Evidence map›Paper›PMID 39410227›Full record

ArticleFoods (Basel, Switzerland)2024

Application of 2D Extension of Hjorth's Descriptors to Distinguish Defined Groups of Bee Pollen Images.

Ewaryst Tkacz, Przemysław Rujna, Wojciech Więcławek, Bartosz Lewandowski, Barbara Mika, Szymon Sieciński

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ewaryst TkaczDepartment of Clinical Engineering, Academy of Silesia, Rolna 43, 40-555 Katowice, Poland.ORCID 0000-0003-2580-7954
Przemysław RujnaAI Technika Sp. z o.o., Włocławska 167, 87-100 Toruń, Poland.
Wojciech WięcławekDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, F.D. Roosevelta 40, 41-800 Zabrze, Poland.ORCID 0000-0002-7783-2732
Bartosz LewandowskiAI Technika Sp. z o.o., Włocławska 167, 87-100 Toruń, Poland.
Barbara MikaDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, F.D. Roosevelta 40, 41-800 Zabrze, Poland.ORCID 0000-0001-7094-2405
Szymon SiecińskiDepartment of Clinical Engineering, Academy of Silesia, Rolna 43, 40-555 Katowice, Poland.ORCID 0000-0002-3801-3144

Funding

National Research and Development Center (Poland) NUTRITECH1/0004A/2022
6 · The paper itself

Abstract

Adulteration of food products is a serious problem in the current economy. Honey has become the third most counterfeit food product in the world and requires effective authentication methods. This article presents a new approach to the differentiation of bee pollen, which can support the development of a methodology to test honey quality based on the analysis of bee pollen. The proposed method is built on applying the Hjorth descriptors-Activity, Mobility, and Complexity-known from electroencephalography (EEG) analysis, for 2D bee pollen images. The sources for extracting the bee pollen images were the photos of honey samples, which were taken using a digital camera with a resolution of 5 megapixels connected to the tube of an optical microscope. The honey samples used were prepared according to the Polish standard PN-88/A-77626 (related to the European standard CELEX-32001L0110-PL-TXT). The effectiveness of the proposed method was positively verified for three selected groups of bee pollen-

Indexed as

bee pollen analysisfood product adulterationfood product classificationHjorth’s descriptorspollen image parametrization

Identifiers

PMID39410227
PMCPMC11476096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.