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Solving matrix effect, spectral overlapping and nonlinearity by generalized standard addition method coupled with radial basis functions–partial least squares: simultaneous determination of atorvastatin and amlodipine in urine
Author(s) -
ShariatiRad Masoud,
Irandoust Mohsen,
Amini Tayyebeh,
Shamsipur Mojtaba
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2491
Subject(s) - collinearity , partial least squares regression , analyte , mathematics , matrix (chemical analysis) , chemometrics , standard deviation , nonlinear system , linear least squares , least squares function approximation , chemistry , chromatography , statistics , linear model , physics , quantum mechanics , estimator
For simultaneous determination in conditions with spectral overlap and variation of matrix effects, coupling of the generalized standard addition method (GSAM) with the multivariate nonlinear method of radial basis function–partial least squares (RBF–PLS) was proposed. The nonlinearity caused by the GSAM used to correct matrix effects was studied, and principal component analysis was proposed for identifying it. In the method introduced, the whole sensor range can be used without the collinearity problem encountered in the application of GSAM with classical least squares (CLS), and calibration can be made for each analyte, separately. The introduced method was applied to determine amlodipine and atorvastatin in urine samples. The mean of the percent recoveries was between 95 and 101.12. The percent relative standard deviation values of the method were in most cases below 5%. The results of GSAM–RBF–PLS were compared with those obtained by GSAM–CLS and GSAM–PLS. Copyright © 2013 John Wiley & Sons, Ltd.