Abstract
Abstract. The analysis of the shape of excitation-emission matrices (EEMs) is a relevant tool for exploring the origin, transport and fate of dissolved organic matter (DOM) in aquatic ecosystems. Within this context, the decomposition of EEMs is acquiring a notable relevance. A simple mathematical algorithm that automatically deconvolves individual EEMs is described, creating new possibilities for the comparison of DOM fluorescence properties and EEMs that are very different from each other. A mixture model approach is adopted to decompose complex surfaces into sub-peaks. The laplacian operator and the Nelder-Mead optimisation algorithm are implemented to individuate and automatically locate potential peaks in the EEM landscape. The EEMs of a simple artificial mixture of fluorophores and DOM samples collected in a Mediterranean river are used to describe the model application and to illustrate a strategy that optimises the search for the optimal output.
Highlights
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To test if the introduction of the asymmetric parameter, ri (Eq 2), into the Gaussian distribution helps to improve the model fit we modelled the tryptophan quinine sulfate (TQS) EEM assuming that peaks fit the classic Gaussian distribution as suggested by Westerhoff et al (2001)
Advances in our knowledge of dissolved organic matter (DOM) fluorescence properties in aquatic ecosystems strongly benefit from technological advances in data acquisition of fluorescence data
Summary
The local minimum of second derivative is used to identify the position of non-evident peaks in complex chromatograms (Stevenson et al, 2010). In complex surfaces the Nelder-Mead algorithm can be trapped in local minima (or maxima) that are very close to each other and, presumably, are identifying the same peak. Quinine sulfate shows two clear maxima at emission of ∼ 450 nm and a characteristic shoulder between these two peaks (Fig. 1a) In this EEM the identification of the local maxima of quinine sulphate and tryptophan is straightforward (Fig. 1a). The plot reveals the failure of the classical Gaussian distribution to fit reasonably well the spectra at λem < 450 nm and highlights that the introduction of an asymmetry factor in the Gaussian distribution improve notably the model goodness
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