Mass Error Research Articles

Energy-optimal speed control for connected electric buses considering passenger load

Electric buses (EBs) have gained increasing popularity in public transportation services due to zero-emission and less noise. However, range limitations have substantially hindered the mass adoption of EBs. How to reduce energy consumption is a pressing challenge for bus operations and businesses. This paper develops a whole trip energy-optimal speed control model for connected electric buses passing multiple signalized intersections considering the passenger load, with the help of advanced connected technology. First, the energy consumption related to speed, acceleration and statical features is investigated based on longitudinal dynamics. Then, the energy-optimal speed control model is developed that comprehensively considers passenger load, stop-free passage through multiple signalized intersections and constraints such as travel speed, acceleration, arrival time at stations and intersections. The model is simplified and recast into a mixed-integer linear programing (MILP) with high computational efficiency. Finally, numerical experiments show that the model could reduce energy consumption by approximately 40% compared to the greedy strategy. Sensitive analysis indicates that the model's performance is robust to vehicle mass and forecast error in passenger load.

Joint Estimation of Mass and Center of Gravity Position for Distributed Drive Electric Vehicles Using Dual Robust Embedded Cubature Kalman Filter

The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.

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.

Novel automated identification and quantification database using liquid chromatography quadrupole time-of-flight mass spectrometry for quick, comprehensive, cheap and extendable organic micro-pollutant analysis in environmental systems

In order to protect human health and the environment, highly efficient, low-cost, labor-saving, and green analysis of toxic chemicals are urgently required. To achieve this objective, we have developed a novel database-based automated identification and quantification system (AIQS) using LC-QTOF-MS. Since the AIQS uses retention times (RTs), exact MS and MS-MS spectra, and calibration curves of 484 chemicals registered in the database instead of the use of standards, the targets can be determined with low-cost in a short time. The AIQS uses Sequential Window Acquisition of All Theoretical Fragment-ion Spectra as an acquisition method by which we can obtain accurate MS and MS-MS spectra of all detectable substances in a sample with minimal interference from co-eluted peaks. Identification is certainly done using RTs, mass error, ion ratios (a precursor to two product ions), and accurate MS and MS-MS spectra. Consequently, the chance of misidentification is very low even in dirty samples. To examine the accuracy of the AIQS, two collaborative tests were conducted. The first test used 208 pesticide standards at two concentrations (10 and 100 ng mL−1) using 7 instruments, and showed that average trueness was 106 and 95.2%, respectively, with relative standard deviations of 90% of the test compounds below 30%. The second collaborative study involved 5 laboratories carrying out recovery tests on 200 pesticides using 10 river waters. The average recovery was 71.6%; this was 15% lower than that using purified water probably due to the matrix effects. The average relative standard deviation was 30% worse than that of measurement of the standards. Both the recovery and reproducibility, however, satisfied the criteria of Analytical Method Validity Guidelines, Ministry of Health, Labour and Welfare, Japan. Instrument detection limits of 96% of the registered compounds are below 10 pg. The AIQS allows for easy addition of new substances and retrospective analysis after their addition. The results applied to actual samples showed that the AIQS has sufficient identification and quantification performance as a target screening method for a large number of substances in environmental samples.

Analysis of N-nitrosodimethylamine in metformin hydrochloride products by high-resolution accurate mass gas chromatography mass spectrometry.

