Proprietary data formats used by vendors of analytical instruments limit data portability, reproducibility, and reuse of raw data. When official application programming interfaces are unavailable, access to such data often relies on black-box reverse engineering, but the underlying strategies are rarely described in a systematic and transparent manner. In this work, we reconstructed the proprietary Chromatec .msdx binary format for gas chromatography - mass spectrometry (GC-MS) data, explicitly documented the reconstruction workflow, and implemented an open-source parser as the msdxr package for R. The parser was validated using test files corresponding to different acquisition modes, and the extracted data showed quantitative agreement with data exported from the vendor software. The utility of the package for GC-MS data handling was demonstrated using an environmental monitoring dataset comprising soil and water samples. A retrospective screening workflow based on automated prioritization of candidate files revealed signals tentatively assigned to polychlorinated biphenyls in previously acquired GC-MS data, without sample reanalysis or manual data conversion. The results show that reconstructing proprietary analytical data formats can support reproducible data handling, facilitate integration with advanced data-processing workflows, and increase the long-term value of datasets stored in vendor-specific formats.

Metabolomic aging clocks—quantitative models that estimate biological age based on small-molecule profiles in biological fluids—have become dynamic tools for assessing individual aging trajectories and predicting the risk of age-related diseases. Although NMR-based approaches have been previously reviewed, the analytical landscape of mass spectrometry (MS)-based metabolomic clocks has not been systematically assessed. This review examines the key components of the MS-based analytical workflow underlying the development of metabolomic aging clocks, including biological matrix selection, chromatographic separation strategies, MS instrumentation, data preprocessing, metabolite annotation, and machine learning-based modeling approaches. Twelve published studies on MS-based metabolomic clocks were identified and systematically compared. These clocks provide predictions of chronological age with mean absolute errors of 3.5–10 years and demonstrate robust associations between metabolomic age acceleration and cardiometabolic risk, frailty, and mortality. Recurrent age-related metabolite classes include tryptophan–kynurenine pathway metabolites, acylcarnitines, sphingolipids, modified nucleosides, and tricarboxylic acid (TCA) cycle intermediates. Currently, liquid chromatography with electrospray ionization (LC-ESI) coupled to high-resolution QTOF or Orbitrap instruments dominates current workflows, with elastic net regression being the most commonly applied modeling strategy. Significant heterogeneity in analytical conditions, incomplete methodological descriptions, and limited cross-study validation remain key obstacles to clinical application. Standardization of pre-analytical protocols, the use of certified reference materials, and harmonized validation frameworks are identified as critical priorities for advancing mass spectrometry-based metabolomic clocks toward clinical translation.

METLIN SMRT is a widely-used dataset of retention times for high-performance liquid chromatography (HPLC). Besides direct application it is used for training models aimed at predicting retention times in HPLC. Although there are quite a number of articles featuring METLIN SMRT, the pipelines used for filtering from errors are either simplistic or nonexistent. Therefore, a reliable method for filtering potentially erroneous entries is still required. An approach to filter potentially erroneous entries, suggested in our earlier work for a database of gas chromatography retention indexes, was repurposed for METLIN SMRT using five predictive models (GNN, CNN, FCFP, FCD, and CatBoost). The retention times were predicted for the whole dataset using a 5-fold cross-validation strategy. Entries with retention times differing significantly from the predictions obtained from a given model (bottom 5%) were flagged with a “yellow card”. This procedure was repeated for each model, leading to a group containing about 1500 entries (or 2% of the dataset) with 5 “yellow cards”. According to our estimate (derived from analyzing trends and distributions for groups with varying numbers of “yellow cards”) about 1200 entries were strongly suspected to be erroneous, while 300 were likely predicted inaccurately. This work demonstrates the viability of the approach and its potential to improve the quality of other large-scale chromatography-related databases for both machine learning and experimental use.

High-level programming languages such as Python and R are widely used in mass spectrometry data processing, where library searching is a standard step. Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. MSPepSearch, another NIST tool, provides access to the same algorithms with greater flexibility for automation. However, its use requires calling a command-line interface with multiple flags and parsing output text files to retrieve results, which can be cumbersome. To address this, we developed mspepsearchr, an R package that streamlines the integration of library searches against NIST-format mass spectral databases into complex, multistep workflows. MSPepSearch is a single-threaded tool; therefore, parallelization was achieved externally by running multiple instances from within R. We describe the package, evaluate its performance, and illustrate its utility through the recognition of steroid-like compounds in untargeted gas chromatography-mass spectrometry analysis of biological samples.

