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

doi:10.3390/foods13193193

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

^*Corresponding author for this work

Institute of Medical Informatics

2 Citations (Scopus)

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—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity 𝑝<0.00001, Mobility 𝑝<0.0001, and Complexity 𝑝<0.00001.

Original language	English
Article number	3193
Journal	Foods
Volume	13
Issue number	19
ISSN	2304-8158
DOIs	https://doi.org/10.3390/foods13193193
Publication status	Published - 08.10.2024

Funding

This article was supported by the project program NUTRITECH.I-004A/22 entitled “Development and implementation of a globally innovative digital honey pollen analysis service using technologies based on automation and artificial intelligence for use in the functional food production sector”, selected as part of the 1st NUTRITECH competition—nutrition in the light of the challenges of improving the well-being of society and climate change.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Research Areas and Centers

Research Area: Intelligent Systems

DFG Research Classification Scheme

2.31-05 Agricultural Economics, Agricultural Policy, Agricultural Sociology

Access to Document

10.3390/foods13193193

https://www.mdpi.com/2304-8158/13/19/3193Licence: CC BY

Cite this

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title = "Application of 2D Extension of Hjorth{\textquoteright}s Descriptors to Distinguish Defined Groups of Bee Pollen Images",

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—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity 𝑝<0.00001, Mobility 𝑝<0.0001, and Complexity 𝑝<0.00001.",

author = "Ewaryst Tkacz and Przemys{\l}aw Rujna and Wojciech Wi{\c e}c{\l}awek and Bartosz Lewandowski and Barbara Mika and Szymon Sieci{\'n}ski",

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doi = "10.3390/foods13193193",

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T1 - Application of 2D Extension of Hjorth’s Descriptors to Distinguish Defined Groups of Bee Pollen Images

AU - Tkacz, Ewaryst

AU - Rujna, Przemysław

AU - Więcławek, Wojciech

AU - Lewandowski, Bartosz

AU - Mika, Barbara

AU - Sieciński, Szymon

PY - 2024/10/8

Y1 - 2024/10/8

N2 - 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—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity 𝑝<0.00001, Mobility 𝑝<0.0001, and Complexity 𝑝<0.00001.

AB - 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—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity 𝑝<0.00001, Mobility 𝑝<0.0001, and Complexity 𝑝<0.00001.

UR - https://www.scopus.com/pages/publications/85206561225

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ER -

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

Abstract

Funding

UN SDGs

Research Areas and Centers

DFG Research Classification Scheme

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