People | Locations | Statistics |
---|---|---|
Naji, M. |
| |
Motta, Antonella |
| |
Aletan, Dirar |
| |
Mohamed, Tarek |
| |
Ertürk, Emre |
| |
Taccardi, Nicola |
| |
Kononenko, Denys |
| |
Petrov, R. H. | Madrid |
|
Alshaaer, Mazen | Brussels |
|
Bih, L. |
| |
Casati, R. |
| |
Muller, Hermance |
| |
Kočí, Jan | Prague |
|
Šuljagić, Marija |
| |
Kalteremidou, Kalliopi-Artemi | Brussels |
|
Azam, Siraj |
| |
Ospanova, Alyiya |
| |
Blanpain, Bart |
| |
Ali, M. A. |
| |
Popa, V. |
| |
Rančić, M. |
| |
Ollier, Nadège |
| |
Azevedo, Nuno Monteiro |
| |
Landes, Michael |
| |
Rignanese, Gian-Marco |
|
Pawlak, Tomasz
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 Powderscitations
- 2022Resolving atomic-scale interactions in non-fullerene acceptor organic solar cells with solidstate NMR spectroscopy, crystallographic modelling, and molecular dynamics simulationscitations
- 2022Understanding the p-doping of spiroOMeTAD by tris(pentafluorophenyl)boranecitations
- 2021Cytotoxic Activity against A549 Human Lung Cancer Cells and ADMET Analysis of New Pyrazole Derivativescitations
Places of action
Organizations | Location | People |
---|
article
Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy—Case Study: Blackcurrant Powders
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>