and Partial Least Squares (PLS) regression were employed.
• Quality parameters such as Haugh units and sensory scores were measured as reference.

Main Results:

• PCA and DFA successfully differentiated egg samples based on storage time, explaining over 93% of the variance in volatile profiles.
• PLS models accurately predicted storage time (R²=0.9441), Haugh units (R²=0.9511), odor score (R²=0.9725), and overall acceptability (R²=0.9530).
• Predicted values from the GC e-nose models showed no significant difference from reference measurements.

Conclusions:

• The fast GC e-nose is a promising tool for objective assessment of hen egg freshness.
• This technology can reliably predict multiple quality indices of eggs during storage.
• The GC e-nose offers a feasible solution for real-time quality monitoring in the egg supply chain.