Contribution to second-order calibration based on multivariate curve resolution with and without previous...

Area of Science:

• Analytical Chemistry
• Chemometrics

Background:

• Rotational ambiguity in multivariate curve resolution - alternating least-squares (MCR-ALS) complicates analyte concentration estimation in second-order calibration.
• Non-trilinear data from liquid chromatography with spectral detection, exhibiting variable elution profiles, pose challenges for unique decomposition models.

Purpose of the Study:

• To compare the analytical performance of standard MCR-ALS with a novel approach using correlation optimized warping (COW) to address rotational ambiguity.
• To evaluate if synchronizing elution profiles via COW and re-applying MCR-ALS restores data trilinearity and improves accuracy.

Main Methods:

• Standard MCR-ALS analysis of augmented matrices from raw data.
• Correlation optimized warping (COW) to synchronize MCR-ALS elution profiles, followed by augmented matrix reconstruction and constrained MCR-ALS.

Main Results:

• The COW-based method (alternative 2) did not resolve rotational ambiguity and artificially modified data.
• Simulated systems showed lower average errors (approx. 2%) for standard MCR-ALS compared to COW-enhanced MCR-ALS (4-11%).
• Experimental systems yielded similar errors: 2-3% for standard MCR-ALS and 3-4% for COW-enhanced MCR-ALS.

Conclusions:

• The tested COW-based method offers no significant analytical improvement over standard MCR-ALS for non-trilinear data.
• Focus should remain on mitigating rotational ambiguity through chemically relevant constraints within MCR-ALS.