Combination of Statistical Methods and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for More Comprehensive, Molecular-Level Interpretations of Petroleum Samples

Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization

In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultrasonic density and ultrasonic time were positively correlated with soluble chemical oxygen demand (SCOD), total nitrogen (TN), and total phosphorus (TP). Within five machine learning models, the best model for SCOD prediction was XG-boost (R2 = 0.855), while RF was the best for TN and TP (R2 = 0.974 and 0.957, respectively). In addition, SHAP indicated that the importance feature for SCOD, TN, and TP was ultrasonic time, and sludge concentration, respectively. Finally, the typical industrial scenario of ultrasonic pretreatment of sludge was analyzed. In the secondary sludge, treatment volume at 0.6 L, the pH at 7.0, and the ultrasonic time at 20 min was best to improve the SCOD. In the ultrasonic pretreatment primary sludge, treatment volume of 0.3 L, pH of 7.0, and ultrasonic time of 15 min was best to improve the SCOD. Furthermore, the ultrasonic power at 700 W and ultrasonic time at 20 min were best to improve the C/N and C/P in the secondary sludge. In the primary sludge, the ultrasonic power at 600 W, and the ultrasonic time at 15 min were best to improve C/N and C/P. This study lays a foundation for the practical application of ultrasonic pretreatment of sludge and provides basic information for typical industrial scenarios.

Origin, composition, and accumulation of dissolved organic matter in a hypersaline lake of the Qinghai-Tibet Plateau

Inland saline lakes are widely distributed and commonly exist in arid and semi-arid regions. Dissolved organic matter (DOM) in saline lakes plays an important role in the global carbon cycle and is a key regulator of saline lake ecosystem functions through biotic and abiotic processes. However, the origin, composition, and cycling of DOM in saline lakes, especially hypersaline lakes, remain largely unknown. In this study, two lake brine DOM samples and three input river DOM samples from a hypersaline lake, Da Qaidam Lake (DQL) in the Qaidam Basin of the Qinghai-Tibet Plateau (QTP), were isolated and analyzed using a multi-analytical approach. The results indicated that, although terrestrial in origin, the DOM composition and features of DQL were dominated by indigenous in-lake processes owing to the very long water residence time of the lake brine. Lake DOM contained more aliphatic compounds but fewer aromatic compounds than DOM from the rivers. Lake DOM also exhibited more chemodiversity and contained highly saturated and oxidized components that were incorporated with heteroatoms. Despite the limited contributions from riverine DOM, some special features of lake DOM, such as the high content of sulfur-bearing components, may be partly related to the long-term accumulation of hotspring riverine input. Flocculation, photodegradation, microbial degradation, evapo-concentration, and primary production processes were considered synergistic factors in the persistence and features of the hypersaline lake DOM. The results of this study can further our knowledge of the transformation and long-term turnover of DOM in hypersaline lakes and how DOM chemodiversity changes across wide aquatic ecosystems.

Screening of potential chemical marker with interspecific differences in Pterocarpus wood and a spatially-resolved approach to visualize the distribution of the characteristic markers

Profiling the spatial distributions and tissue changes of characteristic compounds with interspecific differences is critical to elucidate the complex species identification during tree species traceability, wood anti-counterfeiting verification and timber trade control. In this research, in order to visualize the spatial position of characteristic compounds in two species with similar morphology (Pterocarpus santalinus and Pterocarpus tinctorius), a high coverage MALDI-TOF-MS imaging method was used to found the mass spectra fingerprints of different wood species. 2-Mercaptobenzothiazole matrix was used to spray wood tissue section to enhance the detection effect of metabolic molecules, and the mass spectrometry imaging data were obtained. Based on this technology, the spatial location of fifteen potential chemical markers with remarkable interspecific differences in 2 Pterocarpus timber species were successfully obtained. Distinct chemical signatures obtained from this method can promote rapid identification at the wood species level. Thus, matrix-assisted laser desorption/time-of-flight/ionization mass spectrometry imaging (MALDI-TOF-MSI) provides a spatial-resolved way for traditional wood morphological classification and breaking through the limitations of traditional wood identification technology.

