Initialization effects in two-component second-order multivariate calibration with the extended bilinear model

Area of Science:

• Chemometrics
• Analytical Chemistry
• Multivariate Data Analysis

Background:

• Bilinear decomposition of augmented data matrices often suffers from rotational ambiguity, complicating profile recovery.
• Significant rotational ambiguity can reduce the utility of quantitative and qualitative information.
• While constraints can mitigate ambiguity, initial parameter estimation is crucial for multivariate curve resolution-alternating least-squares (MCR-ALS).

Purpose of the Study:

• To investigate the impact of initialization on two-component second-order multivariate calibration using the extended bilinear model.
• To determine if accurate analyte quantitation is achievable despite persistent rotational ambiguity.
• To provide data-driven guidelines for analytical chemists on managing rotational ambiguity and selecting initialization strategies.

Main Methods:

• Applied the extended bilinear model for two-component second-order multivariate calibration.
• Investigated the effect of different initialization strategies on profile resolution.
• Evaluated the 'purest variables' selection strategy for parameter estimation.

Main Results:

• The 'purest variables' initialization strategy can aid in correct profile resolution, contingent on the data direction.
• Demonstrated that accurate analyte quantitation is possible even with substantial rotational ambiguity.
• Identified specific conditions where initialization significantly impacts MCR-ALS outcomes.

Conclusions:

• Initialization strategy is a critical factor in resolving rotational ambiguity in bilinear decomposition.
• Effective initialization can lead to successful multivariate calibration protocols despite inherent data complexities.
• The study offers practical guidance for analytical chemists to navigate rotational ambiguity and optimize MCR-ALS analyses.