The high resolving power of the Orbitrap mass spectrometer in a high-resolution accurate mass gas chromatography (HRAM-GC-MS) system provides greater selectivity and sensitivity for the identification and quantification of volatile analytes at low parts per billion (ppb) levels. Hence, it can be applied for the analysis of pharmaceutical impurities like N-nitrosodimethylamine (NDMA) in metformin hydrochloride products (METs). Different METs extracted by a dichloromethane/aqueous system were analyzed by HRAM-GC-MS under softer electron ionization (EI) at 30 eV. The accurate masses of NDMA and its internal standard NDMA-d6 were analyzed by full scan and targeted selected ion monitoring modes under 60 000 and 30 000 full width at half maximum at m/z 200, respectively. Data acquisition and processing were managed by Xcalibur and Trace Finder software, respectively. Limits of detection (LOD) and quantification (LOQ) at 10 and 20 ng/g were achieved, which is below the allowed daily intake of 32 ng/g. The mass errors measured from experimental data were within ±2 ppm of the theoretical values over a period of a week. Sample analysis showed that 180 out of 212 samples (85%) were below LOD and 15 out of 212 samples (7 %) were within LOD and LOQ. Only 17 samples (8%) were found to be above LOQ, comprising one active pharmaceutical ingredient (API), five immediate-release METs and 11 extended-released METs. Amongst these, seven extended-release METs and one API exceeded the daily allowed intake, 32 ng/g. The validated method has been successfully applied for NDMA analysis in various forms of METs. The method is rather straightforward without an additional clean-up step. The scope can also be extended to other volatile impurities in finished pharmaceutical products.

A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network

More and more wind turbines are installed in cold regions because of better wind resources. In these regions, the high humidity and low temperatures in winter will lead to ice accumulation on the wind turbine impeller. A different icing location or mass will lead to different natural frequency variations of the impeller. In order to monitor the icing situation in time and in advance, a method based on depth neural network technology to predict the icing mass is explored and proposed. Natural-environment icing experiments and iced-impeller modal experiments are carried out, aiming at a 600 W wind turbine, respectively. The mapping relationship between the change rate of the natural frequency of the iced impeller at different icing positions and the icing mass is obtained, and the correlation coefficients are all above 0.93. A deep neural network (DNN) prediction model of ice-coating quality for the impeller was constructed with the change rate of the first six-order natural frequencies as the input factor. The results show that the MAE and MSE of the trained model are close to 0. The average prediction error of the DNN model is 4.79%, 9.35%, 3.62%, 1.63%, respectively, under different icing states of the impeller. It can be seen that the DNN shows the best prediction ability among other methods. The smaller the actual ice-covered mass of the impeller, the larger the relative error of the ice-covered mass predicted by the DNN model. In the same ice-covered state, the relative error will decrease gradually with the increase in ice-covered mass. In a word, using the natural frequency change rate to predict the icing quality is feasible and accurate. The research achievements shown here can provide a new idea for wind farms to realize efficient and intelligent icing monitoring and prediction, provide engineering guidance for the wind turbine blade anti-icing and deicing field, and further reduce the negative impact of icing on wind power generation.

Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition.

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food's nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT.

Effective Amino Acid Sequencing of Intact Filgrastim by Multimodal Mass Spectrometry with Topdownr.

Therapeutic proteins, known as biologicals, are an important and growing class of drugs for treatment of a series of human ailments. Amino acid sequence variants of therapeutic proteins can affect their safety and efficacy. Top-down mass spectrometry is well suited for the sequence analysis of intact therapeutic proteins. Fine-tuning of tandem mass spectrometry (MS/MS) fragmentation conditions is essential for maximizing the amino acid sequence coverage but is often time-consuming. We used topdownr, an automated and integrated multimodal approach to systematically assess high mass accuracy MS/MS fragmentation parameters to characterize filgrastim, a 19 kDa recombinant human granulocyte colony-stimulating factor used in treating neutropenia. A total of 276 different MS/MS conditions were systematically tested, including the following parameters: protein charge state, HCD and CID collision energy, ETD reaction time, ETD supplemental activation, and UVPD activation time. Stringent and accurate evaluation and annotation of the MS/MS data was achieved by requiring a fragment ion mass error of 5 ppm, considering reproducible N- and C-terminal fragment ions only, and excluding internal fragment ion assignments. We report the first EThcD and UVPD MS/MS analysis of intact filgrastim, and these two techniques combined resulted in 98% amino acid sequence coverage. By combining all tested fragmentation modes, we obtained near-complete amino acid sequence coverage (99.4%) of intact filgrastim.

