Fluorescence spectroscopic excitation-emission matrices (EEMs) can be used to characterize dissolved organic matter in water with high sensitivity. Our goal is to predict the standard biosensor-based measurement Microtox® utilizing EEMs, pH, turbidity and conductivity, among other variables. EEMs have been modeled using novel latent fluorescent Dirichlet allocation (LFDA) based probabilistic graphical model. We found that nonparametric techniques offer a better mapping from, LFDA based EEMs scores and other measurements, to Microtox® measurements. The final decision on Microtox® measurement is given using an evidence fusion mechanism. In general, the novel LFDA based graphical model can be utilized in analyzing two dimensional data.
WuT. and EnglehardtJ.D., Mineralizing urban net-zero water management: Field experience for energy-positive water management, Water Research106 (2016), 352-363.
2.
GassieL., EnglehardtJ.D., WangJ., BrinkmanN., GarlandJ., GardinaliP. and GuoT., Mineralizing urban net-zero water treatment: Phase II field results and design recommendations, Water Research105 (2016), 496-506.
3.
StedmonC.A., MarkagerS. and BroR., Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy, Marine Chemistry82(3-4) (2003), 239-254.
4.
HudsonN., BakerA. and ReynoldsD., Fluorescence analysis of dissolved organic matter in natural, waste and polluted waters a review, River Research and Applications23(6) (Apr 2007), 631-649.
5.
OhnoT. and BroR., Dissolved organic matter characterization using multiway spectral decomposition of fluorescence landscapes, Soil Science Society of America Journal70(6) (2006), 2028-2037.
6.
HallG. and KennyJ., Estuarine water classification using eem spectroscopy and parafac-simca, Anal Chim Acta581(1) (2007), 118-124.
7.
HallG.J., ClowK.E. and KennyJ.E., Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis, Environmental Science and Technology39(19) (2005), 7560-7567.
8.
HuaB., VeumK., KoiralaA., JonesJ., ClevengerT. and DengB., Fluorescence fingerprints to monitor total trihalomethanes and n-nitrosodimethylamine formation potentials in water, Environmental Chemistry Letters5 (2007), 73-77.
9.
LakowiczJ.R., Principles of Fluorescence Spectroscopy, Springer, 2009.
10.
LawaetzA., BroR., Kamstrup-NielsenM., ChristensenI., JorgensenL. and NielsenH., Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer, Metabolomics8 (2012), 111-121.
11.
MadhuriS., ArunaP., Summiya BibiM.I., GowriV.S., KoteeswaranD. and GanesanS., Ultraviolet fluorescence spectroscopy of blood plasma in the discrimination of cancer from normal, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series2982 (May 1997), 41-45.
12.
UppalA., GhoshN., DattaA. and GuptaP., Fluorimetric estimation of the concentration of NADH from human blood samples, Biotechnology and Applied Biochemistry41(1) (Feb 2005), 43-47.
13.
LeinerM., SchaurR., DesoyeG. and WolfbeisO., Fluorescence topography in biology, III: Characteristic deviations of tryptophan fluorescence in sera of patients with gynecological tumors, Clin Chem32(10) (1986), 1974-1978.
14.
MadhuriS., SuchitraS., ArunaP., SrinivasanT. and GanesanS., Native fluorescence characteristics of blood plasma of normal and liver diseased subjects, Midical Science Research27(2) (Jan 1999), 635-639.
15.
MadhuriS., VengadesanN., ArunaP., KoteeswaranD., VenkatesanP. and GanesanS., Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy, Photochemistry and Photobiology78(2) (Aug 2003), 197-204.
16.
MasilamaniV., Al-ZhraniK., Al-SalhiM., Al-DiabA. and Al-AgeilyM., Cancer diagnosis by autofluorescence of blood components, Journal of Luminescence109 (Sep 2004), 143-154.
17.
BroR. and KiersH.A.L., A new efficient method for determining the number of components in PARAFAC models, J of Chemometrics17(5) (May 2003), 274-286.
18.
FarréM. and BarcelóD., Toxicity testing of wastewater and sewage sludge by biosensors, bioassays and chemical analysis, TrAC Trends in Analytical Chemistry22(5) (May 2003), 299-310.
19.
KaiserK.L., Correlations of Vibrio fischeri bacteria test data with bioassay data for other organisms, Environ Health Perspect106(2) (Apr 1998), 583-591.
20.
SmildeA., BroR. and GeladiP., Multi-Way Analysis: Applications in the Chemical Sciences, John Wiley & Sons, 2004.
21.
BroR., PARAFAC, tutorial and applications, Chemometrics and Intelligent Laboratory Systems38(2) (Oct 1997), 149-171.
22.
AcarE. and YenerB., Unsupervised multiway data analysis: A literature survey, Knowledge and Data Engineering, IEEE Transactions on21(1) (Jan 2009), 6-20.
23.
TuckerL.R., The extension of factor analysis to three-dimensional matrices, in: Contributions to Mathematical Psychology, GulliksenH. and FrederiksenN., eds, New York, Holt, Rinehart and Winston, 1964, pp. 110-127.
24.
HarshmanR., Foundations of the PARAFAC procedure: Models and conditions for an ``explanatory'' multi-modal factor analysis, UCLA Working Papers in Phonetics16 (Dec 1970).
25.
CattellR., Parallel proportional profiles and other principles for determining the choice of factors by rotation, Psychometrika9 (Dec 1944), 267-283.
MartinezO., DabareraR., PremaratneK. and KubatM., LDA-based probabilistic graphical model for excitation-emission matrices, International Journal of Intelligent Data Analysis19(5) (2015).
28.
RacineJ. and LiQ., Nonparametric estimation of regression functions with both categorical and continuous data, Journal of Econometrics119(1) (Mar 2004), 99-130.
29.
HendersonD.J. and ParmeterC.F., Applied Nonparametric Econometrics, Cambridge University Press, 2015.
30.
RacineJ. and LiQ., Cross-validation local linear nonparametric regression, Statistica Sinica14 (2004), 485-512.
31.
NadarayaE.A., On non-parametric estimates of density functions and regression curves, Theory of Probability & its Applications10(1) (Jul 1965), 186-190.
32.
WatsonG.S., Smooth regression analysis, Sankhyã: The Indian Journal of Statistics, Series26(4) (Dec 1964), 359-372.
33.
AndersenC.M. and BroR., Practical aspects of parafac modeling of fluorescence excitation-emission data, Journal of Chemometrics17(4) (Apr 2003), 200-215.
34.
ShaferG., A Mathematical Theory of Evidence, Princeton University Press, Princeton, 1976.
35.
WickramarathneT., PremaratneK., KubatM. and JayaweeraD., CoFiDS: A belief-theoretic approach for automated collaborative filtering, IEEE Transactions on Knowledge and Data Engineering23(2) (Feb 2011), 175-189.
36.
SmetsP., Practical uses of belief functions, in: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, ser, UAI'99, Morgan Kaufmann Publishers Inc. 1999, pp. 612-621.
37.
DemšarJ., Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research7 (2006), 1-30.
