Mass Defect Analysis Research Articles

Polymers of micro(nano) plastic in household tap water of the Barcelona Metropolitan Area

Microplastics (MPLs) are emerging persistent pollutants affecting drinking water systems, and different studies have reported their presence in tap water. However, most of the work has a focus on particles in the 100–5 µm range.Here, a workflow to identify and quantify polymers of micro and nanoplastics (MNPLs), with sizes from 0.7 to 20 µm in tap water, is presented. The analytical method consisted of water fractionated filtration followed by toluene ultrasonic-assisted extraction and size-exclusion chromatography, using an advanced polymer chromatography column coupled to high-resolution mass spectrometry with atmospheric pressure photoionization source with negative and positive ionization conditions (HPLC(APC)-APPI(±)-HRMS) and normal phase chromatography HILIC LUNA® column and electrospray ionisation source in positive and negative mode (HPLC(HILIC)-ESI(±)-HRMS). The acquisition was performed in full scan mode, and the subsequent tentative identification of MNPLs polymers has been based on increasing the confirmation level, including the characterisation of monomers by using Kendrick Mass Defect (KMD) analysis, and confirmation and quantification using standards. This approach was applied to assess MNPLs in tap water samples of the Barcelona Metropolitan Area (BMA), that were collected from August to October 2020 from home taps of volunteers distributed in the 42 postal codes of the BMA.Polyethylene (PE), polypropylene (PP), polyisoprene (PI), polybutadiene (PBD), polystyrene (PS), polyamide (PA), and polydimethylsiloxanes (PDMS) were identified. PE, PP, and PA were the most highly detected polymers, and PI and PBD were found at the highest concentrations (9,143 and 1,897 ng/L, respectively).A principal component analysis (PCA) was conducted to assess differences in MNPLs occurrence in drinking water, that was provided from the two drinking water treatment plants (DWTPs) suppliers. Results showed that no significant differences (at 95% confidence level) were established between the drinking water supplies to the different areas of the BMA.

Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments.

Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis.

Development of a peak extraction method using the high-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and machine learning techniques: Analysis of peak shapes.

Combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) and Kendrick mass defect (KMD) analysis is a powerful tool for visualizing polymers in complex mass spectra. The identification of minor polymers by KMD analysis requires reduction of the broad noise peaks often observed in the low-mass region. A machine-learning model was created using pix2pixHD. It converts an original mass spectrum into a pseudo-mass spectrum that contains only the original peaks at m/z positions that the model judges as sharp single-component peaks. It reduces noise by selecting only the m/z and intensity values from the original spectrum's peak list that correspond to peaks in the pseudo-mass spectrum. A machine-learning model was applied to a low-concentration polymer mass spectrum observed at m/z <2000. Extracting single-component peaks from the mass spectrum made the minor polymer series appear clearly in the KMD plot. The technique facilitated mass spectrometric imaging of the ultraviolet degradation of polyethylene terephthalate by plotting the polymers' spatial distributions. It could also distinguish between polymer series (before and after degradation) to identify their separate spatial distributions. A machine-learning method for peak extraction from high-resolution MALDI-TOFMS was developed. Single-component peaks of the mass spectrum were distinguished from noise peaks by their peak shapes. Combining with KMD analysis facilitated the identification of minor polymer series in complex mass spectra.

Deconvolving Native and Intact Protein Mass Spectra with UniDec.

Intact protein, top-down, and native mass spectrometry (MS) generally requires the deconvolution of electrospray ionization (ESI) mass spectra to assign the mass of components from their charge state distribution. For small, well-resolved proteins, the charge can usually be assigned based on the isotope distribution. However, it can be challenging to determine charge states with larger proteins that lack isotopic resolution, in complex mass spectra with overlapping charge states, and in native spectra that show adduction. To overcome these challenges, UniDec uses Bayesian deconvolution to assign charge states and to create a zero-charge mass distribution. UniDec is fast, user-friendly, and includes a range of advanced tools to assist in intact protein, top-down, and native MS data analysis. This chapter provides a step-by-step protocol and an in-depth explanation of the UniDec algorithm, and highlights the parameters that affect the deconvolution. It also covers advanced data analysis tools, such as macromolecular mass defect analysis and tools for assigning potential PTMs and bound ligands. Overall, this chapter provides users with a deeper understanding of UniDec, which will enhance the quality of deconvolutions and allow for more intricate MS experiments.

