Mass-Remainder Analysis (MARA): a New Data Mining Tool for Copolymer Characterization

作者：Nagy Tibor; Kuki Akos; Zsuga Miklos; Keki Sandor^*

来源：Analytical Chemistry, 2018, 90(6): 3892-3897.

DOI：10.1021/acs.analchem.7b04730

A new data mining method is proposed for the determination of the copolymer composition from moderate/low resolution complex mass spectra. The Mass-remainder analysis (MARA) does not require a "Kendrick-like" transformation to a new mass scale, it is simply based on the calculation of the remainder after dividing by the exact mass of one of the repeat units of the copolymer (e.g., B of an A/B copolymer). Plotting the remainder of this division (MR) versus m/z the homologous series differing only by a number of base units (e.g., B unit) can be visualized. The number of A units (n(A)) and subsequently n(B) is assigned to the m/z peaks using the bijective n(A), MR mapping. Simultaneously, our algorithm removes the isotopes from the peak list. However, the intensities of the monoisotopes are increased to the value corresponding, approximately, to the total intensity of their isotope peaks. The correction of the mass spectral peak intensities enables the accurate calculation of the usual polymer and copolymer quantities: the molecular weight-average, the number-averaged molecular weight of A and B units, the composition drift, or the bivariate distribution, among others. Our Mass-remainder analysis method was demonstrated by the analysis of various ethylene oxide/propylene oxide copolymers.

• 出版日期2018-3-20