Digital image colorimetry assisted by smartphone: A green approach for the analysis of reducing sugars in demerara sugar

The quantification of reducing sugars (RSs) in foods is essential for quality control, even in products with high sucrose content and low levels of RS, such as demerara sugar. Although sucrose is the primary component of this type of sugar, even minimal amounts of reducing sugars may indicate thermal or enzymatic degradation reactions, which can influence the product's sensory characteristics and stability. Accurate determination of these compounds is therefore crucial not only to ensure compliance with quality standards, but also to meet regulatory requirements and consumer expectations for more stable and safer products. In this context, the present study reports the development and validation of a low-cost, smartphone-based digital image colorimetry (DIC) method for RS quantification, with results expressed as % glucose (m/v). Twenty-two commercial samples from various Brazilian regions were analyzed through a colorimetric reaction with Benedict’s reagent under optimized conditions (1:1 reagent-to-sample volumetric ratio; 10 cm camera-to-sample distance), determined via ANOVA. Images were captured using a Samsung Galaxy S21 in a controlled light box. The analytical signal, derived from the red-to-green (R/G) channel intensity ratio in RGB space, showed a strong linear relation (R² = 98.9%) with glucose concentration in the 0.2–0.6% (m/v) range. The method was validated following ANVISA, AOAC, and ICH guidelines, demonstrating adequate selectivity (a significant matrix effect addressed by standard addition), trueness (recoveries between 94.9 and 100.1%), precision (RSD ≤ 1.11%, HorRat = 1.00), and limits (LOD = 0.04%, LOQ = 0.12%). A Blue Applicability Grade Index (BAGI) score of 72.5 confirmed the method’s practicality, highlighting its portability, minimal reagent use, and absence of pre-concentration steps. Despite limitations related to manual operation, the proposed method effectively quantified RS in all samples and is suitable for routine quality control, particularly in resource-constrained settings and for the analysis of foods with low RS contents.