Determination of maize hardness by biospeckle and fuzzy granularity

Autores
Weber, Christian; Dai Pra, Ana L.; Passoni, Lucía I.; Rabal, Héctor J.; Trivi, Marcelo Ricardo; Poggio Aguerre, Guillermo J.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.
Materia
Óptica, Acústica
Ciencias Agrarias
laser
biospeckle
dry milling
maize hardness
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/5872

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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/5872
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Determination of maize hardness by biospeckle and fuzzy granularityWeber, ChristianDai Pra, Ana L.Passoni, Lucía I.Rabal, Héctor J.Trivi, Marcelo RicardoPoggio Aguerre, Guillermo J.Óptica, AcústicaCiencias Agrariaslaserbiospeckledry millingmaize hardnessIn recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.Wiley2014-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/5872enginfo:eu-repo/semantics/altIdentifier/doi/10.1002/fsn3.130info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:12Zoai:digital.cic.gba.gob.ar:11746/5872Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:12.515CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Determination of maize hardness by biospeckle and fuzzy granularity
title Determination of maize hardness by biospeckle and fuzzy granularity
spellingShingle Determination of maize hardness by biospeckle and fuzzy granularity
Weber, Christian
Óptica, Acústica
Ciencias Agrarias
laser
biospeckle
dry milling
maize hardness
title_short Determination of maize hardness by biospeckle and fuzzy granularity
title_full Determination of maize hardness by biospeckle and fuzzy granularity
title_fullStr Determination of maize hardness by biospeckle and fuzzy granularity
title_full_unstemmed Determination of maize hardness by biospeckle and fuzzy granularity
title_sort Determination of maize hardness by biospeckle and fuzzy granularity
dc.creator.none.fl_str_mv Weber, Christian
Dai Pra, Ana L.
Passoni, Lucía I.
Rabal, Héctor J.
Trivi, Marcelo Ricardo
Poggio Aguerre, Guillermo J.
author Weber, Christian
author_facet Weber, Christian
Dai Pra, Ana L.
Passoni, Lucía I.
Rabal, Héctor J.
Trivi, Marcelo Ricardo
Poggio Aguerre, Guillermo J.
author_role author
author2 Dai Pra, Ana L.
Passoni, Lucía I.
Rabal, Héctor J.
Trivi, Marcelo Ricardo
Poggio Aguerre, Guillermo J.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Óptica, Acústica
Ciencias Agrarias
laser
biospeckle
dry milling
maize hardness
topic Óptica, Acústica
Ciencias Agrarias
laser
biospeckle
dry milling
maize hardness
dc.description.none.fl_txt_mv In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.
description In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.
publishDate 2014
dc.date.none.fl_str_mv 2014-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/5872
url https://digital.cic.gba.gob.ar/handle/11746/5872
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/fsn3.130
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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