Characteristics and evolution of heavy components in bio-oil from the pyrolysis of cellulose, hemicellulose and lignin

Three main components of biomass were pyrolyzed individually in a closed reaction system at 500–700 °C for 60s and 90s. Then bio-oil heavy compounds were further analyzed with Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) and Kendrick mass defect (KMD) analysis. The evolution paths of heavy compounds for the different pyrolysis stages were proposed. It was found that the sugars and phenolic-like species in heavy compounds were the most active substances during secondary reactions. Moreover, the rising temperature promoted this secondary reaction of phenolic-like species as the decrease in their abundances growing from 13% to 54%, while contrarily inhibited it for hemicellulose as the decrease in their abundances changing from 44% to −2%. The lignin-derived lipids and unsaturated hydrocarbons that generated in the secondary reactions increased with rising temperature. KMD analysis showed that the heavy compounds of cellulose and hemicellulose prefer homologous evolution during pyrolysis, while those of lignin had more complex evolution paths like cracking and recombination.

Fourier-Transform Approach for Reconstructing Macromolecular Mass Defect Profiles.

State-of-the-art native mass spectrometry (MS) methods have been developed for analysis of highly heterogeneous intact complexes and have provided much insight into the structure and properties of noncovalent assemblies that can be difficult to study using denatured proteins. These native MS methods can often be used to study even highly polydisperse membrane proteins embedded in detergent micelles, nanodiscs, and other membrane mimics. However, characterizing highly polydisperse native complexes which are also heterogeneous presents additional challenges for native MS. Macromolecular mass defect (MMD) analysis aims to characterize heterogeneous ion populations obfuscated by adduct polydispersity and reveal the distribution of "base" masses, and was recently implemented in the Bayesian analysis software UniDec. Here, we illustrate an alternative, orthogonal MMD analysis method implemented in the deconvolution program iFAMS, which takes advantage of Fourier transform (FT) to deconvolve low-resolution data with few user-input parameters and which can provide high quality results even for mass spectra with a signal-to-noise ratio of ∼5:1. Agreement between this method, which is based on frequency-domain data, and the mass-domain algorithm of UniDec provides strong evidence that both methods can accurately characterize highly polydisperse and heterogeneous ion populations. The FT algorithm is expected to be very useful in characterizing many types of analytes ranging from membrane proteins to polymer-conjugated proteins, branched polymers, and other large analytes, as well as for reconstructing isotope profiles for highly complex but still isotope-resolved mass spectra.

High-Resolution Mass Spectrometry and Nontraditional Mass Defect Analysis of Brominated Historical Pigments.

The implementation of high-resolution mass spectrometry systems offers new possibilities for the analysis of complex art samples such as historical oil paintings. However, these multicomponent systems generate large and complex data sets that require advanced visualization tools to aid interpretation, especially when no chromatographic separation is performed. In the context of this research, it was crucial to propose a data analysis tool to identify the products generated during the synthesis, drying, and aging of historical pigments. This study reports for the first time a nontraditional mass defect analysis of oil paint samples containing a fugitive brominated-organic pigment, eosin or geranium lake, by using direct infusion electrospray ionization in combination with a high-resolution Orbitrap mass spectrometer. The use of nontraditional Kendrick mass defect plots is presented in this study as a processing and visualization tool to recognize brominated species based on their specific mass defect and isotope pattern. The results demonstrate that this approach could provide valuable molecular compositional information on the degradation pathways of this pigment. We anticipate that mass defect analysis will become highly relevant in future degradation studies of many more historical organic pigments.

Characterizing the Chemical Landscape in Commercial E-Cigarette Liquids and Aerosols by Liquid Chromatography-High-Resolution Mass Spectrometry.

