Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.procbio.2016.07.017 http://hdl.handle.net/11449/161974 |
Resumo: | This work aimed to establish a chemometric technique for quantifying amylase and protease activities as well as protein concentration in aqueous extracts of Rhizopus microsporus var. oligosporus obtained via solid-state fermentation (SSF). The kinetics of four agro-industrial wastes (wheat bran, soybean meal, type II wheat flour and sugarcane bagasse) were studied for 144 h, along with two different sets of their ternary mixtures, at a constant fermentation time of 120 h, to obtain primary data (biochemical parameters as well as near-infrared (NIR) spectral data). Then, models such as artificial neural network (ANN) and partial least squares (PLS) were calibrated to predict biochemical parameters using the spectral data. Primary data and three methods of preprocessing data - first, second and third derivatives - were assessed as inputs for both chemometric tools. The third derivative, that is, spectral pre-processing plus an optimized ANN, showed the least relative errors (<8.3%+/- 10.5%). The third-derivative spectrum was found to be suitable as the ANN input data for monitoring amylase and protease activities and protein concentration in the SSF under study. The proposed methodology can serve as a foundation for at-line sensor development and decrease the time and cost of bioprocess development using Rhizopus microsporus var. oligosporus. (C) 2016 Elsevier Ltd. All rights reserved. |
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Repositório Institucional da UNESP |
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2946 |
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Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentationArtificial neural networkBioprocess monitoringChemometricsEnzymesNIR spectroscopyPartial least squaresThis work aimed to establish a chemometric technique for quantifying amylase and protease activities as well as protein concentration in aqueous extracts of Rhizopus microsporus var. oligosporus obtained via solid-state fermentation (SSF). The kinetics of four agro-industrial wastes (wheat bran, soybean meal, type II wheat flour and sugarcane bagasse) were studied for 144 h, along with two different sets of their ternary mixtures, at a constant fermentation time of 120 h, to obtain primary data (biochemical parameters as well as near-infrared (NIR) spectral data). Then, models such as artificial neural network (ANN) and partial least squares (PLS) were calibrated to predict biochemical parameters using the spectral data. Primary data and three methods of preprocessing data - first, second and third derivatives - were assessed as inputs for both chemometric tools. The third derivative, that is, spectral pre-processing plus an optimized ANN, showed the least relative errors (<8.3%+/- 10.5%). The third-derivative spectrum was found to be suitable as the ANN input data for monitoring amylase and protease activities and protein concentration in the SSF under study. The proposed methodology can serve as a foundation for at-line sensor development and decrease the time and cost of bioprocess development using Rhizopus microsporus var. oligosporus. (C) 2016 Elsevier Ltd. All rights reserved.Fundação para o Desenvolvimento da UNESP (FUNDUNESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Paulista, Dept Ciencias Biol, Grp Engn Bioproc, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Lab Biotecnol Ind, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Inst Quim Araraquara, Campus Araraquara,Rua Prof Francisco Degni 55, BR-14800900 Araraquara, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Lab Matemat Aplicada, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Grp Engn Bioproc, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Lab Biotecnol Ind, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Inst Quim Araraquara, Campus Araraquara,Rua Prof Francisco Degni 55, BR-14800900 Araraquara, SP, BrazilUniv Estadual Paulista, Dept Ciencias Biol, Lab Matemat Aplicada, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilFUNDUNESP: 0312/001/14-Prope/CDCFAPESP: 14/06447-0Elsevier B.V.Universidade Estadual Paulista (Unesp)Barchi, Augusto Cesar [UNESP]Ito, Shuri [UNESP]Escaramboni, Bruna [UNESP]Oliva Neto, Pedro de [UNESP]Herculano, Rondinelli Donizetti [UNESP]Romeiro Miranda, Matheus Carlos [UNESP]Passalia, Felipe Jose [UNESP]Rocha, Jose Celso [UNESP]Fernandez Nunez, Eutimio Gustavo [UNESP]2018-11-26T17:06:23Z2018-11-26T17:06:23Z2016-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1338-1347application/pdfhttp://dx.doi.org/10.1016/j.procbio.2016.07.017Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 10, p. 1338-1347, 2016.1359-5113http://hdl.handle.net/11449/16197410.1016/j.procbio.2016.07.017WOS:000384384600004WOS000384384600004.pdf46389522635027440000-0001-9378-9036Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProcess Biochemistry0,761info:eu-repo/semantics/openAccess2023-10-05T06:04:37Zoai:repositorio.unesp.br:11449/161974Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-05T06:04:37Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
