Estuarine water classification using EEM spectroscopy and PARAFAC–SIMCA

In the projection methods (PCA, PLS) two distance measures are of importance. They are the score distance (SD, a.k.a. leverage) and the orthogonal distance (OD, a.k.a. the residual variance). This paper shows that both distance measures can be modeled by the x 2 -distribution. Each model includes a scaling factor that can be described by an explicit equation. Moreover, the models depend on an unknown number of degrees of freedom, which have to be estimated using a training dataset. Such modeling is further applied to classification within the SIMCA framework, and various acceptance areas are built for a given significance level. A triangular area, constructed using the sum of the normalized SD and OD, is deemed to be the most practical. This theoretical notion is supported by three examples. The first is based on a simulated dataset, while the other two employ real world data.

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“…where L 0 is defined in Equation (5). Taking this into account one can obtain the following equality:…”

Section: The Orthogonal Distance (Od)mentioning

confidence: 95%

Acceptance areas for multivariate classification derived by projection methods

Pomerantsev

2008

Journal of Chemometrics

121

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“…The combination of EEM and PARA-FAC has been widely applied in the study of aquatic ecosystems, taking advantage of the natural fluorescence of chromophoric DOM. For instance, it has been used to characterize DOM in natural waters (Stedmon et al 2003;Cory and Mcknight 2005;Holbrook et al 2006), water soluble DOM in soil (Ohno and Bro 2006;Ohno et al 2007;Fellman et al 2008), to determine specific binding capacities of fluorescent components with trace metals , to classify water sources and their origin (Hall and Kenny 2007;Hua et al 2007), and in studies of the DOM dynamics in oceanic as well as coastal environments (Stedmon and Markager 2005;Stedmon et al 2007;Wedborg et al 2007;Murphy et al 2008;.…”

Section: Introductionmentioning

confidence: 99%

Characterizing humic substances from estuarine soils and sediments by excitation-emission matrix spectroscopy and parallel factor analysis

et al. 2009

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The determination of optical properties of organic matter using spectroscopic techniques is a powerful tool for the characterization of humic substances (HS) in soils and sediments because of sensitivity, specificity and sample throughput. However, basic spectroscopic techniques have practical limitations because of the similarity in the optical properties of many HS. To improve resolution, the combination of excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC) was applied for characterizing fulvic acid (FA) and humic acid (HA) fractions from soils and sediments of two estuarine environments in Spain. Five fluorescent components were identified by EEM-PARA-FAC and were found in both FA and HA fractions, consistent with the new paradigm of HS as supramolecular associations as well as the ubiquity of the HS components in the environment. Their contribution was, however, different between the FA and HA fractions. Two different, humic-like, fluorescent components were representative of FA and HA fractions, respectively. The spectral characteristics of these components were similar to previously reported PARAFAC components in dissolved organic matter (DOM) in a wide range of environments, suggesting their applicability in assessing OM quality and environmental dynamics. A microbial humic-like component was much more abundant in FA than in HA fractions. Furthermore, principal component analysis clarified that the two identified protein-like components, were enriched in sediment HA compared to soil HA, suggesting a larger contribution of refractory algaenan in sediment HA. The results of the present study demonstrate that EEM-PARAFAC is a useful technique for the biogeochemical characterization of soil and sedimentary HS.

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“…These multivariate approaches have been applied to fluorescence-based water research to detect the presence and quantify the underlying fluorescence characteristics of complex mixtures of DOM [98,125]. A number of studies have been carried out on fluorescent DOM with multivariate analysis techniques in marine water [126], estuarine water [96,127,128], fresh water [86,129] glacial systems [130,131] and, more recently, in drinking water [114,132] and sewage treatment [133], including process performance [134] as well as to elucidate changes in fluorescence throughout the treatment trains of a number of water recycling plants [135]. Another approach to the interpretation of EEM data is by fluorescence regional integration (FRI) -a method where the EEM is divided into separate regions as appropriate and the intensity area of each region is integrated for comparison [136].…”

Section: Identification Of Contamination Using Fluorescence Fingerprimentioning

confidence: 99%

Cross-connection detection in Australian dual reticulation systems by monitoring inherent fluorescent organic matter

Hambly

Henderson

Baker

et al. 2012

Environmental Technology Reviews

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New housing developments are now commonly incorporating dual reticulation water systems for the redistribution of recycled water back to households for non-potable use. Within such distribution systems is the potential for cross-connections between recycled and drinking water pipelines, and a number of such events have been documented both in Australia and internationally. While many cross-connections are unlikely to present a source of health concern given the high level of recycled water treatment, they do have the potential to negatively impact public confidence in dual reticulation systems. A rapid and highly sensitive method of cross-connection detection is required to increase consumer confidence in the construction and maintenance of such recycled water distribution systems. This paper reviews a number of current and potential cross-connection detection methods, highlighting the use of fluorescence spectroscopy as a highly promising analytical tool for portable cross-connection detection.

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Estuarine water classification using EEM spectroscopy and PARAFAC–SIMCA

Cited by 81 publications

References 21 publications

Acceptance areas for multivariate classification derived by projection methods

Acceptance areas for multivariate classification derived by projection methods

Characterizing humic substances from estuarine soils and sediments by excitation-emission matrix spectroscopy and parallel factor analysis

Cross-connection detection in Australian dual reticulation systems by monitoring inherent fluorescent organic matter

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