The surge in electronic cigarette (e-cig) use in recent years has raised questions on chemical exposures that may result from vaping. Previous studies have focused on measuring known toxicants, particularly those present in traditional cigarettes, while fewer have investigated unknown compounds and transformation products formed during the vaping process in these diverse and constantly evolving products. The primary aim of this work was to apply liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemical fingerprinting techniques for the characterization of e-liquids and aerosols from a selection of popular e-cig products. We conducted nontarget and quantitative analyses of tobacco-flavored e-liquids and aerosols generated using four popular e-cig products: one disposable, two pod, and one tank/mod. Aerosols were collected using a condensation device and analyzed in solution alongside e-liquids by LC-HRMS. The number of compounds detected increased from e-liquids to aerosols in three of four commercial products, as did the proportion of condensed-hydrocarbon-like compounds, associated with combustion. Kendrick mass defect analysis suggested that some of the additional compounds detected in aerosols belonged to homologous series resulting from decomposition of high-molecular-weight compounds during vaping. Lipids in inhalable aerosols have been associated with severe respiratory effects, and lipid-like compounds were observed in aerosols as well as e-liquids analyzed. Six potentially hazardous additives and contaminants, including the industrial chemical tributylphosphine oxide and the stimulant caffeine, were identified and quantified in the e-cig liquids and aerosols analyzed. The obtained findings demonstrate the potential of nontarget LC-HRMS to identify previously unknown compounds and compound classes in e-cig liquids and aerosols, which is critical for the assessment of chemical exposures resulting from vaping.

Screening and Quantification of Micro(Nano)Plastics and Plastic Additives in the Seawater of Mar Menor Lagoon

In this work a suspect-screening approach was employed to assess the polymers and plastic additives of micro(nano)plastics (NPL/MPLs) of size ranges from the nm range to 20 μm present in seawater from the top 5 cm of the Mar Menor lagoon during two sampling campaigns (summer and winter), as well of other potentially adsorbed compounds onto the plastic particles surfaces and suspended material. The identification of NPL/MPLs has been based on characteristic Kendrick Mass Defect analysis for each polymer type in mass spectra. The applied methodology allowed to identify NPLs/MPLs of polystyrene (PS), polyethylene (PE), polyisoprene (PI), polybutadiene (PBD), polypropylene (PP), polyamides (PA), polyvinylchloride (PVC), n-isopropylacrylamide (PNIPAm), and polydimethylsiloxanes. In addition, PS, PE, PI, PBD, PP, PA, and PVC were confirmed with standards, and the equivalent concentrations were quantified. The results of this study showed that most frequently found compounds were PP, PE, PA and PNIPAm, while the compound found at higher concentrations was by far PP reaching the 9,303 ± 366 ng/mL in one of the samples. A total number of 135 chemical compounds were tentatively identified, 74 of them plastic additives and compounds used in the polymers manufacture or coming from the polymer’s decomposition. In relation to plastic additives, the more frequently tentatively identified compounds were plasticizers such as phthalates group; stabilizers such as antioxidants (e.g., distearyl 3,3′-thiodipropionate, 2,5-di-tert-butylhydroquinone), and UV filters as benzotriazoles. Several flame retardants of the group of phosphates were as well detected. The other compounds tentatively identified in the samples were pharmaceuticals, pesticides, food additives, flavors and natural products that were attached onto the plastic particles and particulate matter from surrounding waters. In regards to the seasonal variation, during the summer a major number of compounds were tentatively detected, while de concentrations of polymers were slightly higher in winter. The spatial distribution showed higher contamination in the southern part of the coastal lagoon.

