Abstract：
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.
摘要：
红糖是通过热加工获得的天然甜味剂，具有有趣的营养特征。然而,它具有显著的感官变异性，这直接影响到产品质量和消费者的选择。因此,开发快速的质量控制方法是可取的。这项工作提出了一种快速，环保,和同时分析蔗糖的准确方法，减少糖，红糖中的矿物质和ICUMSA颜色，使用一种创新的策略，将智能手机获得的数字图像处理与机器学习相结合。从数字图像中提取的数据，以及实验确定的物理化学特征和元素轮廓的含量是通过应用kNN算法构建预测回归模型所采用的变量。这些模型对Ca具有最高的预测能力，ICUMSA颜色，Fe和Zn,决定系数(R2)≥92.33%。观察到蔗糖的R2值较低（81.16％），还原糖(85.67%)，Mn(83.36%)和Mg(86.97%)。对于生成的所有预测模型,发现低数据离散度(RMSE<0.235)。AGREEMetric评估了绿色概况，并确定所提出的方法优于常规方法，因为它避免了使用溶剂和有毒试剂，消耗最少的能量，不会产生有毒废物，对分析师来说更安全。数字图像处理(DIP)和kNN算法的结合,非侵入性和可持续的分析方法。它简化并改善了红糖的质量控制，能够生产满足消费者需求和行业标准的甜味剂。
