Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping A multivariate curve resolution--alternating least-squares approach.
Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.
Culzoni
María J
MJ
Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina.
Goicoechea
Héctor C
HC
Ibañez
Gabriela A
GA
Lozano
Valeria A
VA
Marsili
Nilda R
NR
Olivieri
Alejandro C
AC
Pagani
Ariana P
AP
D002138:Calibration; D007700:Kinetics; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D013057:Spectrum Analysis / Q000379:methods*