Title of article :
Number of species in complexation equilibria of o-, m- and p-CAPAZOXS with Cd2+, Co2+, Ni2+, Pb2+ and Zn2+ ions by PCA of UV–vis spectra
Author/Authors :
Meloun، نويسنده , , Milan and Syrov?، نويسنده , , Tom??، نويسنده ,
Issue Information :
ماهنامه با شماره پیاپی سال 2007
Abstract :
A critical comparison of the selected derivative principal component analysis (PCA) methods on the absorbance matrix data concerning the complexation equilibria between o-CAPAZOXS and Cd2+, Pb2+ and Zn2+ or m-CAPAZOXS and Cd2+, Co2+, Ni2+ and Zn2+ or p-CAPAZOXS and Cd2+ and Zn2+ at 25 °C is provided. As the number of complex species in a complex-forming equilibria mixture is an important step in spectral data treatment, the nine selected index functions for the prediction of the number of light-absorbing species that contribute to a set of spectra is critically tested by the PCA. An improved identification with the second SD(AE) or third derivative TD(AE) and derivative ratio function ROD(AE) for the average error criterion AE is preferred. After the number of various complexes formed the stability constants of species ML, ML2 (and ML3, respectively) type log β11, log β12 (and log β13, respectively) for the system of o-CAPAZOXS (ligand L) with the metals (the standard deviation s(log βpq) of the last valid digits is given in brackets) Cd2+ (6.39(5) and 11.51(9)), Pb2+ (4.24(2) and 9.01(2)) and Zn2+ (5.18(7) and 9.06(10)) and for the system of m-CAPAZOXS with Cd2+ (6.59(20) and 11.51(32)), Co2+ (7.19(6) and 12.19(8)), Ni2+ (7.64(7) and 13.39(12)) and Zn2+ (4.83(3) and 9.57(3)) and for the system of p-CAPAZOXS with Cd2+ (6.44(5), 10.99(10) and 14.57(25)) and Zn2+ (6.84(16), 13.05(29) and 18.74(43)) at 25 °C are estimated using SQUAD(84) nonlinear regression of the mole-ratio spectrophotometric data. The computational strategy is presented with goodness-of-fit tests and various regression diagnostics capable of proving the reliability of the chemical model proposed.
Keywords :
Number of species , number of components , Spectrophotometer error , Rank of matrix , Principal component analysis (PCA) , Factor analysis (FA)