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Updated: Apr 25, 2026
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
Published on: August 22, 2018
The use of decision trees and naïve Bayes algorithms and trace element patterns for controlling the authenticity of
Rommel Melgaço Barbosa1, Letícia Ramos Nacano, Rodolfo Freitas
1Inst. Informática, Univ. Federal de Goiás, Goiânia-Go, Brazil.
Abstract:
This article aims to evaluate 2 machine learning algorithms, decision trees and naïve Bayes (NB), for egg classification (free-range eggs compared with battery eggs). The database used for the study consisted of 15 chemical elements (As, Ba, Cd, Co, Cs, Cu, Fe, Mg, Mn, Mo, Pb, Se, Sr, V, and Zn) determined in 52 eggs samples (20 free-range and 32 battery eggs) by inductively coupled plasma mass spectrometry. Our results demonstrated that decision trees and NB associated with the mineral contents of eggs provide a high level of accuracy (above 80% and 90%, respectively) for classification between free-range and battery eggs and can be used as an alternative method for adulteration evaluation.