“Simultaneous targeted and non-targeted analysis of per- and polyfluoroalkyl substances in environmental samples by liquid chromatography-ion mobility-quadrupole time of flight-mass spectrometry and mass defect analysis”

Per- and polyfluoroalkyl substances (PFAS) represent a large group of synthetic organic compounds which exhibit unique properties and have been extensively used for consumer and industrial products, resulting in a widespread presence in the environment. Regulation requiring PFAS monitoring has been implemented worldwide due to their potential health and eco-toxicological effects. Targeted methods are commonly used to monitor between twenty to forty PFAS compounds, representing only a small fraction of the number of compounds that may be present. Consequently, there is an increasing interest in complementary non-targeted methods to screen and identify unknown PFAS compounds with the aim to improve knowledge and to generate more accurate models regarding their environmental mobility and persistence. This work details the development of a method that simultaneously provided targeted and non-targeted PFAS analysis. Ultra-high performance liquid chromatography (UHPLC) was coupled to ion mobility-quadrupole time of flight-mass spectrometry (IMS-QTOF-MS) and used to quantify known and screen unknown PFAS in environmental samples collected within the greater Sydney basin (Australia). The method was validated for the quantification of 14 sulfonate-based PFAS, and a non-targeted data analysis workflow was developed using a combination of mass defect analysis with common fragment and neutral loss filtering to identify fluorine-containing species. The optimised method was applied to the environmental samples and enabled the determination of 3–7 compounds from the targeted list and the detection of a further 56–107 untargeted PFAS. This simultaneous analysis reduces the complexity of multiple analyses, and allows for greater interrogation of the full PFAS load in environmental samples.

Combining high resolution mass spectrometry with a halogen extraction code to characterize and identify brominated disinfection byproducts formed during ozonation

Ozonation is widely used during water treatment but can generate a variety of toxic disinfection byproducts, especially in the presence of bromide. In the present study, our halogen extraction code was extended and modified to identify bromine isotopic patterns and combined with the R package MFAssignR in selectively identifying brominated disinfection byproducts (Br-DBPs) from high resolution mass spectra. In total, 127 Br-DBPs formed from a Suwannee River natural organic matter (SRNOM) solution were successfully detected from tens of thousands of mass spectrometry peaks. Kendrick mass defect analysis and structural characterization identified 17 structures, 15 of which were identified as brominated carboxylic acids and firstly reported here. Computational model predictions indicated that these brominated carboxylic acids may possess high toxic potencies and raise valid concerns. The adapted halogen extraction code described in this study is a powerful tool for a wider application of analyzing Br-DBPs in complex water matrices and provides an effective technique to characterize and identify these compounds in future studies.

Nontargeted identification of chlorinated disinfection byproducts formed from natural organic matter using Orbitrap mass spectrometry and a halogen extraction code

Natural organic matter is a major source of precursors of hazardous chlorinated disinfection byproducts (Cl-DBPs) formed during water treatment, but the majority of Cl-DBPs are still unidentified. In this study, we used a self-written halogen extraction code to identify halogen isotopic patterns in combination with the R package MFAssignR, to identify Cl-DBPs from Orbitrap mass spectra. One hundred and eighty-nine Cl-DBPs were detected during chlorination of a Suwannee River natural organic matter solution, and the structures of 20 of these compounds are reported for the first time. Kendrick mass defect analysis and structural identification confirmed that chlorinated carboxylic acids are common and likely to form during chlorination. A toxicity prediction using quantitative structure-activity relationship models indicated that most of the chlorinated carboxylic acids may be highly toxic. Our analytical strategy can identify Cl-DBPs accurately from complex mixtures and may also be applicable to the identification of other halogenated disinfection byproducts formed during water treatment.

Top-down lignomics analysis of the French oak lignin by atmospheric pressure photoionization and electrospray ionization quadrupole time-of-flight tandem mass spectrometry: Identification of a novel series of lignans.

