National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath, 400293 Cluj-Napoca, Romania.
Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, 11 Arany Janos, 400028 Cluj-Napoca, Romania.
Molecules. 2020 Oct 26;25(21):4955. doi: 10.3390/molecules25214955.
Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.
葡萄酒数据通常具有高变异性，表现在化合物和浓度范围方面。化学计量学方法可以有效地用于提取和利用这些数据中包含的有意义的信息。因此，本研究中高效应用了模糊划分分层关联聚类（FDHAC）方法，用于根据元素分布（通过 ICP-MS 分析的 30 种元素的浓度）对几种罗马尼亚白葡萄酒进行分类。所研究的葡萄酒产自罗马尼亚的四个不同地理区域（特兰西瓦尼亚、摩尔多瓦、瓦拉几亚和奥尔特尼亚）。FDHAC 算法不仅提供了所研究白葡萄酒的模糊划分，还提供了所考虑特征的模糊划分。此外，该方法是独特的，因为它允许对应于葡萄酒样本和元素的成员资格度的 3D 双标绘图表示。通过这种方式，可以确定每个葡萄酒样本模糊分区（组）中最具特异性的元素（就最高、最低或中间浓度值而言）。对于区分在不同罗马尼亚地区生产的葡萄酒，出现的最有力的化学元素是：K、Rb、P、Ca、B、Na。
