Browsing by Author "Felsner, Maria L."
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- An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learningPublication . Alves, Vandressa; Santos, Jeferson M. dos; Viegas, Olga; Pinto, Edgar; Ferreira, Isabel M.P.L.V.O.; Lima, Vanderlei Aparecido; Felsner, Maria L.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.
- Digital image processing combined with machine learning: A new strategy for brown sugar classificationPublication . Alves, Vandressa; Santos, Jeferson M. dos; Pinto, Edgar; Ferreira, Isabel M.P.L.V.O.; Lima, Vanderlei Aparecido; Felsner, Maria L.The coloring of foods is one of the main attributes of importance for consumers and it can be decisive for a consumer to accept or reject the product. Models that explore brown sugar coloring are scarce in scientific research. So, a new strategy for brown sugar classification through the combination of digital image processing, machine learning and physicochemical composition data was proposed. RGB channel intensities and color histogram data, obtained from digital image processing, in combination with some physicochemical characteristics (sucrose, Ca, Fe, ICUMSA color and total phenolic compounds (TPC)) were used as training and external validation datasets in the creation of classification models by RF algorithm. Excellent performance of classification models was observed by high overall accuracy rates for ICUMSA color (92.6 %), Ca and sucrose (100 %), Fe (94.9 %), and TPC (97.6 %). Thus, classifying brown sugar based on its color can be a valuable strategy for the beverage and food industries, allowing for greater diversification and meeting consumer needs while enhancing the quality and consistency of products.