Abstract This paper uses fluorescence spectroscopy in order to evaluate the potential of this technique to classify Argentinean yerba mate from three commercial categories. An extraction method based on ultrasound for sample preparation is proposed. Acquired fluorescence first- and second-order data were analyzed by different chemometric approaches. Aimed at enhancing data interpretability and to reduce redundant information among the retained variables and maximize their relationship regarding the sample, several variable selection algorithms were evaluated. Two three-way algorithms, PARAFAC and n-PLS, were applied to EEMs. Several two-way algorithms such as PCA, SPA, GA, PLS were used previous to model data gathered from emission spectra recorded at the 410 nm excitation and unfolded EEMs. Sample categorization was performed using LDA or QDA as classification techniques. The ability of the models to predict samples was evaluated based on sensitivity and specificity. First-order models reached 89.6% on sensitivity and 90.9% on specificity. Second-order models achieved up to 100% sensitivity and specificity demonstrating that the EEM matrix provides relevant additional information revealing a significant effect on the classification results.

中文翻译：

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阿根廷马黛茶（Ilex paraguariensis）分类的荧光光谱应用评估一阶和二阶数据结构特性

摘要 本文使用荧光光谱来评估该技术从三个商业类别中对阿根廷马黛茶进行分类的潜力。提出了一种基于超声波的样品制备提取方法。通过不同的化学计量学方法分析获得的荧光一阶和二阶数据。为了增强数据的可解释性并减少保留变量之间的冗余信息并最大化它们与样本的关系，评估了几种变量选择算法。两种三向算法，PARAFAC 和 n-PLS，被应用于 EEM。之前使用了几种双向算法，例如 PCA、SPA、GA、PLS，对从 410 nm 激发和未折叠 EEM 记录的发射光谱收集的数据进行建模。使用 LDA 或 QDA 作为分类技术进行样本分类。基于敏感性和特异性评估模型预测样本的能力。一阶模型的敏感性达到 89.6%，特异性达到 90.9%。二阶模型实现了高达 100% 的灵敏度和特异性，证明 EEM 矩阵提供了相关的附加信息，揭示了对分类结果的显着影响。