Trinity I: self-consistently modelling the dark matter halo–galaxy–supermassive black hole connection from z = 0–10

ABSTRACT We present Trinity, a flexible empirical model that self-consistently infers the statistical connection between dark matter haloes, galaxies, and supermassive black holes (SMBHs). Trinity is constrained by galaxy observables from 0 < z < 10 [galaxies’ stellar mass functions, specific and cosmic star formation rates (SFRs), quenched fractions, and UV luminosity functions] and SMBH observables from 0 < z < 6.5 (quasar luminosity functions, quasar probability distribution functions, active black hole mass functions, local SMBH mass–bulge mass relations, and the observed SMBH mass distributions of high-redshift bright quasars). The model includes full treatment of observational systematics [e.g. active galactic nucleus (AGN) obscuration and errors in stellar masses]. From these data, Trinity infers the average SMBH mass, SMBH accretion rate, merger rate, and Eddington ratio distribution as functions of halo mass, galaxy stellar mass, and redshift. Key findings include: (1) the normalization and the slope of the SMBH mass–bulge mass relation increases mildly from z = 0 to z = 10; (2) The best-fitting AGN radiative+kinetic efficiency is ∼0.05–0.06, but can be in the range ∼0.035–0.07 with alternative input assumptions; (3) AGNs show downsizing, i.e. the Eddington ratios of more massive SMBHs start to decrease earlier than those of lower mass objects; (4) The average ratio between average SMBH accretion rate and SFR is ∼10−3 for low-mass galaxies, which are primarily star-forming. This ratio increases to ∼10−1 for the most massive haloes below z ∼ 1, where star formation is quenched but SMBHs continue to accrete.

Considerations for Achieving Equivalence between Hub- and Roller-Type Dynamometers for Vehicle Exhaust Emissions

Emissions from vehicles can be measured on the road or in laboratories using dynamometers that simulate the forces that a vehicle is subject to while driving on the road. In the light-duty vehicle regulations, only roller-type dynamometers are allowed. For hub-type dynamometers, due to the direct connection of the dynamometers to the wheel hubs, additional parameters that are used are rotational mass, dynamic wheel radius, and the tire force–slip relationship. Following up on an experimental study which showed that equivalent emission results can be achieved between roller- and hub-type dynamometers, this work presents and evaluates methods to determine parameters used by a hub-type dynamometer for mimicking roller-type dynamometer behavior. It also discusses methods to determine the parameters to simulate specific road conditions or when using only a hub-type dynamometer. The results show that using a constant dynamic radius for each wheel and a linear tire force–slip relationship is sufficient for emission measurement because typical errors in these parameters are practically negligible. A typical error in rotational mass results in a minor error in the determined forces during coast down, but the typical accuracy of this parameter is in parity with the difference allowed in the regulation. The final conclusion is that using the information already stated in the certificate of conformity (CoC) of the vehicle (for the coast down), and reasonably set parameters for wheel dynamic radius and the tire slip–force relationship, hub-type dynamometers should yield equivalent results to roller-type dynamometers.

A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences.

Within the Human Biomonitoring for Europe initiative (HBM4EU), a study to determine new biomarkers of exposure to pesticides and to assess exposure patterns was conducted. Human urine samples (N=2,088) were collected from five European regions in two different seasons. The objective of the study was to identify pesticides and their metabolites in collected urine samples with a harmonized suspect screening approach based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) applied in five laboratories. A combined data processing workflow included comprehensive data reduction, correction of mass error and retention time (RT) drifts, isotopic pattern analysis, adduct and elemental composition annotation, finalized by a mining of the elemental compositions for possible annotations of pesticide metabolites. The obtained tentative annotations (n=498) were used for acquiring representative data-dependent tandem mass spectra (MS2) and verified by spectral comparison to reference spectra generated from commercially available reference standards or produced through human liver S9 in vitro incubation experiments. 14 parent pesticides and 71 metabolites (including 16 glucuronide and 11 sulfate conjugates) were detected. Collectively these related to 46 unique pesticides. For the remaining tentative annotations either (i) no data-dependent MS2 spectra could be acquired, (ii) the spectral purity was too low for sufficient matching, or (iii) RTs indicated a wrong annotation, leaving potential for more pesticides and/or their metabolites being confirmed in further studies. Thus, the reported results are reflecting only a part of the possible pesticide exposure.

Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection.

Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.

