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The Use of Decision Trees and Naïve Bayes Algorithms and Trace Element Patterns for Controlling the Authenticity of Free‐Range‐Pastured Hens’ Eggs
Author(s) -
Barbosa Rommel Melgaço,
Nacano Letícia Ramos,
Freitas Rodolfo,
Batista Bruno Lemos,
Barbosa Fernando
Publication year - 2014
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/1750-3841.12577
Subject(s) - bayes' theorem , battery (electricity) , range (aeronautics) , inductively coupled plasma mass spectrometry , algorithm , analytical chemistry (journal) , chemistry , mass spectrometry , mathematics , chromatography , materials science , statistics , physics , bayesian probability , power (physics) , quantum mechanics , composite material
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.