Abstract
The performance of different types of solutions of Beer's law was evaluated on a suite of synthetic uv-vis spectra generated at various degrees of random error. Solutions making use of absorbance matrices filtered from random noise were the most successful at retrieving the properties of chemical species at larger errors. Repeated automated simulations, however, indicated that such solutions display a number of occurrences associated with poor fits to the synthetic data. Solutions to Beer's law starting from the raw absorbance matrices including random errors produced more consistent sets of values of rather poorer quality. A bootstrap statistical analysis of the repeated simulations showed the median quality of the fit (e.g., sum-of-squares of the deviations) nonetheless to be far superior for solutions making use of error/noise filtration. These resulting absorbance matrices when reduced dimensionality, moreover, gave better values of the molar absorption coefficients and formation constant if solved numerically using orthogonal-triangular (QR) factorizations. These solutions are therefore recommended for the extraction of spectroscopic and thermodynamic/kinetic properties of chemical species from spectroscopic data, however, not without a rigorous validation of the model. All the solutions to Beer's law are provided in the computational language of Matlab 7.0 and may be readily implemented for any quantitative spectroscopic analyses.
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Boily, JF., Suleimenov, O.M. Extraction of Chemical Speciation and Molar Absorption Coefficients with Well-Posed Solutions of Beer's Law. J Solution Chem 35, 917–926 (2006). https://doi.org/10.1007/s10953-006-9035-z
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DOI: https://doi.org/10.1007/s10953-006-9035-z