Predictive modeling for monitoring egg freshness during variable temperature storage conditions

Area of Science:

• Food Science
• Agricultural Science
• Quality Control

Background:

• Maintaining egg freshness during storage is crucial for the food industry and consumers.
• Existing methods for assessing egg quality can be time-consuming and destructive.
• Developing predictive models for egg freshness is essential for efficient supply chain management.

Purpose of the Study:

• To develop and validate predictive models for hen egg freshness based on key quality indices.
• To analyze the trends of quality indicators under various constant and fluctuating storage temperatures.
• To establish models capable of predicting egg freshness within a defined temperature range (5-30°C).

Main Methods:

• Conducted six experiments on hen eggs under controlled and variable temperature conditions (5-30°C).
• Monitored changes in relative weight loss (RWL), Haugh unit (HU), yolk index (YI), albumin index (AI), yolk pH, and albumin pH.
• Developed differential and quadratic polynomial models using constant temperature data, then applied them to fluctuating temperature data.

Main Results:

• Observed increasing relative weight loss (RWL) and decreasing Haugh units (HU) and yolk index (YI) across all temperatures.
• Yolk and albumin pH changes were inconsistent.
• Kinetic models accurately described changes in RWL (zeroth order), HU (third order), and YI (eighth order) with high accuracy and low bias.

Conclusions:

• The developed models effectively predict egg freshness using RWL, HU, and YI.
• These models are reliable for forecasting egg quality at temperatures ranging from 5 to 30°C.
• The findings support the use of predictive modeling for optimizing egg storage and shelf-life assessment.