Maqola

Identification of pollen taxa by different microscopy techniques

Matěj PospiechFaculty of Veterinary Hygiene and Ecology, Department of Plant Origin Food Sciences, University of Veterinary Sciences Brno, Brno, Czech RepublicZdeňka JavůrkováFaculty of Veterinary Hygiene and Ecology, Department of Plant Origin Food Sciences, University of Veterinary Sciences Brno, Brno, Czech RepublicPavel HrabecFaculty of Mechanical Engineering, Department of Statistics and Optimization, Brno University of Technology, Brno, Czech RepublicPavel ŠtarhaFaculty of Mechanical Engineering, Department of Computer Graphics and Geometry, Brno University of Technology, Brno, Czech RepublicSimona LjasovskáFaculty of Veterinary Hygiene and Ecology, Department of Plant Origin Food Sciences, University of Veterinary Sciences Brno, Brno, Czech RepublicJ BednárFaculty of Mechanical Engineering, Department of Statistics and Optimization, Brno University of Technology, Brno, Czech RepublicBohuslava TremlováFaculty of Veterinary Hygiene and Ecology, Department of Plant Origin Food Sciences, University of Veterinary Sciences Brno, Brno, Czech Republic

2021en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1371/journal.pone.0256808

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