Kendrick Mass Defect Analysis Research Articles

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

Revealing Molecular-Scale Structural Changes in Polymer Nanocomposites during Thermo-Oxidative Degradation Using Evolved Gas Analysis with High-Resolution Time-of-Flight Mass Spectrometry Combined with Principal Component Analysis and Kendrick Mass Defect Analysis.

This study introduces a novel method that utilizes evolved gas analysis with time-of-flight mass spectrometry (EGA-TOFMS) coupled with principal component analysis (PCA) and Kendrick mass defect (KMD) analysis, called EGA-PCA-KMD, to analyze complex structural changes in polymer materials during thermo-oxidative degradation. While EGA-TOFMS captures exact mass data related to the degradation components in the temperature-dependent mass spectra of the evolved products, numerous high-resolution mass spectra with large amounts of ion signals and varying intensities provide challenges for interpretation. To address this, we employed mathematical decomposition through PCA to selectively extract information about the ion series specific to the products that evolved from the degradation components. Additionally, KMD analysis was applied to the attribution of the exact mass signals extracted from the PCA, which categorizes and visualizes depending on the molecular compositions in a two-dimensional plot. The complex structural changes of the triblock copolymer thermoplastic elastomer and its nanocomposites containing nanodiamonds during thermo-oxidative degradation were elucidated using EGA-PCA-KMD to demonstrate the effectiveness of this characterization technique for polymer degradation. Furthermore, it is revealed that the formation of rigid matrix-filler interfacial interaction via the π-π stacking and chemical bonds in the nanocomposites contributes to improvement in the stability toward thermo-oxidative degradation. Our results highlight the benefits of EGA-PCA-KMD and provide valuable insights into polymer degradation.

PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data.

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

Rapid Chemical Screening of Microplastics and Nanoplastics by Thermal Desorption and Pyrolysis Mass Spectrometry with Unsupervised Fuzzy Clustering.

The transport and chemical identification of microplastics and nanoplastics (MNPs) are critical to the concerns over plastic accumulation in the environment. Chemically and physically transient MNP species present unique challenges for isolation and analysis due to many factors such as their size, color, surface properties, morphology, and potential for chemical change. These factors contribute to the eventual environmental and toxicological impact of MNPs. As analytical methods and instrumentation continue to be developed for this application, analytical test materials will play an important role. Here, a direct mass spectrometry screening method was developed to rapidly characterize manufactured and weathered MNPs, complementing lengthy pyrolysis-gas chromatography-mass spectrometry analysis. The chromatography-free measurements took advantage of Kendrick mass defect analysis, in-source collision-induced dissociation, and advancements in machine learning approaches for the data analysis of complex mass spectra. In this study, we applied Gaussian mixture models and fuzzy c-means clustering for the unsupervised analysis of MNP sample spectra, incorporating clustering stability and information criterion measurements to determine latent dimensionality. These models provided insight into the composition of mixed and weathered MNP samples. The multiparametric data acquisition and machine learning approach presented improved confidence in polymer identification and differentiation.

New Insights into the Role of Natural Organic Matter in Fe-Cr Coprecipitation: Importance of Molecular Selectivity.

Coprecipitation of Fe/Cr hydroxides with natural organic matter (NOM) is an important pathway for Cr immobilization. However, the role of NOM in coprecipitation is still controversial due to its molecular heterogeneity and diversity. This study focused on the molecular selectivity of NOM toward Fe/Cr coprecipitates to uncover the fate of Cr via Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS). The results showed that the significant effects of Suwannee River NOM (SRNOM) on Cr immobilization and stability of the Fe/Cr coprecipitates did not merely depend on the adsorption of SRNOM on Fe/Cr hydroxides. FT-ICR-MS spectra suggested that two pathways of molecular selectivity of SRNOM in the coprecipitation affected Cr immobilization. Polycyclic aromatics and polyphenolic compounds in SRNOM preferentially adsorbed on the Fe/Cr hydroxide nanoparticles, which provided extra binding sites and promoted the aggregation. Notably, some specific compounds (i.e., polyphenolic compounds and highly unsaturated phenolic compounds), less unsaturated and more oxygenated than those adsorbed on Fe/Cr hydroxide nanoparticles, were preferentially incorporated into the insoluble Cr-organic complexes in the coprecipitates. Kendrick mass defect analysis revealed that the insoluble Cr-organic complexes contained fewer carbonylated homologous compounds. More importantly, the spatial distribution of insoluble Cr-organic complexes was strongly related to Cr immobilization and stability of the Fe/Cr-NOM coprecipitates. The molecular information of the Fe/Cr-NOM coprecipitates would be beneficial for a better understanding of the transport and fate of Cr and exploration of the related remediation strategy.

