Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study

Detalhes bibliográficos
Autor(a) principal: Catelani, Tiago A. [UNESP]
Data de Publicação: 2018
Outros Autores: Santos, João Rodrigo, Páscoa, Ricardo N.M.J., Pezza, Leonardo [UNESP], Pezza, Helena R. [UNESP], Lopes, João A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.talanta.2017.11.010
http://hdl.handle.net/11449/179372
Resumo: This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.
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spelling Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility studyCoffee roastingMultivariate statistical process controlNear-infrared spectroscopyPrincipal component analysisReal-time monitoringThis work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Instituto de Química Universidade Estadual Paulista “Julio de Mesquita Filho” UNESP, R. Prof. Francisco Degni 55, P.O. Box 355LAQV/REQUIMTE - Departamento de Química e Bioquímica Faculdade de Ciências Universidade do PortoLAQV/REQUIMTE Laboratório de Química Aplicada Departamento de Ciências Químicas Faculdade de Farmácia Universidade do PortoResearch Institute for Medicines (iMed.ULisboa) Faculdade de Farmácia Universidade de LisboaInstituto de Química Universidade Estadual Paulista “Julio de Mesquita Filho” UNESP, R. Prof. Francisco Degni 55, P.O. Box 355CAPES: #99999.000655/2015-05CNPq: SFRH/BPD/81384/2011Universidade Estadual Paulista (Unesp)Universidade do PortoUniversidade de LisboaCatelani, Tiago A. [UNESP]Santos, João RodrigoPáscoa, Ricardo N.M.J.Pezza, Leonardo [UNESP]Pezza, Helena R. [UNESP]Lopes, João A.2018-12-11T17:34:54Z2018-12-11T17:34:54Z2018-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article292-299application/pdfhttp://dx.doi.org/10.1016/j.talanta.2017.11.010Talanta, v. 179, p. 292-299.0039-9140http://hdl.handle.net/11449/17937210.1016/j.talanta.2017.11.0102-s2.0-850346105482-s2.0-85034610548.pdf5978908591853524Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTalanta1,186info:eu-repo/semantics/openAccess2024-01-05T06:26:48Zoai:repositorio.unesp.br:11449/179372Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:11:44.106379Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
title Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
spellingShingle Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
Catelani, Tiago A. [UNESP]
Coffee roasting
Multivariate statistical process control
Near-infrared spectroscopy
Principal component analysis
Real-time monitoring
title_short Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
title_full Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
title_fullStr Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
title_full_unstemmed Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
title_sort Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study
author Catelani, Tiago A. [UNESP]
author_facet Catelani, Tiago A. [UNESP]
Santos, João Rodrigo
Páscoa, Ricardo N.M.J.
Pezza, Leonardo [UNESP]
Pezza, Helena R. [UNESP]
Lopes, João A.
author_role author
author2 Santos, João Rodrigo
Páscoa, Ricardo N.M.J.
Pezza, Leonardo [UNESP]
Pezza, Helena R. [UNESP]
Lopes, João A.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade do Porto
Universidade de Lisboa
dc.contributor.author.fl_str_mv Catelani, Tiago A. [UNESP]
Santos, João Rodrigo
Páscoa, Ricardo N.M.J.
Pezza, Leonardo [UNESP]
Pezza, Helena R. [UNESP]
Lopes, João A.
dc.subject.por.fl_str_mv Coffee roasting
Multivariate statistical process control
Near-infrared spectroscopy
Principal component analysis
Real-time monitoring
topic Coffee roasting
Multivariate statistical process control
Near-infrared spectroscopy
Principal component analysis
Real-time monitoring
description This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T17:34:54Z
2018-12-11T17:34:54Z
2018-03-01
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://dx.doi.org/10.1016/j.talanta.2017.11.010
Talanta, v. 179, p. 292-299.
0039-9140
http://hdl.handle.net/11449/179372
10.1016/j.talanta.2017.11.010
2-s2.0-85034610548
2-s2.0-85034610548.pdf
5978908591853524
url http://dx.doi.org/10.1016/j.talanta.2017.11.010
http://hdl.handle.net/11449/179372
identifier_str_mv Talanta, v. 179, p. 292-299.
0039-9140
10.1016/j.talanta.2017.11.010
2-s2.0-85034610548
2-s2.0-85034610548.pdf
5978908591853524
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Talanta
1,186
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 292-299
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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