Ropciuc Sorina, Dranca Florina, Pauliuc Daniela, Oroian Mircea
Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania.
Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 May 15;293:122459. doi: 10.1016/j.saa.2023.122459. Epub 2023 Feb 9.
The aim of this study was to evaluate the usefulness of emission-excitation matrices for honey authentication and adulteration detection. For this purpose, 4 types of authentic honeys (tilia, sunflower, acacia and rape) and samples adulterated with different adulteration agents (agave, maple, inverted sugar, corn and rice in different percentages - 5%, 10% and 20%) were analysed. Each honey type and each adulteration agent exhibit unique emission-excitation spectra that can be used for the classification according to the botanical origin and for the detection of adulteration. The principal component analysis clearly separated the rape, sunflower and acacia honeys. The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.
本研究的目的是评估发射-激发矩阵在蜂蜜鉴定和掺假检测中的效用。为此，分析了4种纯正蜂蜜（椴树蜜、向日葵蜜、刺槐蜜和油菜蜜）以及用不同掺假剂（龙舌兰、枫糖、转化糖、玉米和大米，不同比例——5%、10%和20%）掺假的样品。每种蜂蜜类型和每种掺假剂都呈现出独特的发射-激发光谱，可用于根据植物来源进行分类以及掺假检测。主成分分析清晰地分离出了油菜蜜、向日葵蜜和刺槐蜜。偏最小二乘判别分析（PLS-DA）和支持向量机（SVM）以二元模式用于将纯正蜂蜜与掺假蜂蜜分开，结果表明SVM的分离效果比PLS-DA好得多。
