Publication
An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning
| dc.contributor.author | Alves, Vandressa | |
| dc.contributor.author | Santos, Jeferson M. dos | |
| dc.contributor.author | Viegas, Olga | |
| dc.contributor.author | Pinto, Edgar | |
| dc.contributor.author | Ferreira, Isabel M.P.L.V.O. | |
| dc.contributor.author | Lima, Vanderlei Aparecido | |
| dc.contributor.author | Felsner, Maria L. | |
| dc.date.accessioned | 2025-01-03T09:20:46Z | |
| dc.date.embargo | 2025-09 | |
| dc.date.issued | 2024-09 | |
| dc.description.abstract | Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Alves, V., dos Santos, J. M., Viegas, O., Pinto, E., Ferreira, I. M. P. L. V. O., Aparecido Lima, V., & Felsner, M. L. (2024). An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning. Food Research International, 191, 114673. https://doi.org/10.1016/j.foodres.2024.114673 | pt_PT |
| dc.identifier.doi | 10.1016/j.foodres.2024.114673 | pt_PT |
| dc.identifier.eissn | 1873-7145 | |
| dc.identifier.issn | 0963-9969 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/26950 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation | The authors thank the Brazilian Council for Scientific and Technological Development (CNPq), the Brazilian Coordination of Superior Level Staff Improvement (CAPES) and the Araucária Foundation for the Support of the Scientific and Technological Development of the State of Paraná for their financial support. Authors also thank Portuguese Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior through the project PTDC/SAU-NUT/6061/2020. | pt_PT |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0963996924007439?via%3Dihub | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Brown sugar | pt_PT |
| dc.subject | Quality control | pt_PT |
| dc.subject | Digital image processing | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Greenness metrics | pt_PT |
| dc.subject | Sustainable assessment | pt_PT |
| dc.title | An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Food Research International | pt_PT |
| oaire.citation.volume | 191 | pt_PT |
| person.familyName | Pinto | |
| person.givenName | Edgar | |
| person.identifier.ciencia-id | 271F-B7DF-8FAB | |
| person.identifier.orcid | 0000-0002-8021-4783 | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | eaf9fc86-1a1c-437f-adee-d28040aa7f2f | |
| relation.isAuthorOfPublication.latestForDiscovery | eaf9fc86-1a1c-437f-adee-d28040aa7f2f |