LFDA model for the assessment of water quality through Microtox® using excitation-emission matrices

Oscar Martinez; Ranga Dabarera; Kamal Premaratne; Miroslav Kubat; James Douglas Englehardt

doi:10.3233/IDA-150291

LFDA model for the assessment of water quality through Microtox® using excitation-emission matrices

Oscar Martinez, Ranga Dabarera, Kamal Premaratne, Miroslav Kubat, James Douglas Englehardt

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	181-203
Number of pages	23
Journal	Intelligent Data Analysis
Volume	21
Issue number	1
DOIs	https://doi.org/10.3233/IDA-150291
State	Published - 2017

Keywords

fluorescence spectroscopy
Latent Dirichlet allocation
nonparametric estimation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Vision and Pattern Recognition
Artificial Intelligence

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title = "LFDA model for the assessment of water quality through Microtox{\textregistered} using excitation-emission matrices",

abstract = "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{\textregistered} 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{\textregistered} measurements. The final decision on Microtox{\textregistered} measurement is given using an evidence fusion mechanism. In general, the novel LFDA based graphical model can be utilized in analyzing two dimensional data.",

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AU - Martinez, Oscar

AU - Dabarera, Ranga

AU - Premaratne, Kamal

AU - Kubat, Miroslav

AU - Englehardt, James Douglas

PY - 2017

Y1 - 2017

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AB - 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.

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KW - Latent Dirichlet allocation

KW - nonparametric estimation

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LFDA model for the assessment of water quality through Microtox® using excitation-emission matrices

Abstract

Keywords

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