Among the numerous applications of 3D printing technology in chemistry, data visualization is one of the most unusual as people are typically accustomed to the idea that digital data exist in a purely abstract form. Gas chromatography—mass spectrometry (GC-MS) data are unsuitable for 3D printing without preliminary data processing, as neighboring extracted ion chromatograms merge into a single structure. In this work, we developed an R script ( https://osf.io/djkyz ) to convert a fragment of raw GC-MS data into an STL file suitable for 3D printing. A series of test prints was conducted to determine the optimal profile for mass spectral peaks, as well as the width and height of chromatographic peaks. A GC-MS fragment containing a group of five closely eluting compounds was 3D-printed using a fused deposition modeling technique and served as a tangible model in mass spectrometry classes. Students reported enhanced clarity and a better understanding of the concept of deconvolution after interacting with the 3D-printed models.

BACKGROUND:The NIST retention index database is one the most widely used sources of retention indices. In both untargeted analysis and machine learning studies filtering for potential errors is rather lacking or nonexistent. According to our estimates about 80% of the compounds from both NIST 17 and NIST 20 retention index databases have only one RI value per stationary phase, which makes searching for erroneous values with statistical methods impossible. Manual inspection is also impractical because the database contains more than 300 000 entries. RESULTS:We suggest a two-step procedure to find potentially erroneous retention indices based on machine learning. The first step is to use five predictive models to obtain predicted retention index values for the whole database. The second one is to compare these predicted values against the experimental ones. We consider a retention index erroneous if its accuracy (the difference between predicted and experimental value) is in the bottom 5% for each of the five models simultaneously. Using this method, we were able to detect 2093 outlier entries for standard and semi-standard non-polar stationary phases in the NIST 17 retention index database, 566 of those were corrected or removed by the developers in the NIST 20. SIGNIFICANCE:This is a novel approach to find potentially erroneous entries in a large-scale database with mostly unique entries, which can be applied not only to retention indices. The procedure can help filter and report mishandled data to improve the quality of the dataset for machine learning applications and experimental use.

Obtaining information about the molecular structure from the mass spectra is one of the most pursued challenges in non-targeted analysis. The complete solution to the problem has not been found yet, therefore only partial information about the structure can be obtained from mass spectra, often in the form of various molecular fingerprints. One of the latest approaches for prediction of molecular fingerprints from electron ionization mass spectra is DeepEI, which suggested a suboptimal procedure based on using a separate neural network for each molecular fingerprint (more than 100 models in our work and 636 using the DeepEI method). More than that, after repeating the procedure described in the original article, we assumed that at least some of their models were most likely overfitted. We streamlined the original approach by predicting multiple types of molecular fingerprints with a single multi-output neural network. We developed a lightweight and performant architecture (called Lite model for brevity) with improved accuracy (0.91 vs 0.89), precision (0.86 vs 0.77), and recall (0.71 vs 0.70) compared to the DeepEI approach. Additionally, the Lite version of the model was more than 100 times faster than the DeepEI approach in training and inference.

Рассматривается вариант Attacker-Defender-Target задачи с одним или двумя защитниками в плоской постановке. Предполагается, что цель и защитники двигаются прямолинейно с постоянной скоростью, атакующий двигается по догонной траектории без ограничения на радиус кривизны. Скорость защитников меньше скорости цели, скорость атакующего – больше. Смысл использования защитников заключается в том, чтобы атакующий вначале занимался их перехватом, и только после этого переключался на преследование основной цели. Тем самым время перехвата основной цели увеличивается, и она может стать недостижима для имеющего ограниченный запас топлива атакующего. Оптимизируются углы и времена выпуска защитников, в том числе рассматривается вариант выпуска защитников с одной стороны от цели. Исследование осуществляется для различных моделей работы системы самонаведения автономного атакующего аппарата: движения к центру масс всех преследуемых объектов, к ближайшей цели по расстоянию или по угловой дальности. Проведено численное моделирование, показывающее важность выбора угла выпуска защитников, целесообразность использования второго защитника, получены сценарии, в которых использование защитников делает основную цель недостижимой для атакующего. This paper considers the Attacker–Defender–Target (ADT) problem with one or two defenders in a 2D statement. By assumption, the target and defenders move in a straight line with a constant velocity whereas the attacker moves along a catch-up trajectory with an unbounded radius of curvature. Compared to the target’s velocity, the defenders move slower whereas the attacker faster. The essence of using defenders is that the attacker first intercepts them and only then switches to pursuing the primary target. As a result, the time of intercepting the primary target increases, and the target may become unattainable for the attacker due to a limited fuel capacity. The angles and times of launching the defenders are optimized, including the case where both defenders are launched on the same side of the target. Different models of the homing system of an autonomous attacking vehicle are studied: moving to the center of mass of all pursued objects and moving to the nearest target by distance or by angular range. Numerical simulations are carried out, showing the importance of choosing the angle of launch of the defenders and the reasonability of using the second defender. Also, scenarios are obtained in which using defenders makes the primary target unattainable for the attacker.