Investigation of the scavenging process in two-stroke uniflow scavenging marine engines by a real-time multi-stage model

The increasing demand of digital twin marine engine requires real-time physical models for prediction. In two-stroke uniflow scavenging marine engines, a real-time physical model for the scavenging process is rarely found since it is difficult to describe the complex in-cylinder air motion. Without an accurate prediction of the fresh air loss and residual burned gas in the cylinder, the precision of combustion and emission simulation cannot be guaranteed as well. In a marine engine digital twin system, the scavenging ratio and fresh air skip are the vital for the further simulation of combustion and emission. To predict them in real-time, a novel zero-dimensional (0D) multi-stage scavenging model is proposed in this article. In the cylinder zone, the complex gas motion is ideally divided to four stages: exhaust gas blowdown, perfect displacement, displacement-mixing, and perfect mixing. The duration of each stage is determined by the engine design and control parameters. Then, the thermodynamic conditions at each stage are simulated to obtain the mass of residual burned gas, fresh air inlet, and outlet. The results of the 0D model agree well with the results of the CFD simulation, and the mean relative errors of temperature, pressure, and total mass do not exceed 3%, 4%, and 2%, respectively, which indicates the potential for the two-stroke marine engine digital twin system. It could also be applied in a control-oriental engine model or engine diagnostics in the future.

Identification criteria, in-house compound libraries and SWATH: A benchmark implementation of quadrupole time-of-flight mass spectrometry in routine toxicological casework

To establish the identification criteria sufficient for the daily and routine reporting of compounds using Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH) in forensic casework. Liquid chromatography coupled with High Resolution Mass Spectrometry (LC-HRMS) provides sensitive full-spectrum MS and MS/MS data for the identification of known and previously unknown compounds. However, the daily and routine implementation of methods that use LC-HRMS techniques in routine analysis can be challenging due to the establishment of identification criteria and confidence in reporting suspected positive detections. With the large amount of data that is collected during SWATH, it is important to have a well-developed and validated identification criterion, that is also high throughput. To identify previously published identification criterion, literature searches were conducted for the keywords Quadrupole Time of Flight, Toxicology, QTOF, Forensic Toxicology, Sequential Window Acquisition, SWATH and Mass Spectrometry. A total of 11 articles were evaluated regarding the use of SWATH in clinical or forensic toxicology. Based off these findings, we performed internal studies to validate identification criteria. Curation of an internal library were conducted using available reference standards. For compound identification and confirmation, criterion acceptability of exact mass ±5 ppm, retention time (RT) ±0.5 min, and library match for MS and MSMS including comparison of isotopic pattern and product ion pattern to the library. A combined weighted score (CWS) for each candidate species was calculated based on these criteria with a reporting score of >75% set. In reviewing the literature of SWATH methods that have already been developed, it has been found that the most common target drug classes are stimulants, benzodiazepines, opiates, NPS’, cannabinoids and anti-depressants. In the acquisition process, the number of SWATH windows ranges from 12-30. Mass error, isotopic ratio difference, retention time error and library scores were used as the identification criteria for all studies. HRMS acquisition is data-rich, voluminous and complicated. We have adopted categories to delineate the identification and reportion of compounds through the QTOF Method: Target Qualitative, Target Quantitative, Target Monitored, Suspect Screening and Unknown Screening. Each category must pass a pre-set identification criterion that resulted from identification criteria studies. The described method uses five levels of identification and confirmation, and three types of mass spectral libraries consisting of a main of an in-house curated library, updated quarterly. New targets are added through a vigorous process including interference studies, as many NPS show similar fragmentation and identical molecular weights. Interference studies may result in the grouping of compounds that have the same exact mass, identical fragmentation, and/or small retention time differences. For HRMS analysis, robust assessment of accurate mass information and reference to in-house libraries are essential in the accurate identification and reporting of drugs in forensic toxicological casework.

