Semi-automated alignment and quantification of peaks using parallel factor analysis for comprehensive two-dimensional liquid chromatography–diode array detector data sets

Abstract

Parallel factor analysis was used to quantify the relative concentrations of peaks within four-way comprehensive two dimensional liquid chromatography-diode array detector data sets. Since parallel factor analysis requires that the retention times of peaks between each injection are reproducible, a semi-automated alignment method was developed that utilizes the spectra of the compounds to independently align the peaks without the need for a reference injection. Peak alignment is achieved by shifting the optimized chromatographic component profiles from a three-way parallel factor analysis model applied to each injection. To ensure accurate shifting, components are matched up based on their spectral signature and the position of the peak in both chromatographic dimensions. The degree of shift, for each peak, is determined by calculating the distance between the median data point of the respective dimension (in either the second or first chromatographic dimension) and the maximum data point of the peak furthest from the median. All peaks that were matched to this peak are then aligned to this common retention data point. Target analyte recoveries for four simulated data sets were within 2 % of 100 % recovery in all cases. Two different experimental data sets were also evaluated. Precision of quantification of two spectrally similar and partially coeluting peaks present in urine was as good as or better than 4 %. Good results were also obtained for a challenging analysis of phenytoin in waste water effluent, where the results of the semi-automated alignment method agreed with the reference LC-LCMS/MS method within the precision of the methods.

Two dimensional assisted liquid chromatography – a chemometric approach to improve accuracy and precision of quantitation in liquid chromatography using 2D separation, dual detectors, and multivariate curve resolution

“…This can be done in many different ways including simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) [19], iterative key set factor analysis (IKSFA) [20], and the orthogonal projection approach (OPA) [21]. The method used in this paper is similar to Allen and Rutan's approach [22], in which OPA is modified to proceed in an iterative fashion. Briefly, OPA is performed by finding the most dissimilar spectra in the raw data by first finding the most different spectrum from the mean of all of the spectra.…”

“…This continues until a user-specified number of spectra are found [21]. Iterative OPA (IOPA) adds an iterative step after the initial OPA, inspired by IKSFA [22,23]. For the first iteration, the first spectrum in the initial guess is replaced by each spectrum in the raw data.…”

Third-order/Four-way Calibration and Beyond

“…These matrices could then be arranged into an augmented matrix, as already explained for second-order data in Chapters 2 and 8, taking the combined temporal mode as the augmentation mode, and the spectral mode as the nonaugmented one. 18 14 The superaugmented matrix behaves in a similar manner to the augmented matrices described in Chapters 2 and 8, in the sense that bilinear decomposition is possible by suitable initialization and application of natural constraints.…”

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

“…These methods consider the variations of all available channels as, for instance, the method proposed by Reichenbach or the recent RT shift correction algorithm proposed by Zushi . In addition, there are alternatives considering approximations based on carrying out a resampling by interpolation of the first chromatographic dimension …”