Generalized Kendrick analysis for improved visualization of atmospheric mass spectral data

Abstract. Mass spectrometry is an important analytical technique within the field of atmospheric chemistry. Owing to advances in instrumentation, particularly with regards to mass-resolving power and instrument response factors (sensitivities), hundreds of different mass-to-charge (m/z) signals are routinely measured. This large number of detected ions creates challenges for data visualization. Furthermore, assignment of chemical formulas to these ions is time consuming and increases in difficulty at the higher m/z ranges. Here, we describe generalized Kendrick analysis (GKA) to facilitate the visualization and peak identification processes for typical atmospheric organic (and to some extent inorganic) compounds. GKA is closely related to resolution-enhanced Kendrick mass defect analysis (REKMD), which introduces a tunable integer into the Kendrick equation that effectively contracts or expands the mass scale. A characteristic of all Kendrick analysis methods is that these changes maintain the horizontal alignment of ion series related by integer multiples of the chosen base unit. Compared to traditional Kendrick analysis, GKA and REKMD use a tunable parameter (“scaling factor”) to alter the mass defect spacing between different homologue ion series. As a result, the entire mass defect range (−0.5 to 0.5) is more effectively used simplifying data visualization and facilitating chemical formula assignment. We describe the mechanism of this transformation and discuss base unit and scaling factor selections appropriate for compounds typically found in atmospheric measurements. We present an open-source graphical user interface (GUI) for calculating and visualizing GKA results within the Igor Pro environment.

Kendrick mass defect analysis - a tool for high-resolution Orbitrap mass spectrometry of native lignin.

Lignin is the second most abundant biopolymer in nature and a promising renewable feedstock for the production of aromatic compounds, composite materials, sorbents, etc. Being a complex mixture of oligomeric molecules with anirregular structure, natural lignin is an extremely difficult object to study. Its molecular level characterization requires advanced analytical techniques among which atmospheric pressure photoionization Orbitrap mass spectrometry holds a promising place. In the present study, Kendrick mass defect (KMD) analysis was proposed to facilitate the visualization and interpretation of Orbitrap mass spectra of the biopolymer on an example of Siberian pine dioxane lignin preparation. The use of the typical guaiacylpropane structure C10H12O4 as a Kendrick base unit made it possible to effectively identify oligomer series with different polymerization degrees and structurally related compounds, as well as to reliably determine the elemental compositions and structures of oligomers with high molecular weights (> 1kDa). For the first time, KMD analysis was applied to the interpretation of the complex tandem mass spectra of lignin oligomers, rapid discrimination of the product ion series, and the establishment of the main collision-induced dissociation pathways. It was demonstrated that especially promising was the use of KMD filtering in the study of broadband fragmentation tandem mass spectra, which allows for the structural characterization of all oligomers with a particular degree of polymerization.

Kendrick mass defect analysis-based data mining technique for trace components in polyolefins observed by pyrolysis-gas chromatography/high-resolution mass spectrometry

Pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) is utilized as an effective technique to characterize additives and degradation products of polyolefins. However, non-target analysis of trace components buried in complex chromatograms or mass spectra is a difficult task without prior information on sample composition. In this report, we propose a data mining method that extracts meaningful trace components by combining Py-GC/time-of-flight MS (Py-GC/TOFMS) and Kendrick mass defect (KMD) analysis. In our first demonstration, we characterized the additives in the polyethylene (PE) cap of a polyethylene terephthalate (PET) beverage bottle using Py-GC/TOFMS. The summed mass spectrum, built by summing all the mass spectra over the entire range of the pyrogram, was converted to a two-dimensional KMD plot. Specific dots on the KMD plot at a distance from the dots relating to a series of hydrocarbon-derived fragment ions suggested the presence of additives. The types of additives could be identified by a library search for the extracted ion chromatograms (EICs) derived from the corresponding mass of the specific dots. The second demonstration was related to the detection of oxidized products in polypropylene (PP) resin. A “Remainders of KM” (RKM) plot was able to highlight the presence of a series of fragment ions of trace PP pyrolysates with one and two oxygen atoms. Relative peak intensities of the ions containing oxygen atoms were useful as an oxidation index to evaluate the degree of degradation in the very early stages of degradation.

