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

Document Type

Article

Publication Date

2-28-2003

Abstract

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. © 2002 Elsevier Science B.V. All rights reserved.

Identifier

0037469954 (Scopus)

Publication Title

Journal of Hazardous Materials

External Full Text Location

https://doi.org/10.1016/S0304-3894(02)00246-7

ISSN

03043894

PubMed ID

12573831

First Page

83

Last Page

97

Issue

1-3

Volume

97

Fund Ref

New Jersey Department of Environmental Protection

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