Author(s): Alves, Vandressa ; Santos, Jeferson M. dos ; Pinto, Edgar ; Ferreira, Isabel M.P.L.V.O. ; Lima, Vanderlei Aparecido ; Felsner, Maria L.
Date: 2024
Persistent ID: http://hdl.handle.net/10400.22/25323
Origin: Repositório Científico do Instituto Politécnico do Porto
Subject(s): Brown sugar; Identity and quality standards; Sugar composition; Classification; Digital image processing; Color analysis
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.
