Chemometric Resolution and Quantification of Four-Way Data Arising from Comprehensive 2D-LC-DAD Analysis of Human Urine

Abstract

Two-dimensional liquid chromatography (LC×LC) is quickly becoming an important technique for the analysis of complex samples, owing largely to the relatively high peak capacities attainable by this analytical technique. With the increase in the complexity of the sample comes a corresponding increase in the complexity of the collected data. Thus the need for chemometric methods capable of resolving and quantifying such data is ever more urgent in order to obtain the maximum information available from the data. To this end, we have developed a chemometric method that combines iterative key set factor analysis and multivariate curve resolution-alternating least squares analysis with a spectral selectivity constraint that is shown to be capable of resolving chromatographically rank deficient, non-multilinear data. (Spectrally rank deficient compounds can only be quantified if the peaks having the same spectra are chromatographically resolved.) Over 50 chromatographic peaks were found in a relatively small section of a LC×LC-diode array data set of replicate urine samples (a four-way data set) using the developed method. The relative concentrations for 34 of the 50 peaks were determined with % RSD values ranging from 0.09 % to 16 %.

Keywords: 2D liquid chromatography; alternating least squares; iterative key set factor analysis; multivariate curve resolution.

Figure 1

Several different types of plots for the 2^nd sample of the standards mixture raw data. (A) Plot of the two-way raw data as collected by the instrument, such that all 2^nd dimension injections of a corresponding sample injection are sequenced end-to-end. The insert shows an enlarged section of the sequenced data that was determined to encompass the corresponding peak illustrated in the contour plot at 216 nm of (B). Using the determined section dimensions, the same peak is shown as a contour plot at 216 nm in the box (C) along with its corresponding sequenced 2^nd dimension chromatograms.

Figure 2

Schematic illustration of the resolution results from the IKSFA-ALS-ssel analysis in which both the data structure and corresponding graphical representations of the resolved spectra and chromatograms are shown. The data structure as shown consists of J = 7 2^nd dimension slices and K samples (1^st dimension injections). Panel 1 illustrates the resolved spectral matrix. The dimensions of the resolved S^T data matrix in this example are four spectral components by the total number of wavelengths collected (L). The corresponding spectra for each of the four components are plotted such that the y-axis is relative intensity versus wavelength. Panel 2 illustrates the resolved chromatographic matrix. Recall that each 1^st chromatographic data point is equal to a 2^nd dimension slice and that a 2^nd dimension slice consists of I data points. The dimensions of the resolved C data matrix is unfolded to combine the 1^st chromatographic dimension (J), the 2^nd chromatographic dimension (I) and the number of samples (K) by the four spectral components. The four spectral components are color coordinated to correspond to their chromatographic counterpart. Each resolved chromatographic peak is graphically represented in two ways (1) by a contour plot of the 1^st chromatographic dimension by the 2^nd chromatographic dimension plotted for a given wavelength and a given sample and (2) a sequence of 2^nd dimension chromatograms. Panel 3 shows the corresponding sequence of 2^nd dimension chromatograms of component 1 for all samples (1-K).

Figure 3

Comparison of the subsection for the standards mixture peak 1 showing the sequence of resolved 2^nd dimension chromatograms and the raw data for the injection of six replicate samples onto the 1^st dimension column. (A) A plot of the sequenced second dimension chromatograms of the raw data. Each 1^st dimension sample injection gives rise to seven 2^nd dimension injections with four of those injections containing the peak of interest and three injections consisting only of the background in this example. (B) The above data after the application of the devoloped chemometric method. The red line drawn under each 2^nd dimension peak shows the manually determined baseline, and the areas for each of the second dimension peaks (shown at the top of the peaks) are totaled (shown at the bottom of each peak grouping), giving the relative concentrations of peak 1 for each of the six sample injections and the % RSD showing the precision of the quantification.

Figure 4

Contour plots at 216 nm of two different sample types within the 64 injection LC×LC-DAD run. (A). Contour plot of the third replicate injection of the standards mixture where peak 1 is indole-3-acetonitrile, peak 2 is indole-3-propionic acid, peak 3 is indole-3-acetic acid, peak 4 is tryptophan peak 5 is hydroxytryptophan and peak 6 is tyrosine. (B). Contour plot of the seventh replicate injection of the urine control standard. Inset shows the section of data selected for chemometric analysis.

Figure 5

Chemometric data analysis scheme used in the resolution and quantification of LC×LC data.

Figure 6

Contour plot of the urine control at 216 nm showing 34 resolved peaks. The N preceding 16 of the 34 resolved and quantified peaks signifies that those peaks were found and resolved only after application of the developed chemometric method (newly found) while the other 18 peaks were visually observable prior to chemometric analysis. The two bar graphs show % RSD values calculated for the corresponding peaks. The star on the visually observed peaks graph indicates that Peak 8 is considered to be a chemically unstable compound.

Figure 7

(A) Contour plot of a subsection of urine control data at 216 nm in which resolution and quantification of Peak N16 is the goal for the chemometric analysis. (B) The chromatographic and corresponding spectral results for each component of the 8 component IKSFA-ALS-ssel analysisl for the above subsection of raw data.

Figure 8

Overlaid contour plot of maize data analyzed by Porter et al. [3] The blue contour plot is the first injection of the mutant sample, the green contour plot is the indole standard mixture and the red contour plot in the second injection of the wild type sample. Adapted from ref. [3].

Figure 9

Component spectra (blue) and background spectra (red) before (A) and after (B) implementation of the spectral selectivity and spectral non-negativity constraints. By zeroing the chemical component spectra after 440 nm, the algorithm is better able to resolve the background spectra from the compound spectra.