An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Alves V. (Autor) Outra; dos Santos J.M. (Autor) Outra; Viegas, Olga (Autor) FCNAUP; Pinto E. (Autor) Outra; Ferreira I.M.P.L.V.O. (Autor) Outra; Aparecido Lima V. (Autor) Outra; Felsner M.L. (Autor) Outra

Revista

Título: Food Research International

Vol. 191

ISSN: 0963-9969

Editora: Elsevier

Indexação

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências da Saúde

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-010-QM6

DOI: 10.1016/j.foodres.2024.114673

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica