Predicting the shelf life and quality of eggs using NIR analysis

Predicting the shelf life and quality of eggs using NIR analysis

Chief investigator: Associate Professor Daniel Cozzolino

MSc Student: Pooja Sanal 

QASP facility: Meat Science Analytical Laboratory

Eggs are a global food staple, valued for their nutritional content and widespread use in various culinary applications. However, maintaining the quality and safety of eggs throughout their shelf life is a critical concern for consumers, producers, and distributors, as the degradation of quality over time can lead to economic losses and potential health risks. One of the key challenges in the egg industry is accurately predicting the shelf life of eggs - the duration of time that eggs can be stored and consumed without compromising their quality and safety.

Traditional methods for assessing egg freshness involve sensory evaluation, including the examination of eggshell integrity, yolk appearance, and albumen viscosity. While these methods provide valuable insights, they are often subjective and time-consuming.

 

 

The application of Near-Infrared (NIR) spectroscopy has emerged as a promising technique for predicting the shelf life of eggs. NIR spectroscopy involves the interaction of electromagnetic radiation with molecular vibrations within a sample, generating a unique spectral signature that contains information about the chemical composition of the sample.

In this project, the aim is utilise NIR analysis to accurately estimate the shelf life of eggs, by correlating the spectral data with traditional sensory evaluation results and biochemical markers associated with egg freshness. 1040 eggs (with an equal number of cage eggs and free range, further broken down into refridgerated and room-temperature sample groups) will be analysed at various points over a two-month period. 

This technique has the potential to revolutionize the egg industry by providing a more objective and efficient means of assessing egg quality and freshness. The methodology and insights gained from this study could pave the way for similar applications in other food industries, promoting the adoption of advanced analytical techniques for quality control and safety assurance.