A new strategy for efficient retrospective data analyses for designer benzodiazepines in large LC-HRMS datasets

To develop a retrospective data analysis (RDA) workflow tailored to benzodiazepines and use this to identify designer and uncommon benzodiazepines (DBZD) in blood samples from driving-under-the-influence-of-drugs (DUID) cases analyzed from 2014 to 2020. The RDA workflow was developed from a training set of hits in ultra-high performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to 13 common benzodiazepines that also had been analyzed with a complementary UHPLC- tandem mass spectrometry (MS/MS) method. Raw data from all UHPLC-QTOF-MS data files were available from a structured query language (SQL) data archive. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters for the RDA. The RDA was finally used to screen for 47 DBZD in 13514 UHPLC-QTOF-MS data files from DUID cases analysed from 2014 to 2020, with filters on retention time window, count level, and mass error. Filters for retention time and mass filters was narrower and the count limit lower than what is used for our in-house drug screening method. Additional filters for diagnostic fragment ions, isotopic pattern, and residual precursor ion in high-energy spectra were evaluated, but not used in the final RDA. Extracting hits from 13514 LC-HRMS data files with simple count, mass, and retention time filters for 47 compounds was executed in less than one minute. Sixteen DBZD were detected in the UHPLC-QTOF-MS data files. 47 identifications had been confirmed by complementary methods when the cases were open (confirmed positive finding), and 43 targets were not reported, when the cases were open (tentative positive finding). Metabolites were not considered targets, once their parent drug was detected. The most common tentative and confirmed findings were etizolam ( n = 26), phenazepam ( n = 13), lorazepam ( n = 9), and flualprazolam ( n = 8). This method efficiently found DBZD in previously acquired UHPLC-QTOF-MS data files, with only 9 false positive hits. Retention time is a required filter in the RDA workflow and therefore relies on availability of the standard or a clean seizure. Alternatively, a workflow relying on diagnostic fragment ions could allow the search for emerging DBZD. Using predicted or online sourced analytical data, for ranking of hits, was not necessary considering the low number of false positive hits. The sensitivity and selectivity can be attributed to the distinguishable chemical nature of benzodiazepines. The RDA workflow can easily be adjusted to other groups of NPS, but would require a new training set and other filter settings. The three most frequently detected DBZD in this study have all been placed under international control. The prevalence of detected DBZD from 2014-2020 remains very low in DUID samples in Eastern Denmark when comparing with conventional benzodiazepines. The overall framework presented in this study to tailor RDA workflows to specific groups of compounds from historic data of similar compounds We developed an efficient and scalable retrospective data analysis workflow for DBZD that can easily be tailored and optimized for other groups of NPS.

Improved Dynamic Range, Resolving Power, and Sensitivity Achievable with FT-ICR Mass Spectrometry at 21 T Reveals the Hidden Complexity of Natural Organic Matter.

Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR MS) is the only mass analyzer that can resolve the molecular complexity of natural organic matter at the level of elemental composition assignment. Here, we leverage the high dynamic range, resolving power, resistance to peak coalescence, and maximum ion number and ion trapping duration in a custom built, 21 tesla hybrid linear ion trap /FT-ICR mass spectrometer for a dissolved organic matter standard (Suwanne River Fulvic Acid). We compare the effect of peak-picking threshold (3σ, 4σ, 5σ, and 6σ) on number of elemental composition assignments, mass measurement accuracy, and dynamic range for a 6.3 s transient across the mass range of m/z 200-1200 that comprises the highest achieved resolving power broadband FT-ICR mass spectrum collected to date. More than 36 000 species are assigned with signal magnitude greater than 3σ at root-mean-square mass error of 36 ppb, the most species identified reported to date for dissolved organic matter. We identify 18O and 17O isotopologues and resolve isobaric overlaps on the order of a few electrons across a wide mass range (up to m/z 1000) leveraging mass resolving powers (3 000 000 at m/z 200) only achievable by 21 T FT-ICR MS and increased by ∼30% through absorption mode data processing. Elemental compositions unique to the 3σ span a wide compositional range of aromaticity not detected at higher peak-picking thresholds. Furthermore, we leverage the high dynamic range at 21 T FT-ICR MS to provide a molecular catalogue of a widely utilized reference standard (SRFA) to the analytical community collected on the highest performing mass analyzer for complex mixture analysis to date. This instrument is available free of charge to scientists worldwide.

