Mass-Remainder Analysis (MARA): An Improved Method for Elemental Composition Assignment in Petroleomics

Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography–mass spectrometry data (LC × LC–MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (i.e., the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC–MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data.

A Short-Cut Data Mining Method for the Mass Spectrometric Characterization of Block Copolymers

A new data mining approach as a short cut method is given for the determination of the copolymer composition from mass spectra. Our method simplifies the copolymer mass spectra by reduction of the number of mass peaks. The proposed procedure, namely the selection of the mass peaks, which is based on the most abundant peak of the mass spectrum, can be performed manually or more efficiently using our recently invented Mass-remainder analysis (MARA). The considerable reduction of the MS spectra also simplifies the calculation of the copolymer quantities for instance the number- and weight-average molecular weights (Mn and Mw, respectively), polydispersity index (Đ = Mw/Mn), average molar fraction (cA) and weight fraction (wA) of the comonomer A and so on. These copolymer properties are in line with those calculated by a reference method taking into account all the mass peaks of the copolymer distribution. We also suggest a highly efficient method and template for the determination of the composition drift by processing the reduced mass spectra.

Mass Spectral Filtering by Mass-Remainder Analysis (MARA) at High Resolution and Its Application to Metabolite Profiling of Flavonoids

Flavonoids represent an important class of secondary metabolites because of their potential health benefits and functions in plants. We propose a novel method for the comprehensive flavonoid filtering and screening based on direct infusion mass spectrometry (DIMS) analysis. The recently invented data mining procedure, the multi-step mass-remainder analysis (M-MARA) technique is applied for the effective mass spectral filtering of the peak rich spectra of natural herb extracts. In addition, our flavonoid-filtering algorithm facilitates the determination of the elemental composition. M-MARA flavonoid-filtering uses simple mathematical and logical operations and thus, it can easily be implemented in a regular spreadsheet software. A huge benefit of our method is the high speed and the low demand for computing power and memory that enables the real time application even for tandem mass spectrometric analysis. Our novel method was applied for the electrospray ionization (ESI) DIMS spectra of various herb extract, and the filtered mass spectral data were subjected to chemometrics analysis using principal component analysis (PCA).

Tandem Mass-Remainder Analysis of Industrially Important Polyether Polyols

The characteristics of the polyalkylene oxide polyether polyols highly influence the properties of final polyurethane products. As a novel approach, in order to gain structural information, the recently invented data mining procedures, namely the Mass-remainder analysis (MARA) and the Multistep Mass-remainder analysis (M-MARA) are successfully applied for the processing of tandem mass spectrometry (MS/MS) data of various industrially important polyether polyols. M-MARA yields an ultra-simplified graphical representation of the MS/MS spectra and sorts the product ions based on their double bond equivalent (DBE) values. The maximum DBE values unambiguously differentiate among the various polyether polyols. Accordingly, the characteristic DBE values were 0, 1 for the linear diol polyethers, 0, 1, 2 for the three-arm, and 0, 1 2, 3, 4 for the six-arm polyether polyols. In addition, it was also found that the characteristic collision energy necessary for the optimum fragmentation yield depended linearly on the molecular weight of the polyols. This relationship offers an easy way for instrument tuning to gain structural information.

An arsenal of tools based on Kendrick mass defects to process congested electrospray ionization high-resolution mass spectra of polymers with multiple charging

RATIONALE

Electrospray ionization (ESI) favors the multiple charging of high molecular weight polymer samples and allows their high-resolution mass analysis in the low-mass range. It also induces the detection of numerous ion series at different charge states with different adducts complicating the interpretation of the mass spectrum which should be facilitated by an appropriate data processing.

METHODS

An arsenal of tools based on the Kendrick mass defect (KMD) is proposed to process congested ESI high-resolution mass spectra of poly(propylene oxide) (PPO) samples. The combination of regular, charge-dependent, and resolution-enhanced KMD plots in addition to a "remainders" plot and a new three-dimensional plot offers unrivaled capabilities of filtering for any minor series among thousands of points. The sequential data processing is conducted using Kendo, a spreadsheet developed in-house for an advanced KMD analysis.

RESULTS

The charge-state distribution is easily evaluated by counting the parallel lines in a regular KMD plot. A charge-dependent resolution-enhanced KMD plot instantly reveals the variation of adducted ions at a given charge state, helping the user to choose the best analytical conditions. Ion series at different charge states from PPO oligomers carrying different end-groups are also efficiently extracted using several combinations of KMD and remainders plots and assigned using a new simulator tool.

CONCLUSIONS

The innovative combination of existing and new KMD-related plots, selection tools, and simulator all combined in a single spreadsheet dramatically facilitates the processing and interpretation of complex ESI mass spectral data. The presented tools may be extended to any other class of homo-, co- and terpolymers.

Petroleomics: Tools, Challenges, and Developments

The detailed molecular characterization of petroleum-related samples by mass spectrometry, often referred to as petroleomics, continues to present significant analytical challenges. As a result, petroleomics continues to be a driving force for the development of new ultrahigh resolution instrumentation, experimental methods, and data analysis procedures. Recent advances in ionization, resolving power, mass accuracy, and the use of separation methods, have allowed for record levels of compositional detail to be obtained for petroleum-related samples. To address the growing size and complexity of the data generated, vital software tools for data processing, analysis, and visualization continue to be developed. The insights gained impact upon the fields of energy and environmental science and the petrochemical industry, among others. In addition to advancing the understanding of one of nature's most complex mixtures, advances in petroleomics methodologies are being adapted for the study of other sample types, resulting in direct benefits to other fields.

The Kendrick analysis for polymer mass spectrometry

The mass spectrum of a polymer often displays repetitive patterns with peak series spaced by the repeating unit(s) of the polymeric backbones, sometimes complexified with different adducts, chain terminations, or charge states. Exploring the complex mass spectral data or filtering the unwanted signal is tedious whether performed manually or automatically. In contrast, the now 60-year-old Kendrick (mass defect) analysis, when adapted to polymer ions, produces visual two-dimensional maps with intuitive alignments of the repetitive patterns and favourable deconvolution of features overlaid in the one-dimensional mass spectrum. This special feature article reports on an up-to-date and theoretically sound use of Kendrick plots as a data processing tool. The approach requires no prior knowledge of the sample but offers promising dynamic capabilities for visualizing, filtering, and sometimes assigning congested mass spectra. Examples of applications of the approach to polymers are discussed throughout the text, but the same tools can be readily extended to other applications, including the analysis of polymers present as pollutants/contaminants, and to other analytes incorporating a repetitive moiety, for example, oils or lipids. In each of these instances, data processing can benefit from the application of an updated and interactive Kendrick analysis.