Huang Kaiyue, Huang Kaiyuan, Bai Tongyuan, Xue Xiaofeng, He Ping, Xu Baojun
Guangdong Provincial Key Laboratory IRADS and Department of Life Sciences, BNU-HKBU United International College, Zhuhai 519087, China.
Guangdong Provincial Key Laboratory IRADS and Department of Life Sciences, BNU-HKBU United International College, Zhuhai 519087, China; Zhuhai Guangdong - Hong Kong Food Safety Testing Co., Ltd, Zhuhai 519087, China.
Food Chem. 2025 Sep 1;485:144490. doi: 10.1016/j.foodchem.2025.144490. Epub 2025 Apr 22.
Honey is a valuable natural food product, prized for its nutritional and therapeutic properties. However, the widespread issue of honey adulteration, often involving the addition of plant-based syrups, poses significant challenges to global markets. This study utilized differential scanning calorimetry (DSC), a thermal-analytical technique, to characterize the thermal profiles of 43 honey samples, including both authentic and adulterated samples with high-fructose corn syrup (HFCS) and varying syrup concentrations. Principal component analysis (PCA) and graph-based semi-supervised learning (GSSL) were applied to classify the samples, achieving high accuracy. Results indicated that increasing adulteration levels led to higher water content and decreased glass transition temperature (Tg) and heat capacity difference (ΔCp). Furthermore, the established K-Nearest Neighbor (KNN) graph and Kullback-Leibler (KL) divergence effectively visualized relationships among samples. The integration of DSC with GSSL presents a cost-efficient and resource-effective approach for detecting honey adulteration with minimal experimental effort while maintaining high classification accuracy. This method holds promise for addressing honey adulteration in the food industry.
蜂蜜是一种珍贵的天然食品，因其营养和治疗特性而备受珍视。然而，蜂蜜掺假这一普遍问题，通常涉及添加植物性糖浆，给全球市场带来了重大挑战。本研究利用差示扫描量热法（DSC），一种热分析技术，来表征43个蜂蜜样品的热特性，包括纯正蜂蜜样品以及掺有高果糖玉米糖浆（HFCS）且糖浆浓度各异的掺假样品。主成分分析（PCA）和基于图的半监督学习（GSSL）被用于对样品进行分类，并取得了很高的准确率。结果表明，掺假程度的增加导致水分含量升高，玻璃化转变温度（Tg）和热容差（ΔCp）降低。此外，所建立的K近邻（KNN）图和库尔贝克-莱布勒（KL）散度有效地可视化了样品之间的关系。将DSC与GSSL相结合，提供了一种经济高效的方法，只需最少的实验工作量就能检测蜂蜜掺假，同时保持较高的分类准确率。该方法有望解决食品行业中的蜂蜜掺假问题。
