Identification of pollen taxa by different microscopy techniques

Autor: Pavel Starha, Bohuslava Tremlová, Zdeňka Javůrková, Simona Ljasovská, Pavel Hrabec, Josef Bednář, Matej Pospiech
Jazyk: angličtina
Rok vydání: 2021
Předmět:
field contrast
Plant Science
shape
medicine.disease_cause
Animal Products
Microscopy
Medicine and Health Sciences
Image Processing
Computer-Assisted
Electron Microscopy
Czech Republic
Multidisciplinary
Plant Anatomy
Eukaryota
Phase Contrast Microscopy
Light Microscopy
Agriculture
Honey
Plants
Visual inspection
classification
Physical Sciences
Medicine
Pollen
Identification (biology)
Scanning Electron Microscopy
Research Article
Imaging Techniques
Science
Color
Image processing
honey
Brassica
Image Analysis
Biology
system
Research and Analysis Methods
Melissopalynology
medicine
features
automation
Nutrition
light-microscopy
business.industry
Organisms
Biology and Life Sciences
Pattern recognition
Eigenvalues
Dark field microscopy
quantification
Diet
images
Taxon
Algebra
Linear Algebra
Food
Artificial intelligence
business
Beekeeping
Mathematics
Zdroj: PLoS ONE
PLOS ONE. 2021, vol. 16, issue 9, p. 1-25.
PLoS ONE, Vol 16, Iss 9, p e0256808 (2021)
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje