Fast Recognition of Table Eggs from Different Farming Systems Using Physical Traits and Multi-layer Perceptron

Abstract

ABSTRACT Eggs are a widely consumed source of protein, with consumers often preferring free-range eggs due to their higher nutritive value and prices. However, dishonest traders sometimes mislabel cage eggs as free-range eggs for unjustified profits. Biochemical methods are currently used to differentiate between caged and free-range eggs, which could involve chemical reagents, sample preparation, and costly instruments. In this study, physical traits measurements were combined with machine learning to identify eggs according to their farming system. Measurements of 27 physical traits for 480 eggs were conducted using simple tools, and the multicollinearity was reduced by comparing correlation coefficients, resulting in 16 physical traits. Multi-layer Perceptron Neural Network, Naive Bayes, Linear Support Vector Classifier, Radial Basis Functions Support Vector Classifier and Random Forest were used to create recognition models, and the leave-one-out cross-validation method was used for training and evaluation. The Multi-layer Perceptron Neural Network achieved the best classification performance with an accuracy of 0.94167, a F1 score of 0.94118. The result demonstrates that the physical traits of eggs provide sufficient features for the Multi-layer Perceptron Neural Network classifier. Compared to mainstream biochemical methods, we proposed a novel approach to differentiate between caged and free-range eggs using only physical trait measurements, thereby avoiding the need for chemical reagents, sample preparation, and expensive instruments.

Keywords

• Egg quality
• Cage egg
• Free-range egg
• Physical traits
• Machine learning