Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Naji, M.
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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2023Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy—Case Study: Blackcurrant Powders4citations
  • 2022Resolving atomic-scale interactions in non-fullerene acceptor organic solar cells with solidstate NMR spectroscopy, crystallographic modelling, and molecular dynamics simulations57citations
  • 2022Understanding the p-doping of spiroOMeTAD by tris(pentafluorophenyl)borane15citations
  • 2021Cytotoxic Activity against A549 Human Lung Cancer Cells and ADMET Analysis of New Pyrazole Derivatives8citations

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Chart of shared publication
Samborska, Katarzyna
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Przybył, Krzysztof
1 / 1 shared
Jedlińska, Aleksandra
1 / 1 shared
Koszela, Krzysztof
1 / 1 shared
Biegalski, Jakub
1 / 1 shared
Masewicz, Lukasz
1 / 1 shared
Walkowiak, Katarzyna
1 / 1 shared
Yoon, Sangcheol
1 / 4 shared
Reddy, G., N. Manjunatha
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Benjamin, R. Luginbuhl
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Wang, Tonghui
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Jung Kim, Hyo
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Chae, Sangmin
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Yi, Ahra
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Raval, Parth
2 / 8 shared
Kupgan, Grit
1 / 1 shared
Schopp, Nora
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Vezin, Herve
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Roussel, Pascal
1 / 65 shared
Dhennin, Margot
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Reddy, Manjunatha
1 / 10 shared
Nguyen, Thuc-Quyen
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Pitucha, Monika
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Raducka, Anita
1 / 6 shared
Szymanski, Pawel
1 / 3 shared
Czarnecka, Kamila
1 / 2 shared
Czylkowska, Agnieszka
1 / 6 shared
Rogalewicz, Bartłomiej
1 / 1 shared
Kręcisz, Paweł
1 / 1 shared
Szczesio, Małgorzata
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Chart of publication period
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2022
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Co-Authors (by relevance)

  • Samborska, Katarzyna
  • Przybył, Krzysztof
  • Jedlińska, Aleksandra
  • Koszela, Krzysztof
  • Biegalski, Jakub
  • Masewicz, Lukasz
  • Walkowiak, Katarzyna
  • Yoon, Sangcheol
  • Reddy, G., N. Manjunatha
  • Benjamin, R. Luginbuhl
  • Wang, Tonghui
  • Du, Zhifang
  • Jung Kim, Hyo
  • Coropceanu, Veaceslav
  • Nguyen, Thucquyen
  • Brédas, Jeanluc
  • Chae, Sangmin
  • Yi, Ahra
  • Raval, Parth
  • Kupgan, Grit
  • Schopp, Nora
  • Vezin, Herve
  • Roussel, Pascal
  • Dhennin, Margot
  • Reddy, Manjunatha
  • Nguyen, Thuc-Quyen
  • Pitucha, Monika
  • Raducka, Anita
  • Szymanski, Pawel
  • Czarnecka, Kamila
  • Czylkowska, Agnieszka
  • Rogalewicz, Bartłomiej
  • Kręcisz, Paweł
  • Szczesio, Małgorzata
OrganizationsLocationPeople

article

Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy—Case Study: Blackcurrant Powders

  • Samborska, Katarzyna
  • Przybył, Krzysztof
  • Jedlińska, Aleksandra
  • Koszela, Krzysztof
  • Biegalski, Jakub
  • Pawlak, Tomasz
  • Masewicz, Lukasz
  • Walkowiak, Katarzyna
Abstract

<jats:p>Fruits represent a valuable source of bioactivity, vitamins, minerals and antioxidants. They are often used in research due to their potential to extend sustainability and edibility. In this research, the currants were used to obtain currant powders by dehumidified air-assisted spray drying. In the research analysis of currant powders, advanced machine learning techniques were used in combination with Lab color space model analysis and Fourier transform infrared spectroscopy (FTIR). The aim of this project was to provide authentic information about the qualities of currant powders, taking into account their type and carrier content. In addition, the machine learning models were developed to support the recognition of individual blackcurrant powder samples based on Lab color. These results were compared using their physical properties and FTIR spectroscopy to determine the homogeneity of these powders; this will help reduce operating and energy costs while also increasing the production rate, and even the possibility of improving the available drying system.</jats:p>

Topics
  • mineral
  • Fourier transform infrared spectroscopy
  • drying
  • machine learning
  • bioactivity