HRMS-Viewer：A Software for High Resolution Mass Spectrometry Formula Assignment and Data Visualization

 

Junyang Chen, Chen He, Jianxun Wu, Yahe Zhang, Quan Shi^*

State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, PR China

*Corresponding Author: E-mail: sq@cup.edu.cn; Tel: +86 010 89739157

 

Abstract: Accurately assigning formulas to thousands of peaks generated by ultra-high resolution mass spectrometry in a single analysis poses a significant challenge, especially when dealing with diverse molecular compositions across complex mixtures. This difficulty is further compounded by the lack of an established universal mass calibration and formula assignment method. We have developed HRMS-Viewer, a Python-based software tool designed for processing ultra-high resolution mass spectrometry data specific to petroleum and natural organic matter (NOM). The software employs and efficient, experience-driven approach for small molecule formula assignment, offering a streamlined yet intuitive workflow. Key features include advanced noise reduction, automatic or manual recalibration, real-time visualization of formula assignment results, and options for manual correction. During the workflow, HRMS-Viewer enable the visualization and manual control of critical steps including noise reduction, recalibration, peak identification, and data review.

Key Words: Formula assignment, High resolution mass spectrometry, Data visualization.

 

1. Introduction

Ultrahigh resolution mass spectrometry, particularly Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and Orbitrap mass spectrometry (Orbitrap MS), plays a pivotal role in the molecular compositional analysis of petroleum ^{1, 2} and natural dissolved organic matter (NOM)³. High-resolution mass spectrometry, characterized by its high resolution and high mass accuracy in measuring mass-to charge ratio (m/z), can achieve a relative error of less than one part per million (1 ppm) under optimal conditions, allowing precise determination of molecular formula. However, as the complexity and molecular weight increases, the reliability of molecular formula matching becomes progressively more uncertain ⁴.

Petroleum contains a diverse array of compounds, including major hydrocarbon species such as saturated and aromatic hydrocarbons, as well as a wide range of heteroatomic compounds. These heteroatomic compounds encompass nitrogen-containing species (e.g. carbazole and pyridine derivatives), acidic oxygen-containing species (naphthenic acids and phenolic compounds), sulfur-containing species (thiophenes and thioethers), and combinations of various heteroatoms. Targeted utilization of specific ionization methods is essential for optimizing the response from diverse compound types ². The primary focus of studies in DOM lies on polar compounds with high oxygen content, typically ranging from 1 to 30. Depending on the source of the sample, DOM may also contain polar compounds incorporating nitrogen, sulfur, and phosphorus elements ^5-7. In addition to the inherent elements within the molecule, there exist certain elements capable of forming adducts with said molecule. These encompass iodine, sodium, chlorine, and silver ions that can be incorporated into specific compounds ⁸. The presence of multiple isotopic peaks corresponding to distinct elements introduces novel challenges in the simultaneous interpretation of mass spectra.

While high-resolution mass spectrometry has successfully resolved simple elemental combinations, the growing complexity of elemental combinations observed in the analysis of heavy petroleum fractions ^{9, 10}, NOM analysis of halogenated elemental influences, and complex derivatization methods poses significant challenges for accurately determining elemental compositions. Consequently, incorrect molecular formula matching can lead to misleading conclusions.

A variety of formula assignment tools have been empolyed in the data processing of high-resolution mass spectrometry, including Sierra Analytics Composer, ICBM-OCEAN ^{11, 12}, PetroOrg ¹³, Formularity ¹⁴, TRFu ^14-16, and NOMspectra ¹⁷. These tools adopt one of two methodologies for formula assignment. The first approach generates all possible combinations of elemental constituents within predefined limits and subsequently compares these combinations against the peak list ¹⁸. The second approach calculates all potential molecular formulas for each peak and subsequently apples a filtering process to refine the results. These software products for molecular formula matching provide identification of elemental combinations; however, they lack a mechanism to assess the accuracy of the matching results, thereby requiring additional data processing to evaluate their reliability.

We have developed a novel methodology for assigning molecular formulas and integrated this approach into our in-house software program, HRMS-Viewer, which is specifically designed for the processing of petroleum and DOM samples. Unlike other software designed for matching molecular formula, HRMS-Viewer does not focus on identify the molecular formula with lowest error or the most plausible one within a specified elemental range. Instead, it identifies matching peaks by leveraging sample information and pre-identified compound types. Conversely, the workflow of HRMS-Viewer requires that researchers actively monitor each stage of the data processing procedure, rather than relying on complex algorithms to restrict the assignment of molecular formulas.