Analysis of environmental organic matters by Ultrahigh-Resolution mass spectrometry-A review on the development of analytical methods

Owing to the increasing environmental and climate changes globally, there is an increasing interest in the molecular-level understanding of environmental organic compound mixtures, that is, the pursuit of complete and detailed knowledge of the chemical compositions and related chemical reactions. Environmental organic molecule mixtures, including those in air, soil, rivers, and oceans, have extremely complex and heterogeneous chemical compositions. For their analyses, ultrahigh-resolution and sub-ppb level mass accuracy, achievable using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), are important. FT-ICR MS has been successfully used to analyze complex environmental organic molecule mixtures such as natural, soil, particulate, and dissolved organic matter. Despite its success, many limitations still need to be overcome. Sample preparation, ionization, structural identification, chromatographic separation, and data interpretation are some key areas that have been the focus of numerous studies. This review describes key developments in analytical techniques in these areas to aid researchers seeking to start or continue investigations for the molecular-level understanding of environmental organic compound mixtures.

Soil Organic Matter Characterization by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation

The biogeochemical cycling of soil organic matter (SOM) plays a central role in regulating soil health, water quality, carbon storage, and greenhouse gas emissions. Thus, many studies have been conducted to reveal how anthropogenic and climate variables affect carbon sequestration and nutrient cycling. Among the analytical techniques used to better understand the speciation and transformation of SOM, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is the only technique that has sufficient mass resolving power to separate and accurately assign elemental compositions to individual SOM molecules. The global increase in the application of FTICR MS to address SOM complexity has highlighted the many challenges and opportunities associated with SOM sample preparation, FTICR MS analysis, and mass spectral interpretation. Here, we provide a critical review of recent strategies for SOM characterization by FTICR MS with emphasis on SOM sample collection, preparation, analysis, and data interpretation. Data processing and visualization methods are presented with suggested workflows that detail the considerations needed for the application of molecular information derived from FTICR MS. Finally, we highlight current research gaps, biases, and future directions needed to improve our understanding of organic matter chemistry and cycling within terrestrial ecosystems.

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses

RATIONALE

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data.

METHODS

We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide the selection of appropriate normalization approaches for two different FT-ICR-MS data sets. Furthermore, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses.

RESULTS

The normalization approach identified by SPANS differed for the two data sets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that total sum scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches.

CONCLUSIONS

Although there is no single, best normalization method for all data sets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. The number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.

Insight into molecular information of Huolinguole lignite obtained by Fourier transform ion cyclotron resonance mass spectrometry and statistical methods

RATIONALE

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was applied to the characterization of organic compounds in coal extracts at the molecular level. Large volumes of data obtained by FT-ICR MS were processed via statistical methods to extract valuable information on the molecular structures and compositions of organic compounds in coal.

METHODS

A low-rank coal was subjected to ultrasonic extraction sequentially with six solvents to separate and enrich species with different molecular characteristics. Complex mass spectra of the six extracts were obtained by a FT-ICR MS system equipped with two ionization sources. Two multivariate statistical methods, hierarchical clustering analysis (HCA) and principle component analysis (PCA), were introduced to mine useful information from the complex MS data and visually exhibit comprehensive molecular details in coal extracts.

RESULTS

Similarities and differences between the 17 MS data sets from six coal extracts ionized by different ion sources were visually exhibited in plots via data processing using HCA and PCA. For HCA, all of the identified compounds were divided into seven classes (CH, O, N, S, ON, OS, and NS), and detailed differences in the relative abundance were revealed. In addition, PCA discriminated the differences in molecular composition for organic compounds from the six extracts.

CONCLUSIONS

Multivariate statistical analysis is a promising methodology which can interpret the chemical composition of coals and coal derivatives at the molecular level, especially for the analysis of multiple complex samples presenting in a single plot.

