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

Abstract

Background

Multivariate calibration models in analytical chemistry often suffer from matrix effects due to variations in sample composition and instrumental conditions. These effects present a major challenge, often resulting in inaccurate predictions due to spectral differences and concentration mismatches between unknown samples and calibration datasets. Existing strategies, such as standard addition and local modeling, are limited in addressing both aspects simultaneously. There is a critical need for a systematic approach that enhances calibration model robustness by ensuring spectral similarity and concentration alignment, thereby improving prediction accuracy across diverse sample matrices.

Results

We developed a matrix-matching procedure using Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) to enhance the accuracy and robustness of multivariate calibration models. Spectral matching is assessed via net analyte signal (NAS) projections and Euclidean distance, isolating analyte and non-analyte contributions. Additionally, concentration matching is performed by evaluating the alignment of predicted concentration ranges between unknown samples and calibration sets, ensuring consistency across varying sample compositions. The method was rigorously validated using both simulated datasets and real-world analytical data, including near-infrared (NIR) spectra of corn and nuclear magnetic resonance (NMR) spectra of alcohol mixtures. In all tested scenarios, the matrix-matching procedure successfully identified optimal calibration subsets that minimized matrix effects. This approach led to substantially improved prediction performance by effectively reducing errors caused by spectral shifts, intensity fluctuations, and concentration mismatches, outperforming conventional calibration strategies in diverse and complex matrices.

Significance

This MCR-ALS-based matrix-matching framework enhances multivariate calibration by systematically selecting calibration sets that match spectrally and in concentration with unknown samples. By minimizing matrix-induced errors, it ensures robust and accurate predictions. Its versatility across analytical platforms and ability to handle diverse matrix effects make it a valuable tool for analytical chemistry, with potential for broad application in real-world analytical challenges.