The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR
| cris.virtual.author-orcid | 0000-0001-7519-6085 | |
| cris.virtual.author-orcid | 0000-0002-2535-8370 | |
| cris.virtual.author-orcid | 0000-0002-1365-8130 | |
| cris.virtual.author-orcid | 0000-0001-5294-5928 | |
| cris.virtual.author-orcid | 0000-0003-1977-0622 | |
| cris.virtual.author-orcid | 0000-0001-6597-0858 | |
| cris.virtualsource.author-orcid | 755be52f-6095-4872-8816-5d2179651fb3 | |
| cris.virtualsource.author-orcid | 898dc715-0fc1-42af-a4d7-0bc909752fee | |
| cris.virtualsource.author-orcid | 8cd4c9a5-da42-46f1-bc14-052a2b2dec7d | |
| cris.virtualsource.author-orcid | a601ceea-78eb-4557-8764-5fdee917dd97 | |
| cris.virtualsource.author-orcid | acf4678f-330c-40e6-8139-12216a6bcbf8 | |
| cris.virtualsource.author-orcid | 08c06993-c96b-41bb-a5f9-551434fdd7df | |
| dc.abstract.en | In the era of technology development, the optimization of production processes, quality control and at the same time increasing production efficiency without wasting food, artificial intelligence is becoming an alternative tool supporting many decision-making processes. The work used modern machine learning and physical analysis tools to evaluate food products (pectins). Various predictive models have been presented to estimate the viscosity of pectin. Based on the physical analyses, the characteristics of the food product were isolated, including L*a*b* color, concentration, conductance and pH. Prediction was determined using the determination index and loss function for individual machine learning algorithms. As a result of the work, it turned out that the most effective estimation of pectin viscosity was using Decision Tree (R2 = 0.999) and Random Forest (R2 = 0.998). In the future, the prediction of pectin properties in terms of viscosity recognition may be significantly perceived, especially in the food and pharmaceutical industries. Predicting the natural pectin substrate may contribute to improving quality, increasing efficiency and at the same time reducing losses of the obtained final product. | |
| dc.affiliation | Wydział Nauk o Żywności i Żywieniu | |
| dc.affiliation.institute | Katedra Fizyki i Biofizyki | |
| dc.affiliation.institute | Katedra Mleczarstwa i Inżynierii Procesowej | |
| dc.article.number | 5877 | |
| dc.contributor.author | Siejak, Przemysław | |
| dc.contributor.author | Przybył, Krzysztof | |
| dc.contributor.author | Masewicz, Łukasz | |
| dc.contributor.author | Walkowiak, Katarzyna | |
| dc.contributor.author | Rezler, Ryszard | |
| dc.contributor.author | Baranowska, Hanna Maria | |
| dc.date.access | 2024-07-11 | |
| dc.date.accessioned | 2024-07-12T06:44:12Z | |
| dc.date.available | 2024-07-12T06:44:12Z | |
| dc.date.copyright | 2024-07-10 | |
| dc.date.issued | 2024 | |
| dc.description.abstract | <jats:p>In the era of technology development, the optimization of production processes, quality control and at the same time increasing production efficiency without wasting food, artificial intelligence is becoming an alternative tool supporting many decision-making processes. The work used modern machine learning and physical analysis tools to evaluate food products (pectins). Various predictive models have been presented to estimate the viscosity of pectin. Based on the physical analyses, the characteristics of the food product were isolated, including L*a*b* color, concentration, conductance and pH. Prediction was determined using the determination index and loss function for individual machine learning algorithms. As a result of the work, it turned out that the most effective estimation of pectin viscosity was using Decision Tree (R2 = 0.999) and Random Forest (R2 = 0.998). In the future, the prediction of pectin properties in terms of viscosity recognition may be significantly perceived, especially in the food and pharmaceutical industries. Predicting the natural pectin substrate may contribute to improving quality, increasing efficiency and at the same time reducing losses of the obtained final product.</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 | 3.3 | |
| dc.description.number | 14 | |
| dc.description.points | 100 | |
| dc.description.review | review | |
| dc.description.version | final_published | |
| dc.description.volume | 16 | |
| dc.identifier.doi | 10.3390/su16145877 | |
| dc.identifier.issn | 2071-1050 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/1586 | |
| dc.language | en | |
| dc.relation.ispartof | Sustainability | |
| dc.rights | CC-BY | |
| dc.sciencecloud | send | |
| dc.share.type | OPEN_JOURNAL | |
| dc.subject.en | pectin viscosity | |
| dc.subject.en | machine learning | |
| dc.subject.en | prediction | |
| dc.subject.en | color L*a*b* | |
| dc.subject.en | concentration | |
| dc.subject.en | conductance | |
| dc.subject.en | pH | |
| dc.title | The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 14 | |
| oaire.citation.volume | 16 | |
| project.funder.name | inne |