Mass spectrometry of polymers: A tutorial review.

Ever since the inception of synthetic polymeric materials in the late 19th century, the number of studies on polymers as well as the complexity of their structures have only increased. The development and commercialization of new polymers with properties fine-tuned for specific technological, environmental, consumer, or biomedical applications requires powerful analytical techniques that permit the in-depth characterization of these materials. One such method with the ability to provide chemical composition and structure information with high sensitivity, selectivity, specificity, and speed is mass spectrometry (MS). This tutorial review presents and exemplifies the various MS techniques available for the elucidation of specific structural features in a synthetic polymer, including compositional complexity, primary structure, architecture, topology, and surface properties. Key to every MS analysis is sample conversion to gas-phase ions. This review describes the fundamentals of the most suitable ionization methods for synthetic materials and provides relevant sample preparation protocols. Most importantly, structural characterizations via one-step as well as hyphenated or multidimensional approaches are introduced and demonstrated with specific applications, including surface sensitive and imaging techniques. The aim of this tutorial review is to illustrate the capabilities of MS for the characterization of large, complex polymers and emphasize its potential as a powerful compositional and structural elucidation tool in polymer chemistry.

(Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms.

This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.

High-speed countercurrent chromatography with offline detection by electrospray mass spectrometry and nuclear magnetic resonance detection as a tool to resolve complex mixtures: A practical approach using Coffea arabica leaf extract.

INTRODUCTION: Many secondary metabolites isolated from plants have been described in the literature owing to their important biological properties and possible pharmacological applications. However, the identification of compounds present in complex plant extracts has remained a great scientific challenge, is often laborious, and requires a long research time with high financial cost. OBJECTIVES: The aim of this study was to develop a method that allows the identification of secondary metabolites in plant extracts with a high degree of confidence in a short period of time. MATERIAL AND METHODS: In this study, an ethanolic extract of Coffea arabica leaves was used to validate the proposed method. Countercurrent chromatography was chosen as the initial step for extraction fractionation using gradient elution. Resulting fractions presented a variation of compounds concentrations, allowing for statistical total correlation spectroscopy (STOCSY) calculations between liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) and NMR across fractions. RESULTS: The proposed method allowed the identification of 57 compounds. Of the annotated compounds, 20 were previously described in the literature for the species and 37 were reported for the first time. Among the inedited compounds, we identified flavonoids, alkaloids, phenolic acids, coumarins, and terpenes. CONCLUSION: The proposed method presents itself as a valid alternative for the study of complex extracts in an effective, fast, and reliable way that can be reproduced in the study of other extracts.

A History of Molecular Level Analysis of Natural Organic Matter by FTICR Mass Spectrometry and The Paradigm Shift in Organic Geochemistry.

Natural organic matter (NOM) is a complex mixture of biogenic molecules resulting from the deposition and transformation of plant and animal matter. It has long been recognized that NOM plays an important role in many geological, geochemical, and environmental processes. Of particular concern is the fate of NOM in response to a warming climate in environments that have historically sequestered carbon (e.g., peatlands and swamps) but may transition to net carbon emitters. In this review, we will highlight developments in the application of high-field Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) in identifying the individual components of complex NOM mixtures, focusing primarily on the fraction that is dissolved in natural waters (dissolved organic matter or DOM). We will first provide some historical perspective on developments in FTICR technology that made molecular-level characterizations of DOM possible. A variety of applications of the technique will then be described, followed by our view of the future of high-field FTICR MS in carbon cycling research, including a particularly exciting metabolomic approach.

Hormesis mediates platelet-rich plasma and wound healing.

Platelet-rich plasma (PRP) has become an accepted and general wound healing approach with an extremely wide range of applications. Despite considerable diversity in the composition of platelet-rich plasma products that are applied in specific wound healing usage, it is widely recognised that such diverse platelet-rich plasma complex mixtures routinely display hormetic-like biphasic concentrations that are independent of the tissue treated and endpoints measured. The present paper is the first to place the area of platelet-rich plasma-biomedical research and applications within an hormetic framework. The platelet-rich plasma area is also unique as it represents the application of the hormetic concept to the issue of complex biological mixtures.

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter.

Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS.

Comprehensive Two-Dimensional Gas Chromatography with Peak Tracking for Screening of Constituent Biodegradation in Petroleum UVCB Substances.

