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 | |
| 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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Martinez, O., Dabarera, R., Premaratne, K., Kubat, M., & Englehardt, J. D. (2017). LFDA model for the assessment of water quality through Microtox® using excitation-emission matrices. Intelligent Data Analysis, 21(1), 181-203. https://doi.org/10.3233/IDA-150291