Estuarine water classification using EEM spectroscopy and PARAFAC–SIMCA

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 (SIMCA) 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.