Molecular-level investigation of soils contaminated by oil spilled during the Gulf War

In this study, molecular-level chemical compositions of soils contaminated by oil spilled during the Gulf War were studied. Two soil samples, respectively collected at 0.1 m and between 0.5 and 1 m below the surface from an oil spill site, were extracted with organic solvents and water. The extracts were analyzed via ultrahigh resolution FT-ICR and two-dimensional gas chromatography/high resolution mass spectrometry. The data showed that the spilled oil was significantly affected by vaporization due to high surface temperatures in the desert. The data obtained with (+) atmospheric pressure photo ionization (APPI) and (-) electrospray ionization (ESI) coupled with ultrahigh resolution-mass spectrometry (UHR-MS) indicated that the degradation of aromatic compounds and increase in oxygen-containing classes occurred in the following order: surface soil > below surface soil > crude oil. The oxygenated compounds were confirmed by principal component analysis. The score and loading plots of O_x and SO_x showed that they were the major contributors to differentiate the samples. However, a comparison with previously reported oceanic oil spills showed that less significant degradation occurred even after almost 30 years. Our data can provide an information basis for designing a strategy for clean-up and restoration efforts of Gulf War oil spills.

Application of molecular dynamics simulation to improve the theoretical prediction for collisional cross section of aromatic compounds with long alkyl chains in crude oils

RATIONALE

Molecular dynamics (MD) simulations with finite temperature were performed to improve the theoretical prediction of collisional cross section (CCS) values, especially for aromatic compounds containing long alkyl chains.

METHODS

In this study, the CCS values of 11 aromatic compounds with long alkyl chains were calculated by MD simulations while considering internal energy at 300, 500, and 700 K, and the results were compared with experimentally determined values.

RESULTS

The CCS values calculated at higher energies showed better agreement with the experimental values. Polycyclic aromatic hydrocarbons (PAHs) such as pentacene and benz[b]anthracene were also investigated, and better agreement between the theoretical and experimental results was observed when higher temperature (or higher internal energy) was considered.

CONCLUSIONS

The data presented in this study show that the internal degrees of freedom of ions must be considered to accurately predict the CCS values of aromatic compounds with a flexible structure measured by ion mobility mass spectrometry.

Linking Natural Oil Seeps from the Gulf of Mexico to Their Origin by Use of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

We report chemical characterization of natural oil seeps from the Gulf of Mexico by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and Gas Chromatography/Atmospheric Pressure Chemical Ionization Mass Spectrometry (GC/APCI-MS), to highlight how FT-ICR MS can also be employed as a means to determine petroleum connectivity, in addition to traditional GC/MS techniques. The source of petroleum is the Green Canyon (GC) 600 lease block in the Gulf of Mexico. Within GC600, two natural oil seepage zones, Mega Plume and Birthday Candles, continuously release hydrocarbons and develop persistent oil slicks at the sea surface above them. We chemically trace the petroleum from the surface oil slicks to the Mega Plume seep itself, and further to a petroleum reservoir 5 km away in lease block GC645 (Holstein Reservoir). We establish the connectivity between oil samples and confirm a common geological origin for the oil slicks, oil seep, and reservoir oil. The ratios of seven common petroleum biomarkers detected by GC/APCI-MS display clear similarity between the GC600 and GC645 samples, as well as a distinct difference from another reservoir oil collected ∼300 km away (Macondo crude oil from MC252 lease block). FT-ICR MS and principal component analysis (PCA) demonstrate further similarities between the GC600 and GC645 samples that distinctly differ from MC252. A common geographical origin is postulated for the GC600/GC645 samples, with petroleum migrating from the GC645 reservoir to the oil seeps found in GC600 and up through the water column to the sea surface as an oil slick.

Themis: Batch Preprocessing for Ultrahigh-Resolution Mass Spectra of Complex Mixtures

Fourier transform ion cyclotron resonance mass spectrometry affords the resolving power to determine an unprecedented number of components in complex mixtures, such as petroleum. The software tools required to also analyze these data struggle to keep pace with advancing instrument capabilities and increasing quantities of data, particularly in terms of combining information efficiently across multiple replicates. Improved confidence in data and the use of replicates is particularly important where strategic decisions will be based upon the analysis. We present a new algorithm named Themis, developed using R, to jointly preprocess replicate measurements of a sample with the aim of improving consistency as a preliminary step to assigning peaks to chemical compositions. The main features of the algorithm are quality control criteria to detect failed runs, ensuring comparable magnitudes across replicates, peak alignment, and the use of an adaptive mixture model-based strategy to help distinguish true peaks from noise. The algorithm outputs a list of peaks reliably observed across replicates and facilitates data handling by preprocessing all replicates in a single step. The processed data produced by our algorithm can subsequently be analyzed by use of relevant specialized software. While Themis has been demonstrated with petroleum as an example of a complex mixture, its basic framework will be useful for complex samples arising from a variety of other applications.

