Matrix effect assessment via matrix matching strategy using multivariate curve resolution methods.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Multivariate calibration models face challenges from matrix effects, leading to inaccurate predictions.
• Variations in sample composition and instrumental conditions cause spectral differences and concentration mismatches.
• Existing methods struggle to simultaneously address spectral and concentration variations.

Purpose of the Study:

• To develop a systematic approach for enhancing multivariate calibration model robustness.
• To improve prediction accuracy across diverse sample matrices by minimizing matrix effects.
• To ensure spectral similarity and concentration alignment between unknown samples and calibration datasets.

Main Methods:

• Developed a matrix-matching procedure utilizing Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS).
• Assessed spectral matching using net analyte signal (NAS) projections and Euclidean distance.
• Performed concentration matching by evaluating the alignment of predicted concentration ranges.

Main Results:

• The MCR-ALS matrix-matching procedure successfully identified optimal calibration subsets, minimizing matrix effects.
• Demonstrated substantially improved prediction performance on simulated and real-world data (NIR, NMR).
• Effectively reduced errors from spectral shifts, intensity fluctuations, and concentration mismatches, outperforming conventional methods.

Conclusions:

• The MCR-ALS matrix-matching framework enhances multivariate calibration by selecting spectrally and concentration-matched calibration sets.
• Minimizing matrix-induced errors ensures robust and accurate predictions in analytical chemistry.
• The versatile method is valuable for diverse analytical platforms and real-world challenges.