Intercomparison study of fragmentation pathways and fragment ion structures for screening of illegal drugs and their novel analogues used to adulterate dietary supplements using liquid chromatography/quadrupole time-of-flight mass spectrometry.

As public interest in health and immunity has increased in recent years, so has the demand for dietary supplements. However, supplements adulterated with illegal drugs and their novel analogues are being sold even as the pharmacological efficacies of these drugs are being advertised. Since the use of these illegal compounds can have serious side effects, they pose a risk to public health. Hence, in this study, we propose a strategy for proactively testing drugs and novel analogues that may be added to dietary supplements illegally. The optimal conditions for liquid chromatography/quadrupole time-of-flight mass spectrometry were explored to determine the fragmentation patterns for 60 compounds. The optimal conditions were established by comparing the areas and heights of the precursor ion peaks at a fragmentor voltage of 125 or 175 V. Furthermore, the optimized spectra were acquired using collision energies of 1 to 50 eV. The energy value was selected based on the condition that the mass error of the precursor ions is 10ppm or lower. The fragmentation pathway of each product ion and its chemical structure were predicted and determined. In addition, the obtained structural information was used to screen 18 seized samples. Based on the precursor ions and the corresponding fragmentation patterns, the unknown compounds present in the samples were identified as desulfonylchlorosildenafil and propoxyphenylthiohydroxy homosildenafil. We obtained mass spectrometry-based information for various compounds by predicting the fragmentation pathways and chemical structures of their fragment ions. Subsequently, based on the obtained structural information, we tested several seized samples and were able to detect two novel analogues in four of the samples. Therefore, the proposed approach is suitable for quickly and accurately identifying the unknown compounds detected in real-world samples.

Development of an Internal Calibration Algorithm for Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter.

In order to obtain a spectrum with high mass accuracy, an internal calibration of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is inevitable. This in turn is critical for subsequent data processing and is generally performed using the commercial instrument software DataAnalysis in the benchmark calibration mode. However, no methodological study has systemically addressed the automated internal calibration of FTICR-MS spectra for dissolved organic matter (DOM) from different sources such as terrestrial and aquatic environments. In this study, a new piecewise algorithm, FTMSCalibrate, was developed to automatically calibrate FTICR-MS spectra in both positive and negative ion modes. FTMSCalibrate was found to reproduce 91.7% ± 4.4% (referred to as the true positive ratio) of the chemical formulas obtained by calibration using manual DataAnalysis. In addition to significantly reducing the mass error, FTMSCalibrate is more accurate in terms of the molecular formula assignment for low m/z peaks than Formularity and MFAssignR. FTMSCalibrate was compatible with deprotonated ions for FTICR-MS spectra in the negative ion mode as well as protonated and adduct ions, including Na- and K-adducts, for FTICR-MS spectra in the positive ion mode. These results suggest that FTMSCalibrate publicly available herein is a robust alternative for the internal calibration of FTICR-MS spectra during postdata processing and will facilitate DOM analysis by FTICR-MS.

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro.

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

Remedy for ill-posedness and mass conservation error of 1D incompressible two-fluid model with artificial viscosities

The two-fluid model is widely used to describe two-phase flows in complex systems such as nuclear reactors. Although the two-phase flow was successfully simulated, the standard two-fluid model suffers from an ill-posed nature. There are several remedies for the ill-posedness of the one-dimensional (1D) two-fluid model; among those, artificial viscosity is the focus of this study. Some previous works added artificial diffusion terms to both mass and momentum equations to render the two-fluid model well-posed and demonstrated that this method provided a numerically converging model. However, they did not consider mass conservation, which is crucial for analyzing a closed reactor system. In fact, the total mass is not conserved in the previous models. This study improves the artificial viscosity model such that the 1D incompressible two-fluid model is well-posed, and the total mass is conserved. The water faucet and Kelvin-Helmholtz instability flows were simulated to test the effect of the proposed artificial viscosity model. The results indicate that the proposed artificial viscosity model effectively remedies the ill-posedness of the two-fluid model while maintaining a negligible total mass error.