Identification of pollen taxa by different microscopy techniques.

Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image an...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Matej Pospiech, Zdeňka Javůrková, Pavel Hrabec, Pavel Štarha, Simona Ljasovská, Josef Bednář, Bohuslava Tremlová
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32ea9df901794e90aa3cd81072fac9e4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32ea9df901794e90aa3cd81072fac9e4
record_format	dspace
spelling	oai:doaj.org-article:32ea9df901794e90aa3cd81072fac9e42021-12-02T20:08:41ZIdentification of pollen taxa by different microscopy techniques.1932-620310.1371/journal.pone.0256808https://doaj.org/article/32ea9df901794e90aa3cd81072fac9e42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256808https://doaj.org/toc/1932-6203Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.Matej PospiechZdeňka JavůrkováPavel HrabecPavel ŠtarhaSimona LjasovskáJosef BednářBohuslava TremlováPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256808 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matej Pospiech Zdeňka Javůrková Pavel Hrabec Pavel Štarha Simona Ljasovská Josef Bednář Bohuslava Tremlová Identification of pollen taxa by different microscopy techniques.
description	Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.
format	article
author	Matej Pospiech Zdeňka Javůrková Pavel Hrabec Pavel Štarha Simona Ljasovská Josef Bednář Bohuslava Tremlová
author_facet	Matej Pospiech Zdeňka Javůrková Pavel Hrabec Pavel Štarha Simona Ljasovská Josef Bednář Bohuslava Tremlová
author_sort	Matej Pospiech
title	Identification of pollen taxa by different microscopy techniques.
title_short	Identification of pollen taxa by different microscopy techniques.
title_full	Identification of pollen taxa by different microscopy techniques.
title_fullStr	Identification of pollen taxa by different microscopy techniques.
title_full_unstemmed	Identification of pollen taxa by different microscopy techniques.
title_sort	identification of pollen taxa by different microscopy techniques.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/32ea9df901794e90aa3cd81072fac9e4
work_keys_str_mv	AT matejpospiech identificationofpollentaxabydifferentmicroscopytechniques AT zdenkajavurkova identificationofpollentaxabydifferentmicroscopytechniques AT pavelhrabec identificationofpollentaxabydifferentmicroscopytechniques AT pavelstarha identificationofpollentaxabydifferentmicroscopytechniques AT simonaljasovska identificationofpollentaxabydifferentmicroscopytechniques AT josefbednar identificationofpollentaxabydifferentmicroscopytechniques AT bohuslavatremlova identificationofpollentaxabydifferentmicroscopytechniques
_version_	1718375172085383168

Identification of pollen taxa by different microscopy techniques.

Ejemplares similares