Mara Andrea, Migliorini Matteo, Ciulu Marco, Chignola Roberto, Egido Carla, Núñez Oscar, Sentellas Sònia, Saurina Javier, Caredda Marco, Deroma Mario A, Deidda Sara, Langasco Ilaria, Pilo Maria I, Spano Nadia, Sanna Gavino
Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy.
Foods. 2024 Jan 12;13(2):243. doi: 10.3390/foods13020243.
Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.
基于地理来源对蜂蜜进行区分是一种常见的欺诈行为，也是蜂蜜真伪鉴定中研究最多的课题之一。本研究旨在通过结合元素指纹图谱和机器学习技术，根据蜂蜜的地理来源对其进行区分。特别是，本研究的主要目标是区分在相邻地区生产的单花蜂蜜和多花蜂蜜的来源，如撒丁岛（意大利）和西班牙。使用电感耦合等离子体质谱法（ICP-MS）测定了247种蜂蜜的元素组成。利用主成分分析（PCA）、线性判别分析（LDA）和随机森林（RF）对蜂蜜的来源进行了区分。与LDA相比，RF表现出更高的稳定性和更好的分类性能。最佳分类基于地理来源，使用钠、镁、锰、锶、锌、铈、钕、铕和铽作为预测因子，准确率达到90%。
