Estuarine water classification using EEM spectroscopy and PARAFAC-SIMCA
Estuarine water classification using EEM spectroscopy and PARAFAC-SIMCA
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DOI：
10.1016/j.aca.2006.08.034
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发表时间：
2007-01-02
影响因子：
6.2
通讯作者：
Kenny, Jonathan E.
中科院分区：
文献类型：
--
作者：
Hall, Gregory J.;Kenny, Jonathan E.
防止非本地水生公害物种引入美国的主要方法是压载水交换（BWE）。我们最近的工作重点是使用有色溶解有机物 (CDOM) 的激发发射矩阵 (EEM) 光谱来“指纹”水作为其来源港的函数，从而为 BWE 法规的执行提供取证工具。在这项工作中，我们利用 N 路偏最小二乘和判别分析 (NPLS-DA)，对数据进行建模，重点关注类别（始发港）之间的差异。在这项工作中，通过并行因子分析（PARAFAC）结合类比软独立建模（SINICA）对来自三个不同美国港口的样本的EEM进行了分析，以提供一种误报率低的有效分类方法。这项工作中首次展示的这种耦合可以成为 NPLS-DA 的有用替代方案，因为 PARAFAC-SIMCA 将 EEM 信号分解为化学成分，并在分类方案中利用这些成分的分数。这使用户可以选择在分类之前消除干扰或无法识别的荧光成分的贡献。 (c) 2006 Elsevier B.V. 保留所有权利。
The primary method for the prevention of the introduction of nonindigenous aquatic nuisance species in the U.S. is ballast water exchange (BWE). Our recent work focused on the use of the excitation emission matrix (EEM) spectroscopy of the colored dissolved organic matter (CDOM) to "fingerprint" water as a function of its port of origin, and therefore provide a forensic tool for the enforcement of BWE regulations. In that work, we utilized N-way partial least squares with discriminant analysis (NPLS-DA), which models the data with an emphasis on differences among classes (ports of origin). In this work, EEMs of samples from three different U.S. ports were analyzed by parallel factor analysis (PARAFAC) coupled with soft independent modeling of class analogy (SINICA) to provide an effective classification method with a low false positive rate. This coupling, which is shown for the first time in this work, can be a useful alternative to NPLS-DA in that PARAFAC-SIMCA decomposes the EEM signal into chemical components and utilizes the scores for these components in the classification scheme. This gives the user the option of removing the contributions of interfering or unidentifiable fluorescent components prior to classification. (c) 2006 Elsevier B.V. All rights reserved.