Characterization of styrene/methyl methacrylate/n-butyl acrylate terpolymer with Kendrick mass defect analysis in electrospray ionization-ion mobility spectrometry-mass spectrometry.

Synthesis of copolymer is one of the major approaches to establish sophisticated polymers. In copolymer analysis in mass spectrometry (MS), an increase in the number of monomer unit species and/or the polymer molecular weight results in complex mass spectra, and a method is required to solve this problem. In this study, electrospray ionization-ion mobility spectrometry-mass spectrometry was employed to analyze styrene/methyl methacrylate/n-butyl acrylate (St/MMA/nBA) terpolymers via Kendrick mass defect (KMD) analysis. An expanded Fourier transform-based noise reduction method for the copolymer was developed to improve the decrease in the signal/noise ratio observed in the higher m/z region. Low-mass terpolymers (0.7kDa) were identified by comparing the observed and theoretical results using a two-dimensional KMD plot. For the 1.5 kDa terpolymer, the signal overlap, of which KMD value was not matched with the theoretical one, was interpreted by the shift from the theoretical value in the KMD plot. For the 3.0 kDa terpolymers, the compositional candidates were determined by the prediction based on the compositional information of low-mass terpolymers previously analyzed. The 4.5kDa terpolymer was interpreted after the expanded noise filtering. The terpolymers, whose molecular weight was up to 4.5kDa, were successfully characterized at a molecular level. The dependency of the St/MMA/nBA composition on molecular weight was observed; that is, the nBA content decreased with an increase in the molecular weight.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]-, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.

Identifying at molecular scale the pyrolysis heavy components from two lignin monomers

4-hydroxy benzaldehyde (H) and vanillin (G) are typical primary pyrolysis products of β-O-4 lignin dimers with key functional groups that affect the secondary reactions of lignin pyrolysis. In this study, the pyrolysis heavy components from these two lignin monomers were analyzed and identified at molecular scale for the first time with Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR-MS) and compared with lignin-derived results. The detected heavy components were typically phenolic oligomers distributed in the nominal mass range of 200–600 Da with 2–6 aromatic rings. They are assumed to be formed through the re-polymerization of certain GC-MS-detected monomers during pyrolysis. In particular, the extra methoxy group in model compound G allows for more variations of monomer products, which serve as building blocks to form heavy components and char. Two-dimensional Kendrick mass defect (2D KMD) analysis was employed to reveal the evolution of different functional groups. Two evolution pathways were found to be dominant, namely the modification of phenol cores and methoxy groups. It was found that aldehyde groups promoted the evolution of heavy components with more aromatic rings (up to 6), which might serve as precursors for char. This influence was mitigated by the co-existence of methoxy group, which might compete for the linking positions on benzene rings.

Development of a comprehensive two-dimensional liquid chromatographic mass spectrometric method for the non-targeted identification of poly- and perfluoroalkyl substances in aqueous film-forming foams

In this research, we developed an online comprehensive two-dimensional liquid chromatographic (LC × LC) method hyphenated with high-resolution mass spectrometry (HRMS) for the non-targeted identification of poly- and perfluorinated compounds (PFASs) in fire-fighting aqueous-film forming foams (AFFFs). The method exploited the combination of mixed-mode weak anion exchange-reversed phase with a octadecyl stationary phase, separating PFASs according to ionic classes and chain length.To develop and optimize the LC × LC method we used a reference training set of twenty-four anionic PFASs, representing the main classes of compounds occurring in AFFFs and covering a wide range of physicochemical properties. In particular, we investigated different modulation approaches to reduce injection band broadening and breakthrough in the second dimension separation. Active solvent and stationary phase assisted modulations were compared, with the best results obtained with the last approach. In the optimal conditions, the predicted peak capacity corrected for undersampling was higher than three-hundred in a separation space of about 60 min. Subsequently, the developed method was applied to the non-targeted analysis of two AFFF samples for the identification of homologous series of PFASs, in which it was possible to identify up to thirty-nine potential compounds of interest utilizing Kendrick mass defect analysis. Even within the samples, the features considered potential PFAS by mass defect analysis elute in the chromatographic regions discriminating for the ionic group and/or the chain length, thus confirming the applicability of the method presented for the analysis of AFFF mixtures and, to a further extent, of environmental matrices affected by the AFFF.

