Punta-Sánchez Irene, Dymerski Tomasz, Calle José Luis P, Ruiz-Rodríguez Ana, Ferreiro-González Marta, Palma Miguel
Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain.
Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G, Narutowicza Str., 80-233 Gdansk, Poland.
Sensors (Basel). 2024 Nov 23;24(23):7481. doi: 10.3390/s24237481.
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.
本文介绍了一种将超快速气相色谱法（UF-GC）与先进的机器学习技术相结合来检测蜂蜜掺假的新方法。应用机器学习模型，特别是支持向量回归（SVR）和最小绝对收缩和选择算子（LASSO）来预测橙花（OB）蜂蜜和向日葵（SF）蜂蜜中的掺假情况。对于混合蜂蜜类型，SVR模型的R值高于0.90。分别处理OB蜂蜜和SF蜂蜜可显著提高准确率，R值超过0.99。当单独处理蜂蜜类型时，LASSO被证明特别有效。UF-GC与机器学习的结合不仅为检测蜂蜜掺假提供了一种可靠的方法，也为该技术在其他食品中的应用的未来研究树立了先例，有可能提高整个行业食品的真实性。
