Parallel vector analysis based soft resolution of spectral data from pH-metric titration using symmetry constraint
✍ Scribed by Mohsen Kompany-Zareh; Somayeh Gholami
- Book ID
- 102793298
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 515 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0886-9383
- DOI
- 10.1002/cem.1255
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✦ Synopsis
In this study, a soft method is proposed to calculate concentration and spectral profiles for the two-way spectral data from dissociation equilibria of polyprotic acids (H n A). This method has four main distinct steps: (i) a fixed size moving window evolving factor analysis (FSMWEFA) was used to identify the local rank map, (ii) WFA was applied to calculate the concentration profiles of H n A and A nS (selection of the window for application of WFA was performed using EFA), (iii) PVA was used to calculate H n S 1 A to HA spectral profiles, and (iv) a symmetry constraint, in addition to the non-negativity constraint, was utilized to obtain the unique concentration and spectral profiles from different acceptable sets of profiles. In the absence of any selective region in the spectral data, the proposed soft method resulted in unique solution without rotational ambiguity. This study is the first application of symmetry constraint on concentration profiles. The rotational ambiguity drastically decreased on considering the constraint of symmetry of the H n S 1 A and HA concentration profiles, in addition to non-negativity of profiles. Simulated examples were used to confirm these approaches. Effect of closeness of dissociation constants on the estimated values of constants was investigated. The results showed that when the difference between pK a values is more than 1.2, the obtained errors in the estimation of pK a values are less than about 6.5%. The considered real data were from pH-metric titration of fluorescein. The obtained spectral and concentration profiles and the estimated pK a values for fluorescein were in good agreement with the previously reported data.