Discrimination on potential adulteration of honey by differential scanning calorimetry (DSC) and graph-based semi-supervised learning (GSSL)

文献类型： 外文期刊

第一作者： Huang, Kaiyue

作者： Huang, Kaiyue;Huang, Kaiyuan;Xu, Baojun;Huang, Kaiyue;Huang, Kaiyuan;Xu, Baojun;Bai, Tongyuan;He, Ping;Bai, Tongyuan;He, Ping;Xue, Xiaofeng;Huang, Kaiyuan

作者机构：

关键词： Honey adulteration; Differential scanning calorimetry (DSC); Principal component analysis (PCA); Graph-based semi-supervised learning (GSSL)

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 485 卷

页码：

收录情况： SCI

摘要： 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 (Delta Cp). 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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序