 

2. Methods Discussion

2.1 Overview and work flow of HRMS-Viewer

The HRMS-Viewer is a software application that utilizes a visual mass spectrogram as its core feature, providing an array of data pre-processing tools such as noise reduction, calibration, and molecular formula matching. Additionally, it offers capabilities for data validation and the implementation of data analysis specifically tailored for two widely employed high-resolution mass spectrometers: FT-ICR MS and Orbitrap MS. The analytical approach implemented in the software is grounded on the flowsheet presented in Figure 1. In the absence of precise calibration and noise analysis, HRMS-Viewer offers preliminary data processing and correction capabilities. During the process of molecular formula matching, HRMS-Viewer matches molecular formulae by specifying elemental combinations. The high-speed molecular formula matching enables instant visualization of results, thereby facilitating researchers in optimizing parameters via visual data verification tools.

The key to successfully completing the mass spectrometry process using HRMS-Viewer lies in performing targeted searches for specific molecule categories within the mass spectra, rather than conducting broad searches across wide elemental ranges. The HRMS-Viewer has the capability to receive data in the form of peak lists, which provide information on both mass-charge ratio (m/z) and peak intensity. The data can often be exported from the software provided by the instrument manufacturer and stored in a spreadsheet format, either as .xlsx or .csv files. Once imported into HRMS-Viewer, an initial manual identification process can be conducted using visualized plots such as reconstructed mass spectrum and Kendrick Mass Defect (KMD) plots. Before the application of molecular formula matching process, two optional operations may be applied to enhance the accuracy of molecular formula assignment. These are noise cancellation and peak recalibration. The denoised and calibrated data can be matched for molecular formulas, including known elements and even repetitive units that can be customized as "elements". Furthermore, a variety of reasonable formula limitations can be set in the molecular formula matching process. Once the molecular formula has been assigned, the data can be subjected to further examination and analysis using software-based visualization techniques such as C-DBE (carbon number versus double bond equivalent) and VK (van Krevelen) plots, among others. The workflow of HRMS-Viewer is shown in Fig. 1 and detailed visualization UI can be found in Supporting Information S-3. The key operations, including noise reduction, calibration, molecular formula pre-check and identification, and result validation, are visually presented to the user for supervision in order to guarantee the accuracy and rationality of data processing.

Fig 1. Typical workflow of the HRMS-Viewer software, encompassing noise cancellation, recalibration, peak identification, and result review, can be visually represented, and manually supervised within the HRMS-Viewer software.

 

2.3. Preprocess of mass spectrum: Denoise and calibration for mass spectrum

Prior to formula assignment, it is essential to perform noise canceling and spectrum recalibration. However, during practical sample testing, diverse pretreatment methods and instrument parameter settings may be required for different samples in order to achieve optimal resolution and mass distribution. Consequently, the collection of blank samples for estimating noise often proves impractical. Reidel et al.¹⁹ proposed the method detection limit (MDL) for estimating instrumental noise in natural organic matter research, positing that peaks with mass defects between 0.3 and 0.9 Da are conspicuously absent in natural organic matter, and any peaks emerging within this region on mass spectrum are considered as spurious noise. Nonetheless, this approach is specific to samples of natural organic matter. In petroleum analysis, most mass defects are contributed by hydrogen atoms, resulting in the potential for mass defects of large petroleum molecules also distributed within the range of 0.3-0.9 Da. Therefore, it is imperative to employ an appropriate methodology for identifying representative noise signals from a vast array of signals.

The signal-to-noise ratio for Orbitrap MS data is generally not as well documented or available compared to that of FT-ICR MS data. Hence, a multiplier can be used to selectively filter the abundance of mass spectrometry peaks when analyzing Orbitrap MS data. The HRMS-Viewer use a straightforward approach to directly estimate the average intensity of noise from the mass spectrum. The underlying principle is that in the m/z-Intensity lists obtained from both types of instruments, noise peaks exhibit comparable intensities and are significantly more numerous than the peaks of the target compounds. By identifying this repetitively occurring noise level as the fundamental noise intensity in a user specified narrow step range, a certain multiplier can be applied to filter noise peaks. Peaks falling below this threshold are then excluded from the molecular formula matching process. Fig. 2 presents a comparison of the results of noise reduction for Orbitrap MS data processed by HRMS-Viewer with the noise reduction tool and FT-ICR MS data using signal-to-noise ratio filter. It is worth noting that the Orbitrap MS inherently produces data with reduced noise signals. Furthermore, the same noise treatment can be applied to FT-ICR MS, thereby enabling the export of data with lower signal-to-noise ratios from FT-ICR MS for potential low-abundance target peaks. To evaluate the denoising results, users can examine the KMD plot in the main workspace of HRMS-Viewer, as shown in Fig. S3.

