Abstract
| Original language | English |
|---|---|
| Article number | 9390 |
| Journal | Scientific Reports |
| Volume | 7 |
| Issue number | 1 |
| Number of pages | 11 |
| ISSN | 2045-2322 |
| DOIs | |
| Publication status | Published - 2017 |
Cite this
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A systematic model identification method for chemical transformation pathways – the case of heroin biomarkers in wastewater. / Ramin, Pedram; Valverde Pérez, Borja; Polesel, Fabio; Locatelli, Luca; Plósz, Benedek G.
In: Scientific Reports, Vol. 7, No. 1, 9390 , 2017.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - A systematic model identification method for chemical transformation pathways – the case of heroin biomarkers in wastewater
AU - Ramin, Pedram
AU - Valverde Pérez, Borja
AU - Polesel, Fabio
AU - Locatelli, Luca
AU - Plósz, Benedek G.
PY - 2017
Y1 - 2017
N2 - This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.
AB - This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.
U2 - 10.1038/s41598-017-09313-y
DO - 10.1038/s41598-017-09313-y
M3 - Journal article
C2 - 28839237
VL - 7
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 9390
ER -