The Use of Decision Trees and Naive Bayes Algorithms and Trace Element Patterns for Controlling the Authenticity of Free-Range-Pastured Hens' Eggs

被引：29

作者：

Barbosa, Rommel Melgaco ^{[1
]}

Nacano, Leticia Ramos ^{[2
]}

Freitas, Rodolfo ^{[2
]}

Batista, Bruno Lemos ^{[2
,3
]}

Barbosa, Fernando, Jr. ^{[2
]}

机构：

[1] Univ Fed Goias, Goiania, Go, Brazil

[2] Univ Sao Paulo, Lab Toxicol & Essencialidade Met, Fac Ciencias Farmaceut Ribeirao Preto, BR-1404903 Ribeirao Preto, SP, Brazil

[3] Univ Fed ABC, Ctr Ciencias Nat & Humanas, Santo Andre, SP, Brazil

来源：

JOURNAL OF FOOD SCIENCE | 2014年 / 79卷 / 09期

基金：

巴西圣保罗研究基金会;

关键词：

chemometrics; decision trees; eggs; ICP-MS; minerals; naive Bayes; PLASMA-MASS SPECTROMETRY; MINERAL-CONTENT; SAMPLES;

D O I：

10.1111/1750-3841.12577

中图分类号：

TS2 [食品工业];

学科分类号：

100403 [营养与食品卫生学];

摘要：

This article aims to evaluate 2 machine learning algorithms, decision trees and naive 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.

引用

页码：C1672 / C1677

页数：6

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