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dimensionality of the data and therefore enhance the efficiency of calibration. Calibration of
the fluorescence data with TOC values incorporated the use of partial least squares (PLS),
multiple linear regression (MLR), and neural network with back-propagation. All models
except PARAFAC-MLR produced consistent correlation coefficients for the validation data
set. This research is the first to conduct such comparative analysis of fluorescence data
modeling and addresses key issues regarding the suitability of different decomposition and
calibration methods for the analysis of fluorescence intensity data. In a follow on from this
work the same researchers (Bieroza et al.,
2011b
) have recently reported the application of
robust data mining techniques for the assessment of water treatment performance. Again
PCA and PARAFAC were used in addition to self-organizing maps. Bagoth et al. (2011)
developed and validated a seven-component PARAFAC model using 147 EEMs of water
samples obtained from two drinking water treatment plants. In this work NOM fractions
(humics, building blocks, neutrals, biopolymers, and low molecular weight acids) corre-
lated with maximum fluorescence intensities of seven PARAFAC components extracted
from the EEM data. This work concluded that the fluorescent components derived from
EEMs using PARAFAC can be related to defined NOM fractions, and therefore provide a
tool for evaluating the removal of defined NOM fractions during water treatment.
The interest in this area is growing substantially and it is envisaged that this particular
area of research will continue to emerge over the coming years such that it will impact con-
siderably on the operational management of water treatment works.
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