Automated Feature Mining for 2D-LC Analysis of Polymers

CAST members Bram van de Put and Stef Molenaar developed an algorithm to automate the feature mining for polymer analysis data generated using LC×LC-MS methods. The algorithm was based on mass remainder analysis.

The development of new materials requires a thorough understanding of the structure-property relationships at the molecular level of the polymers. In this light, comprehensive two-dimensional liquid chromatography (LC×LC) has been recognized as a promising technique. It is thus not surprising that several applications were developed to characterize various polymer systems [1], even including mass spectrometry (LC×LC-MS) [2].

However, while extremely powerful with respect to separation power, the data produced is also very complex. One of the challenges is therefore the interpretation of these highly complex datasets. The variety of instrument types and data formats limits the impact of these powerful separation methods and thus there is a demand to address this challenge.

Figure 1. The data produced with LC×LC-MS separation methods is highly complex. Reproduced with permission from [2].

To alleviate this challenge, CAST members MSc student Bram van der Put and PhD candidate Stef Molenaar developed a fast algorithm for automated feature mining of synthetic industrial homopolymers or perfectly alternating copolymers. The method processes the information in several stages, including pre-processing and clustering, charge-state deconvolution, mass remainder analysis and classification of end-group composition.

Figure 2. Deconvoluted polymer series encountered in the raw data shown in Figure 1A. Reproduced with permission from [2].

The computations of the algorithm were verified with LC×LC–MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. The resulting data was used to propose a chemical structure for each compositional series found.

The work was published open-access in Analytical Chemistry. You can read the publication for free here.

The project commenced under the supervision of Prof. Ron Peters (Covestro, University of Amsterdam) and Dr. Bob Pirok (University of Amsterdam) and with expertise from Dr. Jessica Desport (Universit of Amsterdam). The project was part of the UNMATCHED project, which is supported by BASF, Covestro, DSM, and Nouryon, and receives funding from the Dutch Research Council (NWO) in the framework of the Innovation Fund for Chemistry and from the Ministry of Economic Affairs in the framework of the “PPS-toeslagregeling.

References

[1] B.W.J. Pirok, D.R. Stoll, P.J. Schoenmakers, Anal. Chem. 91(1), 2019, 240-263, DOI: 10.1021/acs.analchem.8b04841
[2] G. Groeneveld, M. Dunkle, M. Rinken, A.F.G. Gargano, A. de Niet, M. Pursch, E.P.C. Mes, P.J. Schoenmakers, J. Chromatogr. A, 1569, 2018, 128–138, DOI: 10.1016/j.chroma.2018.07.054
[3] S.R.A. Molenaar, B. van de Put, J.S. Desport, S. Samanipour, R.A.H. Peters, B.W.J. Pirok, Anal. Chem.,
2022, 94, 5599-5607, DOI: 10.1021/acs.analchem.1c05336

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