Petroleum substances, as archetypical UVCBs (substances of unknown or variable composition, complex reaction products, or biological substances), pose a challenge for chemical risk assessment as they contain hundreds to thousands of individual constituents. It is particularly challenging to determine the biodegradability of petroleum substances since each constituent behaves differently. Testing the whole substance provides an average biodegradation, but it would be effectively impossible to obtain all constituents and test them individually. To overcome this challenge, comprehensive two-dimensional gas chromatography (GC × GC) in combination with advanced data-handling algorithms was applied to track and calculate degradation half-times (DT50s) of individual constituents in two dispersed middle distillate gas oils in seawater. By tracking >1000 peaks (representing ∼53-54% of the total mass across the entire chromatographic area), known biodegradation patterns of oil constituents were confirmed and extended to include many hundreds not currently investigated by traditional one-dimensional GC methods. Approximately 95% of the total tracked peak mass biodegraded after 64 days. By tracking the microbial community evolution, a correlation between the presence of functional microbial communities and the observed progression of DT50s between chemical classes was demonstrated. This approach could be used to screen the persistence of GC × GC-amenable constituents of petroleum substance UVCBs.

Capillary electrophoresis coupled to chemometrics for analysis of carbon dots in nanoparticles mixtures.

Since their discovery in 2004, carbon dots (CDs) have attracted attention due to their intrinsic physicochemical properties and the easy synthesis from simple precursors. However, quantification of CDs in mixtures of nanoparticles with similar sizes and surface functionality is still a challenging issue for research applications or regulatory purposes. In this work, CDs and silver nanoparticles were first synthesized under alkaline conditions by using glucose as precursor and capping agent, respectively. Mixtures of these nanoparticles were made at micromolar range, without purification, and then analyzed by CE-DAD, using an electrolyte solution composed of 20 mM sodium borate and 20 mM SDS at pH 8.5, in a total time of <15 min. The three-way electrophoretic data were then decomposed by advanced chemometric models, parallel factor analysis and multivariate curve resolution-alternating least-squares. The explained variances for both models were 95.8% (parallel factor analysis) and 85.3% (multivariate curve resolution-alternating least-squares). In both cases, the quality of the results was verified by the root mean square standard deviation coefficient variation, which resulted lower than 5%, and no significant bias was observed at 95% of statistical confidence. Satisfactory prediction for CDs concentration was obtained with recovery values between 80.0% and 115%.

Associations between Individual Exposure to Fine Particulate Matter Elemental Constituent Mixtures and Blood Lipid Profiles: A Panel Study in Chinese People Aged 60-69 Years.

Dyslipidemia may be a potential mechanism linking fine particulate matter (PM2.5) to adverse cardiovascular outcomes. However, inconsistent associations between PM2.5 and blood lipids have resulted from the existing research, and the joint effect of PM2.5 elemental constituents on blood lipid profiles remains unclear. We aimed to explore the overall associations between PM2.5 elemental constituents and blood lipid profiles and to identify the significant PM2.5 elemental constituents in this association. Sixty-nine elderly people were recruited between September 2018 and January 2019. Each participant completed a survey questionnaire, 3 days of individual exposure monitoring, health examination, and biological sample collection at each follow-up visit. Bayesian kernel machine regression (BKMR) models were used to identify the joint effects of the 17 elemental constituents on blood lipid profiles. Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) levels were significantly increased in older adults when exposed to the mixture of PM2.5 elemental constituents. Copper and titanium had higher posterior inclusion probabilities than other constituents, ranging from 0.76 to 0.90 (Cu) and 0.74 to 0.94 (Ti). Copper and titanium in the PM2.5 elemental constituent mixture played an essential role in changes to blood lipid levels. This study highlights the importance of identifying critical hazardous PM2.5 constituents that may cause adverse cardiovascular outcomes in the future.

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.

Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeutics for its treatment. One of the prospective approaches to NAFLD therapy is to use a mixture of natural compounds. They showed effectiveness in alleviating NAFLD-related conditions including steatosis, fibrosis, etc. However, understanding the mechanism of action of such mixtures is important for their rational application. In this work, we propose a new dereplication workflow for deciphering the mechanism of action of the lignin-derived natural compound mixture. The workflow combines the analysis of molecular components with high-resolution mass spectrometry, selective chemical tagging and deuterium labeling, liver tissue penetration examination, assessment of biological activity in vitro, and computational chemistry tools used to generate putative structural candidates. Molecular docking was used to propose the potential mechanism of action of these structures, which was assessed by a proteomic experiment.

How Well Do We Handle the Sample Preparation, FT-ICR Mass Spectrometry Analysis, and Data Treatment of Atmospheric Waters?

FT-ICR MS (Fourier-transform ion cyclotron resonance mass spectrometry) analysis has shown great potential to aid in the understanding of the extremely high molecular diversity of cloud water samples. The main goal of this work was to determine the differences in terms of formula assignment for analytical (i.e., measurement replicates) and experimental replicates of a given cloud water sample. The experimental replicates, obtained by solid phase extraction, were also compared to the results obtained for freeze-dried samples to evaluate whether the presence of salts interferes with the analysis. Two S/N ratios, generally adopted for atmospheric samples, were evaluated, and three different algorithms were used for assignment: DataAnalysis 5.3 (Bruker), Composer (Sierra Analytics), and MFAssignR (Chemical Advanced Resolution Methods Lab). In contrast to other works, we wanted to treat this comparison from the point of view of users, who usually must deal with a simple list of m/z ratios and intensity with limited access to the mass spectrum characteristics. The aim of this study was to establish a methodology for the treatment of atmospheric aqueous samples in light of the comparison of three different software programs, to enhance the possibility of data comparison within samples.

Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.

The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.

Closed-Loop Recycling Dual-Mode Counter-Current Chromatography with Specified Sample Loading Durations: Modeling of Preparative and Industrial-Scale Separations.

We previously reported on a new counter-current chromatography (CCC) operating mode called closed-loop recycling dual-mode counter-current chromatography (CLR DM CCC), which incorporates the advantages of closed-loop recycling (CLR) and dual-mode (DM) counter-current chromatography and includes sequential separation of compounds in the closed-loop recycling mode with the mobile x-phase and in the inverted-phase counter-current mode with the mobile y-phase. The theoretical analysis of several implementations of this separation method was carried out under impulse sample injection conditions. This study is dedicated to the further development of CLR DM CCC theory applied to preparative and industrial separations, where high-throughput operation is required. Large sample volumes can be loaded via continuous loading within a specified time. To simulate CLR DM CCC separations with specified sample loading durations, equations are developed and presented in "Mathcad" software.

Comprehensive online multicolumn two-dimensional liquid chromatography-diode array detection-mass spectrometry workflow as a framework for chromatographic screening and analysis of new drug substances.

The analysis of complex mixtures of closely related species is quickly becoming a bottleneck in the development of new drug substances, reflecting the ever-increasing complexity of both fundamental biology and the therapeutics used to treat disease. Two-dimensional liquid chromatography (2D-LC) is emerging as a powerful tool to achieve substantial improvements in peak capacity and selectivity. However, 2D-LC suffers from several limitations, including the lack of automated multicolumn setups capable of combining multiple columns in both dimensions. Herein, we report an investigation into the development and implementation of a customized online comprehensive multicolumn 2D-LC-DAD-MS setup for screening and method development purposes, as well as analysis of multicomponent biopharmaceutical mixtures. In this study, excellent chromatographic performance in terms of selectivity, peak shape, and reproducibility were achieved by combining reversed-phase (RP), strong cation exchange (SCX), strong anion exchange (SAX), and size exclusion chromatography (SEC) using sub-2-µm columns in the first dimension in conjunction with several 3.0 mm × 50 mm RP columns packed with sub-3-µm fully porous particles in the second dimension. Multiple combinations of separation modes coupled to UV and MS detection are applied to the LC × LC analysis of a protein standard mixture, intended to be representative of protein drug substances. The results reported in this study demonstrate that our automated online multicolumn 2D-LC-DAD-MS workflow can be a powerful tool for comprehensive chromatographic column screening that enables the semi-automated development of 2D-LC methods, offering the ability to streamline full visualization of sample composition for an unknown complex mixture while maximizing chromatographic orthogonality. Graphical Abstract.

The joint effect of ambient air pollution and agricultural pesticide exposures on lung function among children with asthma.

BACKGROUND: Ambient environmental pollutants have been shown to adversely affect respiratory health in susceptible populations. However, the role of simultaneous exposure to multiple diverse environmental pollutants is poorly understood. OBJECTIVE: We applied a multidomain, multipollutant approach to assess the association between pediatric lung function measures and selected ambient air pollutants and pesticides. METHODS: Using data from the US EPA and California Pesticide Use Registry, we reconstructed three months prior exposure to ambient air pollutants ((ozone (O3), nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter < 2.5 µm (PM2.5) and <10 µm (PM10)) and pesticides (organophosphates (OP), carbamates (C) and methyl bromide (MeBr)) for 153 children with mild intermittent or mild persistent asthma from the San Joaquin Valley of California, USA. We implemented Bayesian kernel machine regression (BKMR) to estimate the association between simultaneous exposures to air pollutants and pesticides and lung function measures (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of vital capacity (FEF25-75)). RESULTS: In BKMR analysis, the overall effect of mixtures (pollutants and pesticides) was associated with reduced FEV1 and FVC, particularly when all the environmental exposures were above their 60th percentile. For example, the effect of the overall mixture at the 70th percentile (compared to the median) was a -0.12SD (-50 mL, 95% CI: -180 mL, 90 mL) change in the FEV1 and a -0.18SD (-90 mL, 95% CI: -240 mL, 60 mL) change in the FVC. However, 95% credible intervals around all of the joint effect estimates contained the null value. CONCLUSION: At this agricultural-urban interface, we observed results from multipollutant analyses, suggestive of adverse effects on some pediatric lung function measures following a cumulative increase in ambient air pollutants and agricultural pesticides. Given the uncertainty in effect estimates, this approach should be explored in larger studies.

