Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique

Abstract

The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like the albumen and yolk heights. The study’s image processing involved noise reduction, feature extraction, and calibration. The PCA captured 90.18% of the data variability, while LDA achieved 100% classification accuracy, and KNN demonstrated 80% accuracy. These findings underscore the effectiveness of combining machine vision with statistical methods to enhance the egg grading accuracy, contributing to consumer safety and industry standards.