Fig 2. Denoised Kendrick Mass Defect (KMD) plots using filtering signal to noise ratio and denoise algorithm in HRMS-Viewer software. Data acquired form FT-ICR MS and Orbitrap MS are compared.

 

Despite the exceptional resolution and precision offered by modern high-resolution mass spectrometry, it is imperative to account for potential mass deviations during instrument operation, thereby necessitating recalibration of the acquired mass spectrum data. Software provided by instrument manufacturer, such as Bruker Compass Data Analysis and Thermo Xcalibur, also has internal mass calibration utilities. The data calibration model proposed by many researchers uses multiple order equations, piecewise calibration, and other methods^{15, 20-22}. These calibration routines are typically predicated on a list of known compounds to adjust the mass error. To calibrate various samples, it is imperative to pre-identify molecular formulas and specify a set of compounds' m/z values. It should be emphasized that if the mass list selected for calibration remains unchanged across different samples, the accuracy of calibration results may be compromised in cases where certain peaks are absent in the tested sample.

Two methods are available for automatic calibration in HRMS-Viewer. When the relative error distribution of the spectral data is linear and can be expressed as an polynomial equation, the relative error curve can be directly fitted from the KMD distribution using eq 1. F(KM) refers to relationship between Kendrick Mass Defect (KMD) and Kendrick Mass (KM) and the function f(m) means relationship between relative error and m/z. In the event that the relative error distribution curve is more complex and nonlinear, HRMS-Viewer is capable of executing a molecular formula search based on the user-defined possible compound classes and the rough error. The resulting calibration curve is then constructed based on the search results. Detailed workflow of calibration as well as relationship between error curve and KMD distribution can be found in Supporting Information S-2.

                                                                                                                                                (eq 1)

The calibration results are present in Fig. 3 for comparison. The results of SRFA obtained from FT-ICR MS and Orbitrap MS were calibrated using HRMS-Viewer and compared with those of Data Analysis and Xcalibur, respectively. The data results exhibited a more centralized error distribution in FT-ICR MS and a more discrete distribution in Orbitrap MS. However, HRMS-Viewer demonstrated a calibration capability comparable to that of the instrument software. The offline calibration of the data in Data Analysis and Xcalibur necessitates the creation of a pre-designed calibration table, sequential selection of peak locations based on calibration curves, and removal of any absent peaks. HRMS-Viewer is capable of automatically searching multiple specified compound classes within a wide error range, followed by fitting a polynomial equation to determine the relative error and m/z values. By accurately identifying the theoretical m/z values of high-abundance compounds that from homologous series, it enables the establishment of a precise calibration function for individual mass spectrum. In the case of a calibration curve that is affected by misidentified compounds, the use of additional manually selected "anchors" (suppositional m/z values and relative errors, and high weightiness) enables the fitted polynomial curve to yield results that are reasonable.

Fig 3 Mass error distribution after calibration by HRMS-Viewer, Bruker Data Analysis, and Thermo Xcalibur.

 

2.4. Formulae Assignment of Full Mass Spectrum

The assignment of m/z values to specific formulas is a pivotal process in the analysis of mass spectrometry. A typical organic molecule can be depicted as a combination of C, H, and other heteroatoms. The relationship between formula and m/z value is illustrated by the following equation.

The m/z value is comprised of three components: the hydrocarbon moiety, heteroatom moiety, and the mass contribution from gained or lost electrons. In cases where  an element within the molecule is substituted by an isotope, it is considered as a distinct element within the heteroatom group. The extent of electron mass gain or loss relies the specific detection mode employed in mass spectrometry.

Figure 4 shows the algorithmic process of formula assignment in HRMS-Viewer. Various algorithms^{4, 14, 20, 23, 24}, albeit similar, can be used to match individual m/z or all m/z values within the entire mass spectrum with a a suitable molecular formula, thereby enhancing the efficiency of molecular formula assignment. For a given m/z value, all possible heterogeneous combinations of elements can be considered as M_Group, and subsequently, molecular formulas with errors falling within acceptable limits can be retained as possible formula. The entire mass spectrum can be filtered using the M_Group, which is calculated using a given combination of hetero-elements. This allows the user to accurately identify m/z values that fall within the acceptable error tolerance range for this specific compound type. The hetero-element combinations can be either user-specified or derived from the number of elements limiting the combinations.

