Object classification in analytical chemistry via data‐driven discovery of partial differential equations. Issue 4 (21st April 2021)

• Record Type: Journal Article
• Authors: Padgett, Joshua Lee; Geldiyev, Yusup; Gautam, Sakshi; Peng, Wenjing; Mechref, Yehia; Ibraguimov, Akif
• Is Part Of: Computational and mathematical methods. Volume 3:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-21
• Subjects: data‐driven PDE -- Einstein approach -- learning algorithms; Mathematics -- Data processing -- Periodicals; Numerical analysis -- Periodicals; Numerical analysis; Mathematics -- Data processing; Periodicals; 004.0151
• DOI: 10.1002/cmm4.1164
• Languages: English
• ISSNs: 2577-7408

Abstract

Abstract: Glycans are one of the most widely investigated biomolecules, due to their roles in numerous vital biological processes. However, few system‐independent, LC‐MS/MS (liquid chromatography tandem mass spectrometry) based studies have been developed with this particular goal. Standard approaches generally rely on normalized retention times as well as m/z‐mass to charge ratios of ion values. Due to these limitations, there is need for quantitative characterization methods which can be used independently of m/z values, thus utilizing only normalized retention times. As such, the primary goal of this article is to construct an LC‐MS/MS based classification of the glycans derived from standard glycoproteins and human blood serum using a glucose unit index as the reference frame in the space of compound parameters. For the reference frame, we develop a closed‐form analytic formula via the Green's function of a relevant convection‐diffusion‐absorption equation used to model composite material transport. The aforementioned equation is derived from an Einstein–Brownian motion paradigm, which provides a physical interpretation of the time‐dependence at the point of observation for molecular transport in the experiment. The necessary coefficients are determined via a data‐driven learning procedure. The methodology is presented in an abstractly and validated via comparison with experimental mass spectrometer data.