title |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
spellingShingle |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation Barchi, Augusto Cesar [UNESP] Artificial neural network Bioprocess monitoring Chemometrics Enzymes NIR spectroscopy Partial least squares |
title_short |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
title_full |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
title_fullStr |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
title_full_unstemmed |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
title_sort |
Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation |
author |
Barchi, Augusto Cesar [UNESP] |
author_facet |
Barchi, Augusto Cesar [UNESP] Ito, Shuri [UNESP] Escaramboni, Bruna [UNESP] Oliva Neto, Pedro de [UNESP] Herculano, Rondinelli Donizetti [UNESP] Romeiro Miranda, Matheus Carlos [UNESP] Passalia, Felipe Jose [UNESP] Rocha, Jose Celso [UNESP] Fernandez Nunez, Eutimio Gustavo [UNESP] |
author_role |
author |
author2 |
Ito, Shuri [UNESP] Escaramboni, Bruna [UNESP] Oliva Neto, Pedro de [UNESP] Herculano, Rondinelli Donizetti [UNESP] Romeiro Miranda, Matheus Carlos [UNESP] Passalia, Felipe Jose [UNESP] Rocha, Jose Celso [UNESP] Fernandez Nunez, Eutimio Gustavo [UNESP] |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Barchi, Augusto Cesar [UNESP] Ito, Shuri [UNESP] Escaramboni, Bruna [UNESP] Oliva Neto, Pedro de [UNESP] Herculano, Rondinelli Donizetti [UNESP] Romeiro Miranda, Matheus Carlos [UNESP] Passalia, Felipe Jose [UNESP] Rocha, Jose Celso [UNESP] Fernandez Nunez, Eutimio Gustavo [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural network Bioprocess monitoring Chemometrics Enzymes NIR spectroscopy Partial least squares |
topic |
Artificial neural network Bioprocess monitoring Chemometrics Enzymes NIR spectroscopy Partial least squares |
description |
This work aimed to establish a chemometric technique for quantifying amylase and protease activities as well as protein concentration in aqueous extracts of Rhizopus microsporus var. oligosporus obtained via solid-state fermentation (SSF). The kinetics of four agro-industrial wastes (wheat bran, soybean meal, type II wheat flour and sugarcane bagasse) were studied for 144 h, along with two different sets of their ternary mixtures, at a constant fermentation time of 120 h, to obtain primary data (biochemical parameters as well as near-infrared (NIR) spectral data). Then, models such as artificial neural network (ANN) and partial least squares (PLS) were calibrated to predict biochemical parameters using the spectral data. Primary data and three methods of preprocessing data - first, second and third derivatives - were assessed as inputs for both chemometric tools. The third derivative, that is, spectral pre-processing plus an optimized ANN, showed the least relative errors (<8.3%+/- 10.5%). The third-derivative spectrum was found to be suitable as the ANN input data for monitoring amylase and protease activities and protein concentration in the SSF under study. The proposed methodology can serve as a foundation for at-line sensor development and decrease the time and cost of bioprocess development using Rhizopus microsporus var. oligosporus. (C) 2016 Elsevier Ltd. All rights reserved. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-10-01 2018-11-26T17:06:23Z 2018-11-26T17:06:23Z |
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.procbio.2016.07.017 Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 10, p. 1338-1347, 2016. 1359-5113 http://hdl.handle.net/11449/161974 10.1016/j.procbio.2016.07.017 WOS:000384384600004 WOS000384384600004.pdf 4638952263502744 0000-0001-9378-9036 |
url |
http://dx.doi.org/10.1016/j.procbio.2016.07.017 http://hdl.handle.net/11449/161974 |
identifier_str_mv |
Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 10, p. 1338-1347, 2016. 1359-5113 10.1016/j.procbio.2016.07.017 WOS:000384384600004 WOS000384384600004.pdf 4638952263502744 0000-0001-9378-9036 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Process Biochemistry 0,761 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1338-1347 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science 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 |
|
_version_ |
1799964441372000256 |