Fig 4. Formula assignment workflow of HRMS-Viewer, left workflow for un-target formula matching of single peak, while right workflow for targeted formula matching of full spectra

 

The computational advantage lies in the reduction of potentially erroneous combinations of heteroatom groups, the exclusion of compound types that significantly deviate from conventional understanding of the analyzed sample, and the reliance on parallel computing to significantly enhance speed. Even if these compound types are not excluded and all heteroatom combinations are considered, the calculation for each type of compound can still rely on fast parallel computation to immediately obtain formula matching results, eliminating the need for pre-generating a large database of non-existent molecular formulas.

 

2.5. Formulas Results Filtration and Correction

Typically, only small molecular compounds and compound with simple molecular composition can be uniquely matched. The increasing average molecular weight of samples and the elevated heteroatom content lead to a multitude of possible heteroatom groups, leading to an augmented  combinations of these groups and subsequently resulting in a higher occurrence of erroneous matches⁴. HRMS-Viewer incorporates multiple algorithms for preliminary data filtering to eliminate incorrect matches.

(1) Automatic Unreasonable Formula Filter

A molecular formula is not simply a random combination of element numbers; it represents at least one specific chemical structure. However, the automatic recognition of element combinations by the software may sometimes yield unreasonable results. Conventional molecular filtering methods are applied in the HRMS-Viewer, including the elimination of formulas outside the main distribution range based on homologous series, the use of H/C limits based on the boundary limits of polycyclic aromatic hydrocarbon (PAHs), and filter based on the isotope abundance. These filters can be selectively applied or blocked as per specific requirements. A further filter option is that determines the most probable formula when a peak is identified as two possible formulae. The confusion in the results of molecular formula recognition necessitates the manual identification of experienced analyst or the use of complex algorithms to eliminate the confusion. While automated determination of results can be achieved based on completeness of the homologous series, reasonableness of isotopic abundance, and accuracy in mass error, such an algorithm would significantly extended processing time. Pan et al. ²⁵ employed machine learning techniques, which were trained using a specific number of manually annotated and corrected datasets, to effectively discriminate between confusions arising from O_x+11 and N₂O_x compounds in NOM analysis. The recommended methodology in HRMS-Viewer is to combine the following manual checking tools in order to prioritize the list of compound types for calculation in the formula matching stage. The compound types that are more likely to be matched should be placed at the beginning of the classes queue and subsequently removed from the mass list once the molecular formula is successfully matched. Figure 5 compares the results of molecular formula verification for N₂O_x series compounds using different methodologies. In the absence of any data verification, the N₂O_x series is often mistaken for O_x+11., which can lead to confusion and inaccuracies in analysis. The homologous series, however, reaches its structural planar limit as the N₂O_x series due to the inability to reduce the number of hydrogen atoms to zero. Nevertheless, longer homologous series can still be observed in accordance with the KMD diagram. Furthermore, the error distribution of N₂O_x deviates from the mean value of zero. The utilization of manual corrections is undoubtedly the optimal approach, and HRMS-Viewer has the capability to employ a customized compound type queue for molecular formula matching calculations. This ensures that once an O_x class is identified, it cannot subsequently be misidentified as an N₂O_x class. By adopting this methodology, comparable performance to manual screening can be achieved.

Fig 5 Data purified by customized compound type queue compared with manual data check and raw data.

 

(2) Manual Data Reviewing Tools

Irrespective of the isotopic or molecular homologous filters employed, they may not always accurately identify all incorrectly matched formulas in a specific sample. The refinement of these matches often necessitates expert manual intervention.

The HRMS-Viewer, as its name, is not only a formula matching tool; it is a "Viewer," a visualization software for formulas matching. The dataset results, post-formula matching, can be visualized and displayed the outcomes as a C-DBE plot with point sizes indicative of the data. Such a visual interface readily highlights compound combinations that do not confirm to molecular continuity. Additionally, the HRMS-Viewer facilitates the generation of VK, KMD, and mass error distribution plots. Moreover, it allows for interactive engagement with each data point in the visualization, enabling recalculation of potential formulas. This allows users to verify if the error falls within a reasonable range or to check for potential misidentification from another compound series. Direct operation within the graphic interface facilitates the exclusion of implausible results, integrating them into a revised formula matching dataset. In certain cases, as previously mentioned, it is possible to revert to the molecular formula matching function while resetting the list of compound types based on the identification of duplicate matches among multiple confirmed compound types during the verification process. As an alternative, in the case that the range of carbon number, DBE, and mass error is insufficient to encompass the range of m/z data following the completion of molecular formula matching, the parameters are reset and the process of fast molecular formula matching is performed once more. It is recommended that molecular formula matching results be subjected to repeated checking and setting in HRMS-Viewer workflows. This approach will facilitate the attainment of more reasonable and accurate results, thereby reducing the likelihood of erroneous research conclusions resulting from incorrect data analysis.