Correlation between experimental data of protonation of aromatic compounds at (+) atmospheric pressure photoionization and theoretically calculated enthalpies

RATIONALE

The theoretical enthalpy calculated from the overall protonation reaction (electron transfer plus hydrogen transfer) in positive-mode (+) atmospheric-pressure photoionization (APPI) was compared with experimental results for 49 aromatic compounds. A linear relationship was observed between the calculated ΔH and the relative abundance of the protonated peak. The parameter gives reasonable predictions for all the aromatic hydrocarbon compounds used in this study.

METHODS

A parameter is devised by combining experimental MS data and high-level theoretical calculations. A (+) APPI Q Exactive Orbitrap mass spectrometer was used to obtain MS data for each solution. B3LYP exchange-correlation functions with the standard 6-311+G(df,2p) basis set was used to perform density functional theory (DFT) calculations.

RESULTS

All the molecules with ΔH <0 kcal/mol for the overall protonation reaction with toluene clusters produced protonated ions, regardless of the desolvation temperature. For molecules with ΔH >0, molecular ions were more abundant at typical APPI desolvation temperatures (300°C), while the protonated ions became comparable or dominant at higher temperatures (400°C). The toluene cluster size was an important factor when predicting the ionization behavior of aromatic hydrocarbon ions in (+) APPI.

CONCLUSIONS

The data used in this study clearly show that the theoretically calculated reaction enthalpy (ΔH) of protonation with toluene dimers can be used to predict the protonation behavior of aromatic compounds. When compounds have a negative ΔH value, the types of ions generated for aromatic compounds could be very well predicted based on the ΔH value. The ΔH can explain overall protonation behavior of compounds with ΔH values >0. Copyright © 2017 John Wiley & Sons, Ltd.

Analysis of soluble components in coals and interpretations for the complex mass spectra

RATIONALE

The deduction of useful information from the mass spectra of a complex mixture like coals remains difficult, which limits the clean and efficient utilization of coals. It is necessary to explore the data interpretation methods for mass spectra and visualize the analytical data of coals for industrial utilization such as feedstock selection.

METHODS

Coal sample and methanol were mixed and heated to 310 °C and kept at that temperature for 2 h. The solvent was under supercritical state at 310 °C and the solubility for the solid mixture increased. Soluble products from thermal dissolution of two Chinese coals were analyzed by high-performance liquid chromatography/atmospheric pressure chemical ionization orbitrap mass spectrometry.

RESULTS

The iso-abundance plot for molecules in coals was upgraded to display the distributions of isomers which are indicated as concentric circles or triangles with the same carbon number and value of double-bond equivalent. The concentration ratio was introduced from economics to describe the content inequality of organic species within the same class of coal molecules.

CONCLUSIONS

Interpretation methods for mass spectra visualize and simplify the understanding of complex components in coals for industrial utilization. Coals with a high concentration ratio for a specific class should take priority as a feedstock for chemicals and receive more attention. Copyright © 2016 John Wiley & Sons, Ltd.

Molecular-level evidence provided by ultrahigh resolution mass spectrometry for oil-derived doc in groundwater at Bemidji, Minnesota

Dissolved organic matter samples extracted from ground water at the USGS Bemidji oil spill site in Minnesota were investigated by ultrahigh resolution mass spectrometry. Principle component analysis (PCA) of the elemental composition assignments of the samples showed that the score plots for the contaminated sites were well separated from those for the uncontaminated sites. Additionally, spectra obtained from the same sampling site 7 and 19 years after the spill were grouped together in the score plot, strongly suggesting a steady state of contamination within the 12year interval. The double bond equivalence (DBE) of O_x class compounds was broader for the samples from the contaminated sites, because of the complex nature of oil and the consequent formation of compounds with saturated and/or aromatic structures from the oxygenated products of oil. In addition, O_x class compounds with a relatively smaller number of x (x<8; x=number of oxygen) and O_xS₁ class compounds were more abundant in the samples from the contaminated sites, because of the lower oxygen and higher sulfur contents of the oil compared to humic substances. The molecular-level signatures presented here can be a fundamental basis for in-depth analysis of oil contamination.

