A singular value decomposition-based background correction (SVD-BC) technique is proposed for the reduction of background contributions in online comprehensive two-dimensional liquid chromatography (LC × LC) data. The SVD-BC technique was compared to simply subtracting a blank chromatogram from a sample chromatogram and to a previously reported background correction technique for one dimensional chromatography, which uses an asymmetric weighted least squares (AWLS) approach. AWLS was the only background correction technique to completely remove the background artifacts from the samples as evaluated by visual inspection. However, the SVD-BC technique greatly reduced or eliminated the background artifacts as well and preserved the peak intensity better than AWLS. The loss in peak intensity by AWLS resulted in lower peak counts at the detection thresholds established using standards samples. However, the SVD-BC technique was found to introduce noise which led to detection of false peaks at the lower detection thresholds. As a result, the AWLS technique gave more precise peak counts than the SVD-BC technique, particularly at the lower detection thresholds. While the AWLS technique resulted in more consistent percent residual standard deviation values, a statistical improvement in peak quantification after background correction was not found regardless of the background correction technique used.
Bibliographical noteFunding Information:
The authors acknowledge financial support from NIH-GM-54585-13 (PWC & SCR) and NSF-CHE-0911330 (PWC &SCR), an ANPCyT-UNLP fellowship from Argentina (MF) and an Altria research fellowship (RCA) used to produce the results presented in this paper. The authors also would like to acknowledge Dr. Steven Reichenbach for providing to us a copy of his LC Image software and Dr. Paul Stevenson for his peak counting Mathematica script. We also wish to acknowledge funding from the Agilent Foundation and the gifts of columns from Agilent Technologies.
- Comprehensive two-dimensional chromatography
- Two-dimensional background correction
- Two-dimensional peak detection
- Two-dimensional quantification