3. Conclusion

The molecular formula matching and visualization software we have developed is tailored for high-resolution mass spectrometry data in petroleum and dissolved organic matter research. It has undergone extensive internal testing in the laboratory. Although the software itself is not open source, the methodology for data processing has been delineated previously. HRMS-Viewer is dedicated to provide a versatile solution across various application scenarios, whether it is in petroleum or dissolved organic matter analysis, and whatever the ionization source used, like ESI, APPI, APCI, MALDI, or others.

Nonetheless, HRMS-Viewer is inescapable of shortcomings. In the automatic filtration of implausible formulas, it is inevitable that some unreasonable formulas may not be accurately excluded, and some valid formulae, such as those of isotopes with high abundance, might be mistakenly removed. Furthermore, during isotope analysis, the inability to accurately infer the elemental count from isotopic abundances may lead to misidentification of formulas. To settle these issues, the software's visualization capabilities and the reprocessing of formulas matching can assist researchers in verifying the authenticity and plausibility of their data.

 

Acknowledgements

The work is supported by the National Natural Science Foundation of China (NSFC U23B20169)

 

References

(1) Abou-Dib, A.; Aubriet, F.; Hertzog, J.; Vernex-Loset, L.; Schramm, S.; Carré, V. Next Challenges for the Comprehensive Molecular Characterization of Complex Organic Mixtures in the Field of Sustainable Energy. Molecules 2022, 27 (24), 8889.

(2) Shi, Q.; Zhang, Y.; Chung, K. H.; Zhao, S.; Xu, C. Molecular Characterization of Fossil and Alternative Fuels Using Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: Recent Advances and Perspectives. Energy & Fuels 2021, 35(22), 18019–18055.

(3) Kim, S.; Simpson, A. J.; Kujawinski, E. B.; Freitas, M. A.; Hatcher, P. G. High resolution electrospray ionization mass spectrometry and 2D solution NMR for the analysis of DOM extracted by C18 solid phase disk. Organic Geochemistry 2003, 34 (9), 1325-1335.

(4) Koch, B. P.; Dittmar, T.; Witt, M.; Kattner, G. Fundamentals of Molecular Formula Assignment to Ultrahigh Resolution Mass Data of Natural Organic Matter. Analytical Chemistry 2007, 79 (4), 1758-1763.

(5) He, C.; Yi, Y.; He, D.; Cai, R.; Chen, C.; Shi, Q. Molecular composition of dissolved organic matter across diverse ecosystems: Preliminary implications for biogeochemical cycling. Journal of Environmental Management 2023, 344, 118559.

(6) Hertkorn, N.; Benner, R.; Frommberger, M.; Schmitt-Kopplin, P.; Witt, M.; Kaiser, K.; Kettrup, A.; Hedges, J. I. Characterization of a major refractory component of marine dissolved organic matter. Geochimica et Cosmochimica Acta 2006, 70 (12), 2990-3010.

(7) LaRowe, D. E.; Van Cappellen, P. Degradation of natural organic matter: A thermodynamic analysis. Geochimica et Cosmochimica Acta 2011, 75 (8), 2030-2042.

(8) Jackson, A. U.; Shum, T.; Sokol, E.; Dill, A.; Cooks, R. G. Enhanced detection of olefins using ambient ionization mass spectrometry: Ag+ adducts of biologically relevant alkenes. ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2011, 399 (1), 367-376.

(9) Bae, E.; Na, J.-G.; Chung, S. H.; Kim, H. S.; Kim, S. Identification of about 30 000 Chemical Components in Shale Oils by Electrospray Ionization (ESI) and Atmospheric Pressure Photoionization (APPI) Coupled with 15 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) and a Comparison to Conventional Oil. Energy & Fuels 2010, 24 (4), 2563-2569.

