Hassan Norfarizah Hanim, Chong Ngee Sing, Yoon Tiem Leong, Wong Yong Foo
Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia,11800 Penang, Malaysia.
Department of Chemistry, Middle Tennessee State University, Murfreesboro, TN 37132, United States.
Food Chem. 2025 Sep 1;485:144398. doi: 10.1016/j.foodchem.2025.144398. Epub 2025 Apr 22.
This study presents a simple approach for detecting honey adulteration by integrating calorimetric data from differential scanning calorimetry (DSC) with machine learning classification (MLC) techniques, specifically using convolutional neural network (CNN) model alongside the Synthetic Minority Over-sampling TEchnique (SMOTE) for data augmentation. The thermal profiles of different honey varieties, sugar adulterants, and adulterated samples were acquired using DSC. Shifts in glass transition temperatures were observed in adulterated honey. The DSC data were analyzed using principal component analysis and MLC workflow. CNN model applied to original dataset reported accuracy of 24-67 %. However, integrating CNN model with SMOTE algorithm resulted in a significant accuracy improvement to 60-91 %. The integration of DSC with MLC provides a rapid and accurate method for detecting honey adulteration, demonstrating strong generalization capability. The proposed approach could facilitate the development of a framework to detect fraudulent practices, safeguarding honey industry and consumers from sugar-based adulterations.
本研究提出了一种通过将差示扫描量热法（DSC）的量热数据与机器学习分类（MLC）技术相结合来检测蜂蜜掺假的简单方法，具体而言，使用卷积神经网络（CNN）模型并结合合成少数过采样技术（SMOTE）进行数据增强。使用DSC获取了不同蜂蜜品种、糖掺假物和掺假样品的热谱图。在掺假蜂蜜中观察到玻璃化转变温度的变化。使用主成分分析和MLC工作流程对DSC数据进行了分析。应用于原始数据集的CNN模型报告的准确率为24%-67%。然而，将CNN模型与SMOTE算法相结合，显著提高了准确率，达到60%-91%。DSC与MLC的结合为检测蜂蜜掺假提供了一种快速准确的方法，具有很强的泛化能力。所提出的方法有助于开发一个检测欺诈行为的框架，保护蜂蜜行业和消费者免受基于糖的掺假行为的影响。
