Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
Department of Analytical Chemistry, University of Granada, c/Fuentenueva, s.n., E-18071 Granada, Spain.
Talanta. 2017 Aug 1;170:413-418. doi: 10.1016/j.talanta.2017.04.035. Epub 2017 Apr 13.
Data fusion combined with a multivariate classification approach (partial least squares-discriminant analysis, PLS-DA) was applied to authenticate the geographical origin of palm oil. Data fusion takes advantage of the synergistic effect of information collected from more than one data source. In this study, data from liquid chromatography coupled to two detectors -ultraviolet (UV) and charged aerosol (CAD)- was fused by high- and mid-level data fusion strategies. Mid-level data fusion combines a few variables from each technique and then applies the classification technique. Principal component analysis and interval partial least squares were applied to obtain the variables selected. High-level data fusion combines the PLS-DA classification results obtained individually from the chromatographic technique with each detector. Fuzzy aggregation connective operators were used to make the combinations. Prediction rates varied between 73% and 98% for the individual techniques and between 87% and 100% and 93% and 100% for the mid- and high-level data fusion strategies, respectively.
数据融合结合多元分类方法（偏最小二乘法判别分析，PLS-DA）被应用于鉴别棕榈油的地理来源。数据融合利用了从多个数据源收集的信息的协同效应。在这项研究中，来自两种检测器 - 紫外（UV）和带电气溶胶（CAD） - 的液相色谱的数据通过高低水平的数据融合策略进行融合。中水平数据融合结合了每种技术的少数几个变量，然后应用分类技术。主成分分析和区间偏最小二乘法用于获得所选变量。高水平数据融合将每个检测器各自获得的色谱技术的 PLS-DA 分类结果进行组合。模糊聚合连接运算符用于进行组合。对于个别技术，预测率在 73%到 98%之间，而对于中水平和高水平的数据融合策略，预测率分别在 87%到 100%和 93%到 100%之间。
