Spectral fluorescence signatures and partial least squares regression: Model to predict dissolved organic carbon in water

Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028mg/l and a root mean squared error of prediction (RMSEP) of 0.35mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.