(10) Marshall, A. G.; Blakney, G. T.; Chen, T.; Kaiser, N. K.; McKenna, A. M.; Rodgers, R. P.; Ruddy, B. M.; Xian, F. Mass Resolution and Mass Accuracy: How Much Is Enough? Mass Spectrometry 2013, 2 (Special_Issue), S0009-S0009.

(11) Merder, J.; Freund, J. A.; Feudel, U.; Hansen, C. T.; Hawkes, J. A.; Jacob, B.; Klaproth, K.; Niggemann, J.; Noriega-Ortega, B. E.; Osterholz, H.; et al. ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures. Analytical Chemistry 2020, 92 (10), 6832-6838.

(12) Merder, J.; Freund, J. A.; Feudel, U.; Niggemann, J.; Singer, G.; Dittmar, T. Improved Mass Accuracy and Isotope Confirmation through Alignment of Ultrahigh-Resolution Mass Spectra of Complex Natural Mixtures. Analytical Chemistry 2020, 92 (3), 2558-2565.

(13) Riches, E.; Corilo, Y. E.; Rodgers, R.; O'Leary, M.; Stevens, D. Ion mobility and PetroOrg software: Novel techniques for petroleomics investigations. Abstracts of Papers of the American Chemical Society 2015, 249.

(14) Tolić, N.; Liu, Y.; Liyu, A.; Shen, Y.; Tfaily, M. M.; Kujawinski, E. B.; Longnecker, K.; Kuo, L.-J.; Robinson, E. W.; Paša-Tolić, L.; et al. Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra. Analytical Chemistry 2017, 89 (23), 12659-12665.

(15) Fu, Q.-L.; Fujii, M.; Kwon, E. Development of an Internal Calibration Algorithm for Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter. Analytical Chemistry 2022, 94 (30), 10589-10594.

(16) Fu, Q.-L.; Fujii, M.; Riedel, T. Development and comparison of formula assignment algorithms for ultrahigh-resolution mass spectra of natural organic matter. Analytica Chimica Acta 2020, 1125, 247-257.

(17) Volikov, A.; Rukhovich, G.; Perminova, I. V. NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter. Journal of the American Society for Mass Spectrometry 2023, 34 (7), 1524-1527.

(18) Kind, T.; Fiehn, O. Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics 2006, 7 (1), 234.

(19) Riedel, T.; Dittmar, T. A Method Detection Limit for the Analysis of Natural Organic Matter via Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Analytical Chemistry 2014, 86 (16), 8376-8382.

(20) Fu, Q.-L.; Fujii, M.; Ma, R. Development of a Gaussian-Based Alignment Algorithm for the Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter. Analytical Chemistry 2023, 95 (5), 2796-2803.

(21) Pan, Q.; He, C.; Shi, Q. Graph-Based Method for Calibration of High-Resolution Mass Spectra of Natural Organic Matter. Analytical Chemistry 2024, 96(9), 3739–3743.

(22) Rosa, T. R.; Folli, G. S.; Pacheco, W. L. S.; Castro, M. P.; Romão, W.; Filgueiras, P. R. DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry. Journal of the American Society for Mass Spectrometry 2020, 31 (7), 1483-1490.

(23) Green, N. W.; Perdue, E. M. Fast Graphically Inspired Algorithm for Assignment of Molecular Formulae in Ultrahigh Resolution Mass Spectrometry. Analytical Chemistry 2015, 87 (10), 5086-5094.

(24) Nagy, T.; Kuki, Á.; Nagy, M.; Zsuga, M.; Kéki, S. Mass-Remainder Analysis (MARA): An Improved Method for Elemental Composition Assignment in Petroleomics. Analytical Chemistry 2019, 91 (10), 6479-6486.

(25) Pan, Q.; Hu, W.; He, D.; He, C.; Zhang, L.; Shi, Q. Machine-learning assisted molecular formula assignment to high-resolution mass spectrometry data of dissolved organic matter. Talanta 2023, 259, 124484.

 

 

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HRMS-Viewer：A Software for High Resolution Mass Spectrometry Formula Assignment and Data Visualization

Junyang Chen, Chen He, Jianxun Wu, Yahe Zhang, Quan Shi^*

 

HRMS-Viewer is a Python-based software designed for processing ultra-high resolution mass spectrometry (UHRMS) data, specifically tailored for applications in petroleum and natural organic matter analysis. The software offers an intuitive yet comprehensive workflow for UHRMS data analysis, incorporating advanced features such as noise reduction, automatic or manual recalibration, real-time visualization of formula assignment results, and options for manual correction.