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

• Published in: Food research international Vol. 191; p. 114673
• Main Authors: Alves, Vandressa, dos Santos, Jeferson M., Viegas, Olga, Pinto, Edgar, Ferreira, Isabel M.P.L.V.O., Aparecido Lima, Vanderlei, Felsner, Maria L.
• Format: Journal Article
• Language: English
• Published: Canada Elsevier Ltd 01.09.2024
• Subjects: Brown sugar; Digital image processing; Greenness metrics; Machine learning; Quality control; Sustainable assessment; Sustainable assessment; Brown sugar; Greenness metrics; Quality control; Machine learning; Digital image processing
• ISSN: 0963-9969; 1873-7145; 1873-7145
• DOI: 10.1016/j.foodres.2024.114673

[Display omitted] •The chemical composition of brown sugar influences consumer preferences and quality.•Smartphone images of brown sugar were analysed with computer vision and kNN models.•Models predict elements and ICUMSA colour excellently, with moderate accuracy for sugars.•DIP–kNN enables fast, eco-friendly, and accurate analysis of brown sugar composition.•DIP–kNN method streamlines the quality control of sugar and meets the standards. 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.