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. (20th February 2013)

• Record Type: Journal Article
• Authors: Shariati‐Rad, Masoud; Irandoust, Mohsen; Amini, Tayyebeh; Shamsipur, Mojtaba
• Is Part Of: Journal of chemometrics. Volume 27:Number 3/4(2013:Apr.)
• Page Start: 63
• Page End: 69
• Publication Date: 2013-02-20
• Subjects: generalized standard addition method -- radial basis function–partial least squares -- matrix effect -- nonlinearity -- spectral interferences -- urine; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2491
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract : 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. Abstract : Nonlinearity in multivariate data was identified by principal component analysis. Generalized standard addition data were processed by radial basis functions–partial least squares for solving matrix effects, spectral interferences and nonlinearity in multivariate calibration.