Department of Food Science and Technology, Federal University of Santa Catarina, 88034-000 Florianopolis, SC, Brazil.
Department of Food Science and Technology, Federal University of Santa Catarina, 88034-000 Florianopolis, SC, Brazil.
Food Chem. 2021 May 1;343:128449. doi: 10.1016/j.foodchem.2020.128449. Epub 2020 Oct 22.
This study aimed to differentiate bracatinga (Mimosa scabrella Bentham) honeydew honey (BHH) from blossom honeys and BHH intentionally adulterated, all of them from three harvests, associating data of aliphatic organic acids (AOA) determined by capillary electrophoresis and chemometric analyses. The profile and concentration of AOA in pure BHH were similar between harvests, but distinct from blossom honeys. Succinic, glycolic, glutaric, malic, acetic, gluconic, and lactic acids were responsible for the differentiation between these two types of honey since they were the dominant variables (r > 0.80) in the principal component analysis. Based on this, the classification and regression trees method was used to develop a classification model considering these AOA. The proposed method needed only six of these AOA and adequately classified all blossom honeys and almost all pure and adulterated BHH. Therefore, the proposed model proved to be promising and reliable for verifying authenticity and fraud detection in BHH.
本研究旨在区分三季采集的巴西糖胶树蜜（Mimosa scabrella Bentham）蜜露蜜（BHH）与花蜜，以及蓄意掺假的 BHH，通过毛细管电泳和化学计量学分析确定的脂肪族有机酸（AOA）数据进行关联。纯 BHH 的 AOA 谱和浓度在各季之间相似，但与花蜜不同。琥珀酸、乙醇酸、戊二酸、苹果酸、乙酸、葡萄糖酸和乳酸是区分这两种蜂蜜的原因，因为它们是主成分分析中主要变量（r>0.80）。基于此，使用分类和回归树方法考虑这些 AOA 开发分类模型。所提出的方法仅需要其中的六种 AOA，就能充分区分所有花蜜和几乎所有纯 BHH 和掺假 BHH。因此，所提出的模型在验证 BHH 的真实性和检测欺诈方面具有良好的应用前景和可靠性。
