A comparison of orthogonal signal correction and net analyte preprocessing methods. Theoretical and experimental study

Abstract A comparison is presented between orthogonal signal correction (OSC) and net analyte signal (NAS) calculations. It was shown that the latter can be used as a preprocessing method comparable to the former, before the application of partial least-squares (PLS) to the filtered data. When the number of factors used in the net analyte preprocessing (NAP) procedure increases, the subsequent application of PLS requires progressively less factors, a behavior comparable to OSC. If enough factors are extracted by either NAP or OSC methods, the remaining calibration problem is amenable to a classical least-squares solution, giving rise to two multivariate calibration methods named NAP/CLS and OSC/CLS. All methods are illustrated from cross-validation and external validation results for two experimental examples: (1) the determination of the antibiotic tetracycline in human serum, and (2) the quantitation of the nasal decongestant naphazoline in multicomponent pharmaceutical solutions.

[1]  I Itzkan,et al.  An enhanced algorithm for linear multivariate calibration. , 1998, Analytical chemistry.

[2]  Nicolaas M. Faber,et al.  Exact presentation of multivariate calibration model as univariate calibration graph , 2000 .

[3]  Avraham Lorber,et al.  Net analyte signal calculation in multivariate calibration , 1997 .

[4]  A. Olivieri,et al.  Enhanced synchronous spectrofluorometric determination of tetracycline in blood serum by chemometric analysis. Comparison of partial least-squares and hybrid linear analysis calibrations. , 1999, Analytical chemistry.

[5]  Israel Schechter,et al.  A Calibration Method Free of Optimum Factor Number Selection for Automated Multivariate Analysis. Experimental and Theoretical Study , 1997 .

[6]  Tom Fearn,et al.  On orthogonal signal correction , 2000 .

[7]  F. Rius,et al.  Detection and correction of biased results of individual analytes in multicomponent spectroscopic analysis. , 1998, Analytical chemistry.

[8]  Darren T. Andrews,et al.  Maximum Likelihood Multivariate Calibration , 2022 .

[9]  A. Olivieri,et al.  Simultaneous spectrophotometric-multivariate calibration determination of several components of ophthalmic solutions: phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal. , 2000, Talanta.

[10]  A. Olivieri,et al.  Determination of bromhexine in cough-cold syrups by absorption spectrophotometry and multivariate calibration using partial least-squares and hybrid linear analyses. Application of a novel method of wavelength selection. , 1999, Talanta.

[11]  A. Lorber.  Error propagation and figures of merit for quantification by solving matrix equations , 1986 .

[12]  Héctor C. Goicoechea,et al.  MULTIVAR. A program for multivariate calibration incorporating net analyte signal calculations , 2000 .

[13]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[14]  Olof Svensson,et al.  An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra , 1998 .

[15]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[16]  Alejandro C. Olivieri,et al.  Wavelength selection by net analyte signals calculated with multivariate factor-based hybrid linear analysis (HLA). A theoretical and experimental comparison with partial least-squares (PLS) , 1999 .

[17]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .