Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
Talanta. 2011 May 15;84(3):829-33. doi: 10.1016/j.talanta.2011.02.014. Epub 2011 Feb 24.
Two data fusion strategies (variable and decision level) combined with a multivariate classification approach (Partial Least Squares-Discriminant Analysis, PLS-DA) have been applied to get benefits from the synergistic effect of the information obtained from two spectroscopic techniques: UV-visible and (1)H NMR. Variable level data fusion consists of merging the spectra obtained from each spectroscopic technique in what is called "meta-spectrum" and then applying the classification technique. Decision level data fusion combines the results of individually applying the classification technique in each spectroscopic technique. Among the possible ways of combinations, we have used the fuzzy aggregation connective operators. This procedure has been applied to determine banned dyes (Sudan III and IV) in culinary spices. The results show that data fusion is an effective strategy since the classification results are better than the individual ones: between 80 and 100% for the individual techniques and between 97 and 100% with the two fusion strategies.
两种数据融合策略（变量和决策级）与多元分类方法（偏最小二乘判别分析，PLS-DA）相结合，从两种光谱技术（紫外可见和 1H NMR）获得的信息协同作用中获益。变量级数据融合包括将每种光谱技术获得的光谱合并到称为“元光谱”中，然后应用分类技术。决策级数据融合结合了在每种光谱技术中单独应用分类技术的结果。在可能的组合方式中，我们使用了模糊聚合连接算子。该程序已应用于确定烹饪香料中的禁用染料（苏丹红 III 和 IV）。结果表明，数据融合是一种有效的策略，因为分类结果优于单独的结果：对于单独的技术在 80%到 100%之间，而对于两种融合策略在 97%到 100%之间。
