A systematic study on the effect of different error structures on pseudo‐univariate and multivariate figures of merit

Abstract

AbstractIn this study, the effect of different error structures on psedounivariate and multivariate analytical figures of merit in simulated data of hyphenated chromatographic systems was investigated. Different error structures (e.g., homoscedastic, heteroscedastic, and correlated) were investigated. For this purpose, five components systems at five concentration levels with three replicates were simulated. Different types of error were added to the data. Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) and Maximum Likelihood Principal Component Analysis (MLPCA‐MCR‐ALS) methods were used. After resolution, pseudo‐univariate and multivariate analytical figures of merit were calculated and compared. As it expected, the detection limit for noisy datasets is higher than the noise‐free datasets, whereas the slopes of the calibration curves are not significantly different. Comparing the results generally showed that the detection limit values in multivariable mode were better than the univariate mode. The LODs of data (pseudo‐univariate and multivariate) with homoscedastic and correlated error structure by MCR‐ALS and MLPCA‐MCR‐ALS were the same. The analysis of data with heteroscedastic error structure by MLPCA‐MCR‐ALS had a lower detection limit than analysis with MCR‐ALS. The figures of merit obtained from WLS and OLS regression in heteroscedastic datasets were compared and better LODs were obtained after WLS method.