We report herein the top-down lignomic analysis of virgin released lignin (VRL) extracted from the French oak wood using atmospheric pressure photoionization quadrupole orthogonal time-of-flight mass spectrometry (APPI-QqTOF-MS) (+ ion mode). Eight major protonated lignin oligomers were identified using the APPI-QqTOF-MS/MS of this complex VRL mixture without any kind of purification. This series of protonated oligomer ions were identified as neolignan cedrusin (1), five different aryltetralin lignans dimers (2-6), one lignan-dehydroshikimic acid complex (7), and a lignan trimer (8). Similarly, electrospray ionization (ESI)-QqTOF-MS (+ ion mode) allowed us to identify three extra aryltetralin lignan derivatives (9-11). The Kendrick mass defect analysis was used for the simplification of this complex APPI-QqTOF-MS into a compositional map, which displayed clustering points of associated ions possessing analogous elemental composition. This series of novel protonated molecules were selected and subjected to low-energy collision-induced dissociation (CID)-MS/MS analyses. The obtained gas-phase fragmentation patterns helped to tentatively assign their most likely structures. Also, it was found that the use of different APPI and ESI ambient ionization techniques enhances the ionization of different types of lignin oligomers.

Coated glass capillaries as SPME devices for DART mass spectrometry.

Solid-phase microextraction (SPME) provides high-throughput sample cleanup and pre-concentration. Here we demonstrate coated glass capillaries (CGCs) as SPME devices for specific applications in direct analysis in real time (DART) mass spectrometry, referred to as "CGC-DART", for rapid screening of environmental contaminants at low parts-per-trillion detection limits and with accurate identification of analytes. The extraction is performed in a one-step process in minutes by dipping the CGC in solutions containing the analytes, and then placing the CGC in a DART source for analysis. CGCs are disposable and relatively inexpensive in comparison with SPME devices, and can be prepared with hydrophobic, hydrophilic or mixed-mode materials similar to SPME. CGCs were prepared by adsorption coating with incubation of capillaries in saturated solutions of octadecylamine or covalent activation of silanes. Quantitation is shown with perfluorooctanoic acid (PFOA) at 1 ppt to 100 ppb, with the lowest detection at 500 parts-per quadrillion (ppq) in tap water. One-step extraction of contaminated groundwater from Northern Queensland, Australia, revealed perfluorooctane sulfonate (PFOS) and perfluorohexanesulfonamide as well as C4-C8 perfluoroalkyl carboxylic acids. A soil sample taken near a former military air base (New Hampshire, USA) revealed the presence of perfluorononanoic acid (PFNA) at 1 ppb and traces of perfluoroheptanoic acid. CGC-DART enabled one-step extraction of PFASs in minutes with mL sample volumes at low concentrations as shown for the standards and contaminated soil and water samples. DART-MS combined with Kendrick mass defect analysis enabled accurate identification of PFASs without chromatography steps, as fluorinated compounds are mass deficient and easily distinguished over background signal.

A data mining method from pyrolyzed products: Pyrolysis-gas chromatography-photoionization-high resolution time-of-flight mass spectrometry and kendrick mass defect analysis for polymer semiconductor poly(3-hexylthiophene)

Structural analysis of polymer semiconductors is important to improve their properties but applicable method is limited due to insolubility. Here an efficient and comprehensive data mining method for such materials is developed by using pyrolysis-gas chromatography- high resolution mass spectrometry (Py-GC-HRMS). Py-GC-HRMS analysis of a polymer semiconductor poly(3-hexylthiophene) (P3HT) was performed and pyrogram with vast number of peaks was obtained. Normally in analysis of Py-GC-HRMS results, a mass spectrum is obtained for one peak in chromatogram. In one of our techniques, the mass spectra in wide time range were integrated to one mass spectrum including the information of over 300 compounds. Analysis of the mass spectrum could be using Kendrick mass defect (KMD) analysis. By making KMD plots, the compounds categorized into 29 groups depending on the structures of thiophene derivative moieties. As a result, chemical composition analysis for 29 compounds lead to peak assignment for 277 compounds in short time. Furthermore, these compounds could be categorized depending on different structural features (e.g., number of sulfur atoms, degree of unsaturation) by using appropriate divisors, leading to understanding of relationship between structural features of compounds and their retention time. The measurement without GC column provided the similar information about compounds, although the information of the retention time was lost. These methods can be one of key analysis method for insoluble polymer materials.

