An Optimized Retention Time Alignment Method for High-Speed Gas Chromatography

Abstract

The alignment of chromatograms collected on nominally identical columns using retention time locking (RTL), an instrumental alignment tool, and software-based alignment using correlation optimized warping (COW) is herein analyzed before a more optimized method combining both forms of alignment is described. To analyze the extent of misalignment and the success of corrective methods, three samples were constructed by spiking two sets of analytes into a base test mixture. The three samples were then analyzed by high-speed gas chromatography with four nominally identical columns and identical separation conditions, which due to unavoidable differences from manufacturing imprecision leads to four different sets of retention times. The data is first analyzed without alignment, then using COW alone, then RTL alone, and finally with RTL followed by COW. Principal component analysis (PCA) is used to investigate how well each alignment method allowed for clustering of the chromatograms into the three sample classes in a scores plot without being compromised by the specific column(s) used, where successful clustering and thus recovery of sample class differences as desired indicated successful alignment. Only the previously unreported method of combining RTL with COW achieved this degree of alignment. To provide a quantitative metric, the degree-of-class separation (DCS), measured as the Euclidian distance between the centroids of two clusters in PC space in the scores plot and normalized by their pooled variance, is used. With no alignment, the average DCS between sample classes (DCSsam) was 3.0, while the average DCS between the four nominally identical columns, i.e., column classes (DCScol) was 76.1 (ideally the DCScol should be 0), indicating the chromatograms were initially classified by the columns used. Using either COW or RTL alone also produced unsatisfactory results, with COW alone incorrectly aligned many peaks, leading to a DCSsam of only 1.9 and DCScol of 1.7, while RTL alone provided a DCSsam 4.7 of and DCScol of 4.2. Finally, using RTL followed by COW alignment, DCSsam increased to 32.5, indicating successful classification by chemical differences between sample classes, while the DCScol decreased to 0.4, indicating virtually no classification due to column-to-column differences, as desired. Thus, RTL provided a “first order” correction of the initial retention mismatch observed for the nominally identical columns, while additional alignment via COW was required to optimize sample classification by PCA.