Comparison of spectral fluorescent signatures-based models to characterize DOM in treated water samples

Statistical procedures enable a multivariate analysis of the measurements to identify specific characteristics of the dissolved organic matter (DOM) fractions in raw natural water, including the concentrations. In this work, three already established models were used to predict the concentrations of fractions of DOM from spectral fluorescent signatures (SFSs): a general linear regression (GLR), loadings and scores of a principal components analysis (PCA), and a partial least squares regression (PLS). Details about the method undertaken to prepare the fractions were given. Water samples from surface water treatment plants in New Jersey were used for the testing. In all cases, PLS have shown much better biases and accuracies than GLR and PCA models. Hydrophilic neutral, however, showed poor performances (bias 33%) due to the isolation technique used. Recommendations were provided in order to improve the DOM characterization through SFS, which linked to PLS make a powerful and cost-effective surrogate parameter to characterize DOM.