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.

Geochemical Significance of Biomarkers in the Methane Hydrate-Bearing Sediments from the Shenhu Area, the South China Sea.

Biomarkers from methane hydrate-bearing sediments can provide vital evidence for microbial activities associated with methanogenesis and their relation to the formation of methane hydrates. However, the former mainly focus on intact polar lipids from these microorganisms, and rarely investigate molecular hydrocarbons such as acyclic isoprenoids and hopanes so far. In this work, the composition of biomarkers in the methane hydrate-bearing sediments in cores SH2B and SH7B from the Shenhu area, the South China Sea (SCS) were identified by gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC×GC-TOFMS). The occurrence of unresolved complex mixtures (UCMs) and 25-norhopane indicate that the organic matters in methane hydrate-bearing sediments underwent a high degree of biodegradation. Although specific biomarkers for methanogens were not identified, the UCMs, 25-norhopane, pristane, phytane, and hopanes can still indicate the microbial activities associated with methanogenesis. These molecular signals suggest that diverse microorganisms, particularly methanogens, were quite vigorous in the methane hydrate-bearing sediments. Further, the biomarkers identified in this study can also be steadily detected from deep oil/gas reservoirs. Considering numerous adjacent oil/gas reservoir systems, fault systems, and mud diapers occurred in the SCS, it can be inferred that microbial activities and deep oil/gas reservoirs may have jointly contributed to the formation of methane hydrate deposits in the SCS.

19F NMR as a Tool for Monitoring Individual Differentially Labeled Proteins in Complex Mixtures.

The ability to monitor the behavior of individual proteins in complex mixtures has many potential uses, ranging from analysis of protein interactions in highly concentrated solutions, modeling biological fluids or the intracellular environment, to optimizing biopharmaceutical co-formulations. Differential labeling NMR approaches, which traditionally use 15N or 13C isotope incorporation during recombinant expression, are not always practical in cases when endogenous proteins are obtained from an organism, or where the expression system does not allow for efficient labeling, especially for larger proteins. This study proposes differential labeling of proteins by covalent attachment of 19F groups with distinct chemical shifts, giving each protein a unique spectral signature which can be monitored by 19F NMR without signal overlap, even in complex mixtures, and without any interfering signals from the buffer or other unlabeled components. Parameters, such as signal intensities, translational diffusion coefficients, and transverse relaxation rates, which report on the behavior of individual proteins in the mixture, can be recorded even for proteins as large as antibodies at a wide range of concentrations.

Diffuse sources of contamination in freshwater fish: Detecting effects through active biomonitoring and multi-biomarker approaches.

Aquatic organisms are usually exposed to a mixture of xenobiotics that may exert a large effect even in low concentrations, and when information is obtained exclusively from chemical analyses the prediction of the deleterious effects is potentially hindered. Therefore, the application of complementary monitoring methods is a priority. Here, in addition to chemical analyses, an active biomonitoring study using multiple biomarker responses in Nile tilapia Oreochromis niloticus was conducted to assess the effects of a contamination gradient along four reservoirs in Iguaçu River. Chemical analysis in the muscle showed high levels of metals in fish from the reservoir closest to an industrialized and environmentally degraded area, however fish exposed to all studied reservoirs showed hepatic alterations (necrosis and inflammatory processes). Also, significant variations of biochemical biomarkers were observed with no clear indication of contamination gradient, since an indicative of higher impact was found in an intermediary reservoir, including high concentrations of biliary polycyclic aromatic hydrocarbons (PAHs). However, nuclear morphological alterations (NMA) were less frequent at the same reservoir. Thus, the multi-biomarker approach allied to active biomonitoring is a practical and important tool to assess deleterious effects of contamination in freshwater, providing data for monitoring and conservation protocols.

Analyses of Complex Mixtures by F1 Homo-Decoupled Diagonal Suppressed Total Correlation Spectroscopy.

A diagonal suppressed F1 decoupled total correlation spectroscopy(TOCSY) experiment is developed for analyses of complex mixtures. In 2D homonuclear correlation, assignment of the cross peaks is crucial for structure elucidation. However, when cross peaks are close to the diagonal peaks in overcrowded spectral regions, their assignment becomes tedious. In complex mixtures, the presence of multiple spectra along with broad and complex proton multiplets owing to homonuclear scalar couplings degrade the resolution to the extent that assignment of various cross peaks becomes tedious or impossible. Herein, a diagonal suppressed total correlation technique with F1 decoupling is presented to improve the resolution of the cross peaks. The resolution of the cross peaks is improved by both diagonal suppression as well as the collapse of the multiplets to singlets. Application of the method to a few mixtures of organic compounds reveals better identification of the cross peaks relative to other TOCSY variants.