Mechanistic study on lowering the sensitivity of positive atmospheric pressure photoionization mass spectrometric analyses: size-dependent reactivity of solvent clusters

RATIONALE

Understanding the mechanism of atmospheric pressure photoionization (APPI) is important for studies employing APPI liquid chromatography/mass spectrometry (LC/MS). In this study, the APPI mechanism for polyaromatic hydrocarbon (PAH) compounds dissolved in toluene and methanol or water mixture was investigated by use of MS analysis and quantum mechanical simulation. In particular, four different mechanisms that could contribute to the signal reduction were considered based on a combination of MS data and quantum mechanical calculations.

METHODS

The APPI mechanism is clarified by combining MS data and density functional theory (DFT) calculations. To obtain MS data, a positive-mode (+) APPI Q Exactive Orbitrap mass spectrometer was used to analyze each solution. DFT calculations were performed using the general atomic and molecular electronic structure system (GAMESS).

RESULTS

The experimental results indicated that methanol significantly reduced the signal in (+) APPI, but no significative signal reduction was observed when water was used as a co-solvent with toluene. The signal reduction is more significant especially for molecular ions than for protonated ions. Therefore, important information about the mechanism of methanol-induced signal reduction in (+) APPI-MS can be gained due its negative impact on APPI efficiency.

CONCLUSIONS

The size-dependent reactivity of methanol clusters ((CH3 OH)n , n = 1-8) is an important factor in determining the sensitivity of (+) APPI-MS analyses. Clusters can compete with toluene radical ions for electrons. The reactivity increases as the sizes of the methanol clusters increase and this effect can be caused by the size-dependent ionization energy of the solvent clusters. The resulting increase in cluster reactivity explains the flow rate and temperature-dependent signal reduction observed in the analytes. Based on the results presented here, minimizing the sizes of methanol clusters can improve the sensitivity of LC/(+)-APPI-MS.

Structure-dependent degradation of polar compounds in weathered oils observed by atmospheric pressure photo-ionization hydrogen/deuterium exchange ultrahigh resolution mass spectrometry

The resin fractions of fresh mixtures of three oils spilled during the M/V Hebei Spirit oil spill, as well as weathered oils collected at weathering stages II and IV from the oil spill site were analyzed and compared by atmospheric pressure photo-ionization hydrogen/deuterium exchange mass spectrometry (HDX MS). The significantly decreased abundance of N(+) and [N-H+D](+) ions suggested that secondary and tertiary amine-containing compounds were preferentially degraded during the early stage of weathering. [N+H](+) and [N+D](+) ions previously attributed to pyridine-type compounds degraded more slowly than secondary and tertiary amine-containing compounds. The preferential degradation of nitrogen-containing compounds was confirmed by photo-degradation experiments using 15 standard compounds. In addition, significant increases of [S1O1+H](+) and [S1O1+D](+) ions with higher DBE values were observed from fresh oil mixtures as compared to stages II and IV samples, and that could be linked with the decrease of higher DBE compounds of the S1 class. This study presented convincing arguments and evidence demonstrating that secondary and tertiary amines were more vulnerable to photo-degradation than compounds containing pyridine, and hence, preferential degradation depending on chemical structures must be considered in the production of hazardous or toxic components.

Characterization of organic composition in snow and surface waters in the Athabasca Oil Sands Region, using ultrahigh resolution Fourier transform mass spectrometry

