Multivariate calibration analysis of colorimetric mercury sensing using a molecular probe
| Autor: | Emilio Palomares, Javier Pérez-Hernández, Xavier Correig, Josep Albero, Eduard Llobet |
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| Rok vydání: | 2009 |
| Předmět: |
Multivariate statistics
Analyte Mean squared error Analytical chemistry chemistry.chemical_element Feature selection Sensitivity and Specificity Biochemistry Ruthenium Analytical Chemistry Partial least squares regression Environmental Chemistry Least-Squares Analysis Colorimetry Spectroscopy Models Statistical Aqueous solution Mercury Mercury (element) Models Chemical chemistry Molecular Probes Calibration Multivariate Analysis Algorithms |
| Zdroj: | Analytica Chimica Acta. 633:173-180 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/j.aca.2008.11.069 |
| Popis: | Selectivity is one of the main challenges of sensors, particularly those based on chemical interactions. Multivariate analytical models can determine the concentration of analytes even in the presence of other potential interferences. In this work, we have determined the presence of mercury ions in aqueous solutions in the ppm range (0–2 mg L −1 ) using a ruthenium bis-thiocyanate complex as a chemical probe. Moreover, we have analyzed the mercury-containing solutions with the co-existence of higher concentrations (19.5 mg L −1 ) of other potential competitors such as Cd 2+ , Pb 2+ , Cu 2+ and Zn 2+ ions. Our experimental model is based on partial least squares (PLS) method and other techniques as genetic algorithm and statistical feature selection (SFS) that have been used to refine, beforehand, the analytical data. In summary, we have demonstrated that the root mean square error of prediction without pre-treatment and with statistical feature selection can be reduced from 10.22% to 6.27%. |
| Databáze: | OpenAIRE |
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