MFAssignR: Molecular formula assignment software for ultrahigh resolution mass spectrometry analysis of environmental complex mixtures

Ultrahigh resolution mass spectrometry is widely used for nontargeted analysis of complex environmental and biological mixtures, such as dissolved organic matter, due to its unparalleled ability to provide accurate mass measurements. Accurate and efficient characterization of these mixtures is critical to being better able to evaluate their effect on human health and climate. This characterization requires accurate mass signals free from isobaric interferences, instrument noise, and mass measurement biases, allowing for molecular formula identification. To address this need, an open source post-processing pipeline for ultrahigh resolution mass spectra of environmental complex mixtures software was developed. MFAssignR contains functions that perform noise estimation, 13C and 34S polyisotopic mass filtering, mass measurement recalibration, and molecular formula assignment as part of a consistent data processing environment. Novel applications of mass defect analysis were used in the functions for noise estimation and isotope pair identification. Using formula extensions, exact mass measurements are converted to unambiguous molecular formulas via data dependent pathways, reducing a priori decisions. Optional molecular formula ambiguity and multiple non-oxygen heteroatoms are provided for custom user applications, including isotopically labeled reactive species, halogen-containing species, or tandem ultrahigh resolution mass spectrometry. This represents uncommon flexibility for an open-source software package. To evaluate the performance of MFAssignR, it was used to characterize a sample of biomass burning influenced organic aerosol and the results were compared to those from other available methods of molecular formula assignment and noise estimation. The differences between the methods are described here. Overall, the inclusion of a full pipeline of data preparation functions and the data-dependent ambiguity reductions in MFAssignR render excellent results and make MFAssignR well-suited for the consistent and efficient analysis of environmental complex mixtures. MFAssignR is publicly available via GitHub.

Nitric oxide reactivity accounts for N-nitroso-ciprofloxacin formation under nitrate-reducing conditions

The formation of N-nitroso-ciprofloxacin (CIP) was investigated both in wastewater treatment plants including nitrification/denitrification stages and in sludge slurry experiments under denitrifying conditions. The analysis of biological wastewater treatment plant effluents by Kendrick mass defect analysis and liquid chromatography - high resolution - mass spectrometry (LCHRMS) revealed the occurrence of N-nitroso-CIP and N-nitroso-hydrochlorothiazide at concentration levels of 34 ± 3 ng/L and 71 ± 6 ng/L, respectively. In laboratory experiments and dark conditions, produced N-nitroso-CIP concentrations reached a plateau during the course of biodegradation experiments. A mass balance was achieved after identification and quantification of several transformation products by LCHRMS. N-nitroso-CIP accounted for 14.3% of the initial CIP concentration (20 µg/L) and accumulated against time. The use of 4,5-diaminofluorescein diacetate and superoxide dismutase as scavengers for in situ production of nitric oxide and superoxide radical anion respectively, revealed that the mechanisms of formation of N-nitroso-CIP likely involved a nitrosation pathway through the formation of peroxynitrite and another one through codenitrification processes, even though the former one appeared to be prevalent. This work extended the possible sources of N-nitrosamines by including a formation pathway relying on nitric oxide reactivity with secondary amines under activated sludge treatment.

Finding the proverbial needle: Non-targeted screening of synthetic opioids in equine plasma.

Synthetic opioids are a class of compounds that are of particular concern due to their high potency and potential health impacts. With the relentless emergence of new synthetic opioid derivatives, non-targeted screening strategies are required that do not rely on the use of library spectra or reference materials. In this study, product ion searching, and Kendrick mass defect analysis were investigated for non-targeted screening of synthetic opioids. The estimated screening cut-offs for these techniques ranged between 0.05 and 0.1 ng/mL. These techniques were designed to not be reliant on a particular vendor's software, meaning that they can be applied to existing drug screening protocols, without requiring the development and validation of new analytical procedures. The efficacy of the developed techniques was tested through blind trials, with spiked samples inserted amongst authentic plasma samples, which demonstrated the usefulness of these methods for high-throughput screening. The use of a non-targeted screening workflow that contains complementary techniques can increase the likelihood of detecting compounds of interest within a sample, as well as the confidence in detections that are made.

