Multivariate standard addition method solved by net analyte signal calculation and rank annihilation factor analysis.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Multivariate calibration methods are essential for analyzing complex samples.
• Standard addition is a common technique for matrix effect compensation.
• Developing robust methods for accurate analyte quantification is crucial.

Purpose of the Study:

• To introduce and evaluate two novel multivariate standard addition models: SANAS and SARAF.
• To demonstrate the application of the net analyte signal (NAS) concept and rank annihilation factor analysis (RAFA).
• To assess the accuracy and reliability of the proposed methods for analyte quantification.

Main Methods:

• Development of the SANAS model using a new subspace and NAS vector calculations.
• Implementation of the SARAF method involving iterative analyte contribution annihilation.
• Validation using simulated absorbance data and experimental analysis of indicators in synthetic matrices.

Main Results:

• Both SANAS and SARAF methods yielded accurate analyte concentration predictions.
• Relative errors of prediction were consistently below 5% for most analyzed samples.
• The methods successfully compensated for matrix effects in synthetic samples.

Conclusions:

• The SANAS and SARAF methods offer accurate and reliable approaches for multivariate analyte quantification.
• These methods provide a valuable alternative to traditional univariate calibration for complex samples.
• The NAS concept and RAFA are effective tools for developing advanced chemometric models.