Novel Ambient Oxidation Trends in Fingerprint Aging Discovered by Kendrick Mass Defect Analysis.

A Kendrick mass defect (KMD) plot is an efficient way to disperse complex high-resolution mass spectral data in a visually informative two-dimensional format which allows for the rapid assignment of compound classes that differ by heteroatom content and/or unsaturation. Fingerprint lipid oxidation has the potential to be used to estimate the time since deposition of a fingerprint, but the mass spectra become extremely complex as the lipids degrade. We apply KMD plot analysis for the first time to sebaceous fingerprints aged for 0–7 days to characterize lipid degradation processes analyzed by MALDI-MS. In addition to the ambient ozonolysis of fingerprint lipids previously reported, we observed unique spectral features associated with epoxides and medium chain fatty acid degradation products that are correlated with fingerprint age. We propose an ambient epoxidation mechanism via a peroxyl radical intermediate and the prevalence of omega-10 fatty acyl chains in fingerprint lipids to explain the features observed by the KMD plot analysis. Our hypotheses are supported by an aging experiment performed in a sparse ozone condition and on-surface Paternò–Büchi reaction. A comprehensive understanding of fingerprint degradation processes, afforded by the KMD plots, provides crucial insights for considering which ions to monitor and which to avoid, when creating a robust model for time since deposition of fingerprints.

Improvement of Kendrick Mass Defect (KMD) Analysis to Accelerate the Polymer Characterization by High-resolution Mass Spectrometry

Improvement of Kendrick Mass Defect (KMD) Analysis to Accelerate the Polymer Characterization by High-resolution Mass Spectrometry

Are resource recovery insects safe for feed and food? A screening approach for bioaccumulative trace organic contaminants.

Most bioaccumulation assessments select one or several compound classes a priori for analysis performed by either liquid or gas chromatography coupled with mass spectrometry (LC-MS or GC-MS). When organisms are exposed to complex mixtures of trace organic contaminants (TOrCs), targeted chemical assays limit understanding of contaminant profiles in biological tissues and associated risks. We used a semi-quantitative suspect-screening approach to assess the bioaccumulation potential of diverse TOrCs in black soldier fly larvae (BSFL) using almond hulls (by-products of the booming almond industry in California) as test substrates. BSFL digestion is gaining traction as a resource recovery strategy to generate animal feed from low-value organic wastes. We screened almond hulls from six California farms for the presence of 5728 TOrCs using high resolution mass spectrometry. We then categorized the risk potential of 46 TOrCs detected in the hulls based on their predicted bioaccumulation, persistence, and toxicity in order to select two hulls for an in situ BSFL bioaccumulation screening study. We analyzed larvae tissues and feeding substrate initially and after 14 days of growth using targeted, suspect-screening, and nontarget-screening methods. The survival rate of BSFL in all rearing reactors was greater than 90%, indicating low toxicity of the substrates to BSFL. Esfenvalerate, cyhalothrin, and bifenthrin were the most abundant pyrethroids quantified (81.7 to 381.6 ng/g-dw) in the hulls. Bifenthrin bioaccumulated in BSFL tissues (14-day bioaccumulation factor, BAF, of 2.17 ± 0.24). For nontarget analysis, kendrick mass defect (KMD) analysis of PFAS homologous series revealed hydrogen-substituted perfluoroalkyl carboxylic acids (H-PFCAs) in the hulls and BSFL tissues after growth. Our approach demonstrates the utility of suspect-screening in chemical safety assessments when organic wastes with highly diverse and variable contaminant profiles are used in resource recovery pipelines.

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.

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.

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.