Terahertz Imaging and Machine Learning in the Classification of Coffee Beans

Descripción del Articulo

Acknowledgments. P. Uceda and H. Yoshida acknowledge the financial support from Project Concytec – The World Bank “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE through Fondecyt [contract no 006–2018].
Detalles Bibliográficos
Autores: Uceda P., Yoshida H., Castillo P.
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/3062
Enlace del recurso:https://hdl.handle.net/20.500.12390/3062
https://doi.org/10.1007/978-3-030-75680-2_94
Nivel de acceso:acceso abierto
Materia:THz spectroscopy
Chemometrics
Coffee post-harvest
Supervised learning
https://purl.org/pe-repo/ocde/ford#2.02.022
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/3062
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
title Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
spellingShingle Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
Uceda P.
THz spectroscopy
Chemometrics
Coffee post-harvest
Supervised learning
https://purl.org/pe-repo/ocde/ford#2.02.022
title_short Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
title_full Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
title_fullStr Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
title_full_unstemmed Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
title_sort Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
author Uceda P.
author_facet Uceda P.
Yoshida H.
Castillo P.
author_role author
author2 Yoshida H.
Castillo P.
author2_role author
author
dc.contributor.author.fl_str_mv Uceda P.
Yoshida H.
Castillo P.
dc.subject.none.fl_str_mv THz spectroscopy
topic THz spectroscopy
Chemometrics
Coffee post-harvest
Supervised learning
https://purl.org/pe-repo/ocde/ford#2.02.022
dc.subject.es_PE.fl_str_mv Chemometrics
Coffee post-harvest
Supervised learning
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.022
description Acknowledgments. P. Uceda and H. Yoshida acknowledge the financial support from Project Concytec – The World Bank “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE through Fondecyt [contract no 006–2018].
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/3062
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-75680-2_94
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85111350964
url https://hdl.handle.net/20.500.12390/3062
https://doi.org/10.1007/978-3-030-75680-2_94
identifier_str_mv 2-s2.0-85111350964
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Smart Innovation, Systems and Technologies
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Science and Business Media Deutschland GmbH
publisher.none.fl_str_mv Springer Science and Business Media Deutschland GmbH
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp08821600rp08822600rp08820600Uceda P.Yoshida H.Castillo P.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2021https://hdl.handle.net/20.500.12390/3062https://doi.org/10.1007/978-3-030-75680-2_942-s2.0-85111350964Acknowledgments. P. Uceda and H. Yoshida acknowledge the financial support from Project Concytec – The World Bank “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE through Fondecyt [contract no 006–2018].The geographical origin of coffee beans represents an effect on the attributes and quality of the product due to the different soil and weather conditions for a specific location. Therefore, the development of methods for rapid classification and authentication of coffee beans based on their geographical origin is essential. This research was done with the purpose of determining the capacity of coffee (Coffea arabica) varieties classification with the use of Terahertz (THz) imaging and machine learning. THz images of coffee beans samples from 3 different geographical origins were acquired with a time-domain spectrometer and then used to measure the classification performance of methods such as neural networks, random forests, and support vector machines. The results obtained reached an accuracy up to 91.2%, which showed that the use of THz imaging and machine learning is an effective method for the non-destructive analysis of coffee variables and classification based on geographical origin. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer Science and Business Media Deutschland GmbHSmart Innovation, Systems and Technologiesinfo:eu-repo/semantics/openAccessTHz spectroscopyChemometrics-1Coffee post-harvest-1Supervised learning-1https://purl.org/pe-repo/ocde/ford#2.02.022-1Terahertz Imaging and Machine Learning in the Classification of Coffee Beansinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/3062oai:repositorio.concytec.gob.pe:20.500.12390/30622024-05-30 16:13:38.883http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ada44a54-2aae-4fa4-a9b6-780f4b11523c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Terahertz Imaging and Machine Learning in the Classification of Coffee Beans</Title> <PublishedIn> <Publication> <Title>Smart Innovation, Systems and Technologies</Title> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <DOI>https://doi.org/10.1007/978-3-030-75680-2_94</DOI> <SCP-Number>2-s2.0-85111350964</SCP-Number> <Authors> <Author> <DisplayName>Uceda P.</DisplayName> <Person id="rp08821" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Yoshida H.</DisplayName> <Person id="rp08822" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Castillo P.</DisplayName> <Person id="rp08820" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Science and Business Media Deutschland GmbH</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>THz spectroscopy</Keyword> <Keyword>Chemometrics</Keyword> <Keyword>Coffee post-harvest</Keyword> <Keyword>Supervised learning</Keyword> <Abstract>The geographical origin of coffee beans represents an effect on the attributes and quality of the product due to the different soil and weather conditions for a specific location. Therefore, the development of methods for rapid classification and authentication of coffee beans based on their geographical origin is essential. This research was done with the purpose of determining the capacity of coffee (Coffea arabica) varieties classification with the use of Terahertz (THz) imaging and machine learning. THz images of coffee beans samples from 3 different geographical origins were acquired with a time-domain spectrometer and then used to measure the classification performance of methods such as neural networks, random forests, and support vector machines. The results obtained reached an accuracy up to 91.2%, which showed that the use of THz imaging and machine learning is an effective method for the non-destructive analysis of coffee variables and classification based on geographical origin. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.448654
Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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