ArticlePloS one2021
Identification of pollen taxa by different microscopy techniques.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 38 citations in OpenAlex.
- The effects of honey solution on postoperative stress, gastric motility, and patient comfort: a randomized controlled trial.Croatian medical journal · 2025Trial
- Modern Comprehensive Metabolomic Profiling of Pollen Using Various Analytical Techniques.Molecules (Basel, Switzerland) · 2025Article
- Pot-pollen DNA barcoding as a tool to determine the diversity of plant species visited by Ecuadorian stingless bees.PloS one · 2025Article
- A Simple and User-Friendly Method for High-Quality Preparation of Pollen Grains for Scanning Electron Microscopy (SEM).Plants (Basel, Switzerland) · 2024Article
- Exploring the Influence of Soil Types on the Mineral Profile of Honey: Implications for Geographical Origin Prediction.Foods (Basel, Switzerland) · 2024Article
- Mint (Food technology and biotechnology · 2022Article
- Article
- Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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