Multistep Analysis of Diol-LC-ESI-HRMS Data Reveals Proanthocyanidin Composition of Complex Plant Extracts (PAComics).

Proanthocyanidins (PACs) are complex oligomeric or polymeric phenolic biopolymers composed of flavan-3-ol building blocks. PACs exert manifold functional bioactivities and are assessed as bioactive ingredients in a variety of food products, beverages, medicinal plants, and phytopharmaceuticals. Although analytical methods for PACs with low degree of polymerization (DP) are well established, a lack of methods for the detailed analysis of higher oligomers and polymers from complex plant extracts is obvious. For this, the present study investigated PAC-enriched extracts from four different plants, traditionally used for medical purpose (Lime flower, Hawthorn leaf and flower, Japanese Wisteria fruits without seeds, and Common Sorrel herb). PACs were separated on diol stationary-phase high-performance liquid chromatography according to the respective DP and detected by fluorescence and quadrupole time-of-flight mass spectrometry (qTOF-MS). The qTOF-MS contour plots [tR → m/z] provided a sufficient overview on the respective PAC distribution. Subsequently, high-resolution mass spectrometry data were used for Kendrick mass defect (KMD) analysis, with (epi)catechin, the main flavan-3-ol unit in PACs, as the reference unit. The resulting KMD plots enabled an elucidation of the general polymer chain composition with regard to DP, building blocks, and potential secondary modifications (e.g., galloylation). Subsequently, analysis of MS2 fragmentation patterns of PAC oligomers confirmed the structural features obtained from the KMD plots. While Lime flower contained oligomeric A- and B-type PACs, composed of (epi)catechin and (epi)afzelechin, Japanese Wisteria fruit contained PACs consisting of three different hydroxylated flavan-3-ols. Cinchonains, A-type PACs, and B-type PACs were detected in the Hawthorn plant material. Galloylated oligo- and polymeric PACs were detected in Common Sorrel herb. This multistep analysis reveals collective insights into the PAC composition of the extracts. The protocol offers a fast and reliable methodology to be used in a standard laboratory. On the other hand, this methodology reaches its limits for higher oligomeric PACs, and further optimization is necessary for a better detection of the polymers, as the optimal DP cluster detection depends on the resolution of diol stationary-phase chromatography and is therefore limited.

Chemical compositional analysis of soil fulvic acids using Fourier transform ion cyclotron resonance mass spectrometry.

Soil fulvic acids (FAs) are considered to be a highly reactive pool of soil organic matter. The functions of FAs are related to their chemical structures, the details of which are largely unidentified. To better understand them, Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) must be a useful but generally unused tool. The structural properties of the components of five FA samples from a variety of soils were determined using FTICR-MS with negative-mode electrospray ionization. The peaks were assigned to molecular formulae, which were categorized into seven compound groups based on the H/C-O/C van Krevelen diagram. Ramp 13 C cross polarization/magic angle spinning nuclear magnetic resonance (NMR) spectra with phase-adjusted spinning side bands were also recorded to estimate the C composition. From FTICR-MS, molecular formulae were assigned to 1746-2605 peaks across the m/z range of 200-700. Those aligned in the lignin-like, tannin-like, and condensed aromatic regions of the van Krevelen diagram accounted for 49-58%, 4-20%, and 18-39% of the total peak magnitude, respectively. The proportion of the summed peak magnitudes that were detected in the lignin-like and condensed aromatic regions correlated positively to the aromatic C% as estimated by 13 C NMR. From Kendrick mass defect analysis using a carboxyl group, 94 molecular formulae were assigned to condensed aromatic acids, of which the maximum ring number was 4-7, as potential structures. A high proportion of lignin-like formulae and condensed aromatics, including those probably condensed aromatic acids with small ring numbers, as well as the existence of tannin-like formulae, which were generally lacking in soil humic acids, was suggested as a common feature of soil FAs.