Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy - Case Study: Blackcurrant Powders
| cris.lastimport.scopus | 2025-10-23T06:54:24Z | |
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| dc.abstract.en | 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. | |
| dc.affiliation | Wydział Nauk o Żywności i Żywieniu | |
| dc.affiliation | Wydział Inżynierii Środowiska i Inżynierii Mechanicznej | |
| dc.affiliation.institute | Katedra Mleczarstwa i Inżynierii Procesowej | |
| dc.affiliation.institute | Katedra Fizyki i Biofizyki | |
| dc.affiliation.institute | Katedra Inżynierii Biosystemów | |
| dc.contributor.author | Przybył, Krzysztof | |
| dc.contributor.author | Walkowiak, Katarzyna | |
| dc.contributor.author | Jedlińska, Aleksandra | |
| dc.contributor.author | Samborska, Katarzyna | |
| dc.contributor.author | Masewicz, Łukasz | |
| dc.contributor.author | Biegalski, Jakub | |
| dc.contributor.author | Pawlak, Tomasz | |
| dc.contributor.author | Koszela, Krzysztof | |
| dc.date.access | 2025-06-04 | |
| dc.date.accessioned | 2025-09-09T09:31:05Z | |
| dc.date.available | 2025-09-09T09:31:05Z | |
| dc.date.copyright | 2023-08-09 | |
| dc.date.issued | 2023 | |
| dc.description.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> | |
| dc.description.accesstime | at_publication | |
| dc.description.bibliography | il., bibliogr. | |
| dc.description.finance | publication_nocost | |
| dc.description.financecost | 0,00 | |
| dc.description.if | 2,5 | |
| dc.description.number | 16 | |
| dc.description.points | 100 | |
| dc.description.version | final_published | |
| dc.description.volume | 13 | |
| dc.identifier.doi | 10.3390/app13169098 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/4686 | |
| dc.identifier.weblink | https://www.mdpi.com/2076-3417/13/16/9098 | |
| dc.language | en | |
| dc.relation.ispartof | Applied Sciences (Switzerland) | |
| dc.relation.pages | art. 9098 | |
| dc.rights | CC-BY | |
| dc.sciencecloud | nosend | |
| dc.share.type | OPEN_JOURNAL | |
| dc.subject.en | machine learning | |
| dc.subject.en | FTIR spectroscopy | |
| dc.subject.en | Lab color | |
| dc.subject.en | currant powders | |
| dc.subject.en | spray drying | |
| dc.subject.en | food technology | |
| dc.title | Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy - Case Study: Blackcurrant Powders | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 16 | |
| oaire.citation.volume | 13 |