Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.3390/app112110319 |
Texto Completo: | http://hdl.handle.net/10316/103714 https://doi.org/10.3390/app112110319 |
Resumo: | This paper presents an extended comparison study between 16 different linear and nonlinear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the SavitzkyGolay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters. |
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Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methodswine grape berrieshyperspectral imaginglinear and non-linear regression methodspenalized regressionvariables importanceThis paper presents an extended comparison study between 16 different linear and nonlinear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the SavitzkyGolay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters.MDPI2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103714http://hdl.handle.net/10316/103714https://doi.org/10.3390/app112110319eng2076-3417Gomes, VéroniqueRendall, RicardoReis, MarcoMendes-Ferreira, AnaMelo-Pinto, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-23T21:36:58Zoai:estudogeral.uc.pt:10316/103714Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:30.057327Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
title |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
spellingShingle |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods Gomes, Véronique wine grape berries hyperspectral imaging linear and non-linear regression methods penalized regression variables importance Gomes, Véronique wine grape berries hyperspectral imaging linear and non-linear regression methods penalized regression variables importance |
title_short |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
title_full |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
title_fullStr |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
title_full_unstemmed |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
title_sort |
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods |
author |
Gomes, Véronique |
author_facet |
Gomes, Véronique Gomes, Véronique Rendall, Ricardo Reis, Marco Mendes-Ferreira, Ana Melo-Pinto, Pedro Rendall, Ricardo Reis, Marco Mendes-Ferreira, Ana Melo-Pinto, Pedro |
author_role |
author |
author2 |
Rendall, Ricardo Reis, Marco Mendes-Ferreira, Ana Melo-Pinto, Pedro |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Gomes, Véronique Rendall, Ricardo Reis, Marco Mendes-Ferreira, Ana Melo-Pinto, Pedro |
dc.subject.por.fl_str_mv |
wine grape berries hyperspectral imaging linear and non-linear regression methods penalized regression variables importance |
topic |
wine grape berries hyperspectral imaging linear and non-linear regression methods penalized regression variables importance |
description |
This paper presents an extended comparison study between 16 different linear and nonlinear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the SavitzkyGolay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
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 |
http://hdl.handle.net/10316/103714 http://hdl.handle.net/10316/103714 https://doi.org/10.3390/app112110319 |
url |
http://hdl.handle.net/10316/103714 https://doi.org/10.3390/app112110319 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1822183293662003200 |
dc.identifier.doi.none.fl_str_mv |
10.3390/app112110319 |