Optimizing the process of reference selection for correlation optimised warping (COW) and interval correlation shifting (icoshift) analysis: automating the chromatographic alignment procedure. Issue 2 (13th December 2017) - Kumar, Keshav
Abstract : The present work introduces an automated procedure for generating the reference chromatogram for subsequent correlation optimised warping (COW) and interval correlation shifting (icoshift) analysis. Abstract : In order to ensure that the results of chromatographic data analysis workflow are well within the chemical and biological premise, the same chromatographic peak must be present at the same position for all the analysed samples. The correlation optimised warping (COW) and interval correlation shifting (icoshift) algorithm are the two most commonly used approaches that are used to correct the drifts in the peak position. Both the approaches work with different algorithms, COW works on expansion and compression approach whereas the icoshift works on an insertion and deletion approach. Both the approaches have their pros and cons. However, both the methods suffer with the problems associated with the selection of the proper reference sample. There are several ways of selecting a reference sample but none of them provides an unambiguous choice. Often, the selection of an inappropriate sample as the reference makes the whole alignment exercise a laborious task with very little success in correcting the drifts in peak position. An unambiguous reference selection procedure is the current prime requirement in the areas of proteomics, metabolomics, and the food industry that use chromatography-based analytical procedures. The present work addresses this issue by taking the advantage of the fact that chromatographic peaks can be approximated using the Gaussian function and proposes an approach that involves synthesis of a reference chromatogram using the Gaussian function for the subsequent COW and icoshift analysis. The proposed approach is successfully validated using the simulated as well as real life chromatograms and the results obtained are evaluated by means of several statistical parameters. The results clearly show that the proposed approach can reduce the ambiguity in the selection of a reference chromatogram and can speed up and automate the whole alignment procedure.