This study was conducted to characterize the composition of dissolved organic compounds present in snow and surface waters in the Athabasca Oil Sands Region (AOSR) with the goal of identifying whether atmospherically-derived organic compounds present in snow are a significant contributor to the compounds detected in surface waters (i.e., rivers and lakes). We used electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) to characterize the dissolved organic compound compositions of snow and surface water samples. The organic profiles obtained for the snow samples show compositional differences between samples from near-field sites (<5 km from oil sands activities) and those from more distant locations (i.e., far-field sites). There are also significant compositional differences between samples collected in near-field sites and surface water samples in the AOSR. The composition of dissolved organic compounds at the upstream Athabasca River site (i.e., Athabasca River at Athabasca) is found to be different from samples obtained from downstream sites in the vicinity of oil sands operations (i.e., Athabasca River at Fort McMurray and Athabasca River at Firebag confluence). The upstream Athabasca River sites tended to share some compositional similarities with far-field snow deposition, while the downstream Athabasca River sites are more similar to local lakes and tributaries. This contrast likely indicates the relative role of regional snowmelt contributions to the Athabasca River vs inputs from local catchments in the reach downstream of Fort McMurray.

An omic approach for the identification of oil sands process-affected water compounds using multivariate statistical analysis of ultrahigh resolution mass spectrometry datasets

Oil sands process-affected water (OSPW) is a major environmental issue due to its acute and chronic toxicity to aquatic life. Advanced oxidation processes are promising treatments to successfully degrade toxic OSPW compounds. This study applied high resolution mass spectrometry to detect over 1000 compounds in OSPW samples after treatments including general ozonation, and ozone with carbonate, tert-butyl-alcohol, carbonate/tert-butyl-alcohol, tetranitromethane, or iron. Hierarchal clustering analysis showed that samples clustered based on sampling time and principal component analysis corroborated these results while also providing information on significant markers responsible for the clustering. Some markers were uniquely present in certain treatment conditions, while others showed variable behaviors in two or more treatments due to the presence of scavengers/catalysts. This advanced approach to monitoring significant changes of markers by using multivariate analysis can be invaluable for future work on OSPW treatment by-products and their potential toxicity to receiving environment organisms.

Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics

Because of the increasing importance of heavy and unconventional crude oil as an energy source, there is a growing need for petroleomics: the pursuit of more complete and detailed knowledge of the chemical compositions of crude oil. Crude oil has an extremely complex nature; hence, techniques with ultra-high resolving capabilities, such as Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), are necessary. FT-ICR MS has been successfully applied to the study of heavy and unconventional crude oils such as bitumen and shale oil. However, the analysis of crude oil with FT-ICR MS is not trivial, and it has pushed analysis to the limits of instrumental and methodological capabilities. For example, high-resolution mass spectra of crude oils may contain over 100,000 peaks that require interpretation. To visualize large data sets more effectively, data processing methods such as Kendrick mass defect analysis and statistical analyses have been developed. The successful application of FT-ICR MS to the study of crude oil has been critically dependent on key developments in FT-ICR MS instrumentation and data processing methods. This review offers an introduction to the basic principles, FT-ICR MS instrumentation development, ionization techniques, and data interpretation methods for petroleomics and is intended for readers having no prior experience in this field of study.

Innovative methods in soil phosphorus research: A review

Phosphorus (P) is an indispensable element for all life on Earth and, during the past decade, concerns about the future of its global supply have stimulated much research on soil P and method development. This review provides an overview of advanced state-of-the-art methods currently used in soil P research. These involve bulk and spatially resolved spectroscopic and spectrometric P speciation methods (1 and 2D NMR, IR, Raman, Q-TOF MS/MS, high resolution-MS, NanoSIMS, XRF, XPS, (µ)XAS) as well as methods for assessing soil P reactions (sorption isotherms, quantum-chemical modeling, microbial biomass P, enzymes activity, DGT, ³³P isotopic exchange, ¹⁸O isotope ratios). Required experimental set-ups and the potentials and limitations of individual methods present a guide for the selection of most suitable methods or combinations.

The comparison of naturally weathered oil and artificially photo-degraded oil at the molecular level by a combination of SARA fractionation and FT-ICR MS

Two sets of oil samples, one obtained from different weathering stages of the M/V Hebei Spirit oil spill site and the other prepared by an in vitro photo-degradation experiment, were analyzed and compared at the molecular level by atmospheric pressure photo-ionization coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). For a more detailed comparison at the molecular level, the oil samples were separated into saturate, aromatic, resin, and asphaltene (SARA) fractions before MS analysis. Gravimetric analysis of the SARA fractions revealed a decreased weight percentage of the aromatic fraction and an increased resin fraction in both sets of samples. Molecular-level investigations of the SARA fractions showed a significant reduction in the S1 class in the saturate fraction and increase of S1O1 class compounds with high DBE values in resin fraction. Levels of N1 and N1O1 class compounds resulting in protonated ions (presumably basic nitrogen compounds) increased after degradation compared to compounds generating molecular ions (presumably non-basic nitrogen compounds). This study revealed changes occurring in heteroatom polar species of crude oils such as sulfur and nitrogen containing compounds that have not been easily detected with conventional GC based techniques.

