Milli Mehmet, Söylemez Milli Nursel, Parlak İsmail Hakkı
Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
Scientific, Industrial and Technological Application and Research Center (SITARC), Bolu Abant Izzet Baysal University, Bolu, Turkey.
NPJ Sci Food. 2025 May 15;9(1):74. doi: 10.1038/s41538-025-00440-9.
Honey has long been an essential component of human nutrition, valued for its health benefits and economic significance. However, honey adulteration poses a significant challenge, whether by adding sweeteners or mixing high-value single-flower honey with lower-quality multi-flower varieties. Traditional detection methods, such as melissopalynological analysis and chromatography, are often time-consuming and costly. This study proposes an artificial intelligence-based approach using the BME688 gas sensor to detect honey adulteration rapidly and accurately. The sensor captures the gas composition of honey mixtures, creating a unique digital fingerprint that can be analysed using machine learning techniques. Experimental results demonstrate that the proposed method can detect adulteration with high precision, distinguishing honey mixtures with up to 5% resolution. The findings suggest that this approach can provide a reliable, efficient, and scalable solution for honey quality control, reducing dependence on expert analysis and expensive laboratory procedures.
长期以来，蜂蜜一直是人类营养的重要组成部分，因其对健康有益且具有经济意义而受到重视。然而，蜂蜜掺假构成了重大挑战，无论是添加甜味剂，还是将高价值的单花蜂蜜与质量较低的多花蜂蜜品种混合。传统的检测方法，如蜂蜜花粉分析和色谱分析，往往既耗时又昂贵。本研究提出了一种基于人工智能的方法，使用BME688气体传感器快速准确地检测蜂蜜掺假。该传感器捕获蜂蜜混合物的气体成分，创建一个独特的数字指纹，可使用机器学习技术进行分析。实验结果表明，所提出的方法能够高精度地检测掺假，分辨出分辨率高达5%的蜂蜜混合物。研究结果表明，这种方法可为蜂蜜质量控制提供可靠、高效且可扩展的解决方案，减少对专家分析和昂贵实验室程序的依赖。
