Multi-level Data Fusion Strategies for Modeling Three-way Electrophoresis Capillary and Fluorescence Arrays Enhancing Geographical and Grape variety Classification of Wines,Analytica Chimica Acta

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Multi-level Data Fusion Strategies for Modeling Three-way Electrophoresis Capillary and Fluorescence Arrays Enhancing Geographical and Grape variety Classification of Wines
Analytica Chimica Acta ( IF 5.7 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.aca.2020.06.014
Rocío Ríos-Reina ₁ , Silvana M Azcarate ₂ , José M Camiña ₂ , Héctor C Goicoechea ₃

Affiliation

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

中文翻译：

三路电泳毛细管和荧光阵列建模的多级数据融合策略增强了葡萄酒的地理和葡萄品种分类

毛细管电泳与二极管阵列检测 (CE-DAD) 和多维荧光光谱 (EEM) 二阶数据被融合和化学计量处理，用于葡萄酒的地理和葡萄品种分类。对三向数据的多级数据融合策略进行了评估和比较，揭示了它们在分类环境中的优缺点。针对每个研究级别开发了基于一系列数据预处理和特征提取步骤的简单方法。偏最小二乘判别分析 (PLS-DA) 及其多路扩展 (NPLS-DA) 分别应用于 CE-DAD、EEM 和融合数据矩阵，分别构造为两路和三路阵列。通过平均灵敏度非错误率和平均精度等全局指标评估每个模型的分类结果。将融合矩阵的结果与使用单一矩阵的结果进行比较，观察到不同程度的改进，当数据融合级别增加时，已经证明了明显的好处，使用高级策略实现了最佳分类结果。

更新日期：2020-08-01

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