Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks

Bibliographic Details
Main Author: Malfatti L.H.*
Publication Date: 2021
Other Authors: Zampar, Aline, Galvao, Alessandro Cazonatto, Robazza, Weber Da Silva, Boiago, Marcel Manente
Format: Article
Language: eng
Source: Repositório Institucional da Udesc
dARK ID: ark:/33523/001300000jvg4
Download full: https://repositorio.udesc.br/handle/UDESC/3676
Summary: © 2021 Elsevier LtdEgg quality is a multidimensional concept that depends on many different parameters. Many studies have evaluated different egg quality attributes subjected to various storage conditions. The present work aimed to study the influence of three environmental parameters (temperature, storage time and relative humidity) on egg quality indicators. Through application of response surface methodology, it was verified that the temperature is the most important environmental factor affecting egg quality attributes followed by the storage time and relative humidity, respectively. Principal Component Analysis showed that most quality indexes are similar except for the eggshell percentage that represents an exterior quality indicator. An artificial neural network composed by one hidden layer and four neurons provided accurate predictions of the Yolk index and is a promising tool to evaluate egg quality.
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spelling Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks© 2021 Elsevier LtdEgg quality is a multidimensional concept that depends on many different parameters. Many studies have evaluated different egg quality attributes subjected to various storage conditions. The present work aimed to study the influence of three environmental parameters (temperature, storage time and relative humidity) on egg quality indicators. Through application of response surface methodology, it was verified that the temperature is the most important environmental factor affecting egg quality attributes followed by the storage time and relative humidity, respectively. Principal Component Analysis showed that most quality indexes are similar except for the eggshell percentage that represents an exterior quality indicator. An artificial neural network composed by one hidden layer and four neurons provided accurate predictions of the Yolk index and is a promising tool to evaluate egg quality.2024-12-06T11:30:46Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0023-643810.1016/j.lwt.2021.111720https://repositorio.udesc.br/handle/UDESC/3676ark:/33523/001300000jvg4LWT148Malfatti L.H.*Zampar, AlineGalvao, Alessandro CazonattoRobazza, Weber Da SilvaBoiago, Marcel Manenteengreponame:Repositório Institucional da Udescinstname:Universidade do Estado de Santa Catarina (UDESC)instacron:UDESCinfo:eu-repo/semantics/openAccess2024-12-07T20:42:24Zoai:repositorio.udesc.br:UDESC/3676Biblioteca Digital de Teses e Dissertaçõeshttps://pergamumweb.udesc.br/biblioteca/index.phpPRIhttps://repositorio-api.udesc.br/server/oai/requestri@udesc.bropendoar:63912024-12-07T20:42:24Repositório Institucional da Udesc - Universidade do Estado de Santa Catarina (UDESC)false
dc.title.none.fl_str_mv Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
title Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
spellingShingle Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
Malfatti L.H.*
title_short Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
title_full Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
title_fullStr Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
title_full_unstemmed Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
title_sort Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
author Malfatti L.H.*
author_facet Malfatti L.H.*
Zampar, Aline
Galvao, Alessandro Cazonatto
Robazza, Weber Da Silva
Boiago, Marcel Manente
author_role author
author2 Zampar, Aline
Galvao, Alessandro Cazonatto
Robazza, Weber Da Silva
Boiago, Marcel Manente
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Malfatti L.H.*
Zampar, Aline
Galvao, Alessandro Cazonatto
Robazza, Weber Da Silva
Boiago, Marcel Manente
description © 2021 Elsevier LtdEgg quality is a multidimensional concept that depends on many different parameters. Many studies have evaluated different egg quality attributes subjected to various storage conditions. The present work aimed to study the influence of three environmental parameters (temperature, storage time and relative humidity) on egg quality indicators. Through application of response surface methodology, it was verified that the temperature is the most important environmental factor affecting egg quality attributes followed by the storage time and relative humidity, respectively. Principal Component Analysis showed that most quality indexes are similar except for the eggshell percentage that represents an exterior quality indicator. An artificial neural network composed by one hidden layer and four neurons provided accurate predictions of the Yolk index and is a promising tool to evaluate egg quality.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024-12-06T11:30:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv 0023-6438
10.1016/j.lwt.2021.111720
https://repositorio.udesc.br/handle/UDESC/3676
dc.identifier.dark.fl_str_mv ark:/33523/001300000jvg4
identifier_str_mv 0023-6438
10.1016/j.lwt.2021.111720
ark:/33523/001300000jvg4
url https://repositorio.udesc.br/handle/UDESC/3676
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv LWT
148
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da Udesc
instname:Universidade do Estado de Santa Catarina (UDESC)
instacron:UDESC
instname_str Universidade do Estado de Santa Catarina (UDESC)
instacron_str UDESC
institution UDESC
reponame_str Repositório Institucional da Udesc
collection Repositório Institucional da Udesc
repository.name.fl_str_mv Repositório Institucional da Udesc - Universidade do Estado de Santa Catarina (UDESC)
repository.mail.fl_str_mv ri@udesc.br
_version_ 1842258139157626880

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