Two dimensional correlation analysis of Fourier transform ion cyclotron resonance mass spectra of dissolved organic matter: a new graphical analysis of trends

Two-dimensional (2D) correlation analysis was applied to 20 Fourier transform ion cyclotron resonance mass spectra (FTICR-MS) of ultrafiltered dissolved organic matter samples from a salinity transect of the lower Chesapeake Bay. We were able to investigate the chemical changes in the dissolved organic matter pool at the molecular level and classify the individual peaks based on their biogeochemical reactivity. The power of this technique is its ability to be used on either the presence/absence of the individual peaks or their normalized magnitudes. The presence or absence of the peaks are utilized to identify the reactivity and correlation between peaks that plot in different regions of the van Krevelen diagram, whereas the normalized magnitudes are used to correlate the changes among individual peaks. One of the promising advantages of 2D correlation of FTICR-MS data is the ability to associate the variations of the individual peaks with the changes in the functional groups that are measured by other spectroscopic techniques. This approach takes us one step further from identifying molecular formulas to proposing chemical structures.

Detailed characterization of polar compounds of residual oil in contaminated soil revealed by Fourier transform ion cyclotron resonance mass spectrometry

Effects of remediation technologies on polar compounds of crude oil in contaminated soils have not been well understood when compared to hydrocarbons. In this study, ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize the changes in NSO polar compounds of crude oil and residual oil after long-term natural attenuation, biostimulation and subsequent ozonation following biostimulation of contaminated soils. N1 and O1 species, which were abundant in the crude oil, were selectively biodegraded, and species with higher double bond equivalent values and smaller carbon numbers appeared to be more resistant to microbial alteration. O2-O6 species were enriched by biodegradation and contained a large number of compounds with a high degree of unsaturation. Ozone could react with a variety of polar compounds in residual oil after biodegradation and showed high reactivity with polar species containing aromatic or multi-aliphatic rings, including the residual N1 and O1 species, naphthenic acids and unsaturated O3-O6 compounds. Fatty acids and O3-O8 species dominated by saturated alkyl compounds were resistant to ozonation or the primarily incomplete ozonation products. Principal component analysis of identified peaks in the FT-ICR MS spectra provided a comprehensive overview of the complex samples at the molecular level and the results were consistent with the detailed analysis. Taken together, these results showed the high complexity of polar compounds in residual oils after biodegradation or ozonation in contaminated soil and would contribute to a better understanding of bioremediation and ozonation processes.

Multivariate statistical approaches for the characterization of dissolved organic matter analyzed by ultrahigh resolution mass spectrometry

We apply multivariate statistics to explore the large data sets encountered from Fourier transform ion cyclotron resonance mass spectra of dissolved organic matter (DOM). Molecular formula assignments for the individual constituents of DOM are examined by hierarchal cluster analysis (HCA) and principal component analysis (PCA), to measure the relationships between numerous DOM samples. We compare two approaches: (1) using averages of elemental ratios and double bond equivalents calculated from the formulas, and (2) employing individual formulas and either their presence/absence or relative magnitude in each sample. With approach 2, PCA deciphers which of the thousands of formulas are significant to particular samples, and then a van Krevelen diagram highlights what types of compounds are molecular signatures to the samples. Our dual approach, especially approach 2, allows for complex data sets to be more easily interpreted, aiding in the characterization of DOM from various sources. By applying this methodology, clear trends can be delineated, trends that are not apparent from currently employed methods. Terrestrial DOM contains various lignin-derived compounds, tannins, and condensed aromatics. Marine DOM contains aliphatic compounds with heteroatom functionalities, as well as lignin-like molecules.