NIR Spectroscopy for Freshness Detection and Classification of Chicken Eggs

Egg freshness is a critical parameter in the food industry, impacting product quality and safety. Traditional methods for assessing egg freshness, such as the Haugh Unit (HU), are time-consuming and often involve manual inspection. NIR (near-infrared) spectroscopy has become a viable tool for non-destructive egg freshness detection. This study explores reconceptualizing the utilization of NIR spectroscopy, when used alongside chemometric techniques for fast and accurate egg freshness assessment. Spectral data in the 900 to 1700 nm wavelength range were collected from eggs stored for various durations, ranging from day 1 to 25. Preprocessing methods, including Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) were used. The data from spectroscopy, after preprocessing, were used to develop and validate predictive models for egg freshness based on Haugh Units. The results demonstrate that NIR spectroscopy, in conjunction with appropriate chemometric techniques, can provide a robust and efficient method for egg freshness detection. The developed models exhibit high accuracy in distinguishing between fresh and stale eggs, offering potential benefits for the food industry, including quality control and product shelf-life management. This research showcases the viability of using NIR spectroscopy as a quick and non-invasive method for egg freshness assessment, with the potential for integration into industrial egg processing and quality assurance protocols. The findings contribute to the advancement of non-destructive quality control methods in the food sector, ensuring that consumers receive fresh and safe egg products.