The Identity algorithm implemented in the MS Search (NIST) software is widely used for library searches of gas chromatography/mass spectrometry data against electron ionization mass spectral databases. It has been available to researchers since 1993, with the release of MS Search 1.5a. Despite its extensive use, the operational details of the algorithm have remained ambiguous. Attempts to replicate the algorithm have been unsuccessful because, as found in this research, the description in the manual is neither fully complete nor accurate. The main novelty of this work is the development of a unique approach for deconstructing the Identity algorithm. It is purely based on analyzing library search results obtained from several groups of synthetic mass spectra, each tailored to isolate and examine specific components of the algorithm. This approach facilitated a comprehensive understanding of the Identity algorithm and led to the development of a custom implementation that fully replicates the results obtained from the original MS Search software. The custom implementation of the Identity algorithm is now available in the mssearchr R package, enhancing accessibility for researchers.

This paper considers the Attacker–Defender–Target (ADT) problem with one or two defenders in a 2D statement. By assumption, the target and defenders move in a straight line with a constant velocity whereas the attacker moves along a catch-up trajectory with an unbounded radius of curvature. Compared to the target’s velocity, the defenders move slower whereas the attacker faster. The essence of using defenders is that the attacker first intercepts them and only then switches to pursuing the primary target. As a result, the time of intercepting the primary target increases, and the target may become unattainable for the attacker due to a limited fuel capacity. The angles and times of launching the defenders are optimized, including the case where both defenders are launched on the same side of the target. Different models of the homing system of an autonomous attacking vehicle are studied: moving to the center of mass of all pursued objects and moving to the nearest target by distance or by angular range. Numerical simulations are carried out, showing the importance of choosing the angle of launch of the defenders and the reasonability of using the second defender. Also, scenarios are obtained in which using defenders makes the primary target unattainable for the attacker.

The determination of the number of components in complex mixtures is a difficult task, especially when the compositions of the mixtures are unknown. Even gas chromatography/mass spectrometry with electron ionization does not always solve this problem. Such test samples may include high-purity organic substances. The possibility of the detection of unknown impurities in such samples with information on their molecular masses increases significantly when chromadistillation is used in combination with atmospheric-pressure photochemical ionization mass spectrometry. In this paper, possibilities of using this method for detecting the number of unknown impurities in a high-purity substance (isopropanol) are considered. Twice as many impurities as specified in the GOST (State Standard) 9805-84 are found.

RationaleDatabases of electron ionization mass spectra are often used in GC/MS-based untargeted metabolomics analysis. The results of the library search depend on several factors, such as the size and quality of the database, and the library search algorithm. We found out that the list of considered m/z values is another important parameter. Unfortunately, this information is not usually specified by software developers and it is hidden from the end user. MethodsWe created synthetic data sets and figured out how several popular software products (AMDIS, ChromaTOF, MS Search, and Xcalibur) select the list of m/z values for the library search. Moreover, we considered data sets of real mass spectra (presented in both the NIST and FiehnLib libraries) and compared the library search results obtained within different software products. All programs under consideration use the NIST MS Search binaries to perform the library search using the Identity algorithm. ResultsWe found that AMDIS and ChromaTOF can give biased library search results under particular conditions. In untargeted metabolomics, this can happen when NIST and FiehnLib libraries are used simultaneously, the scan range of the instrument is less than 85, and the correct answer is present only in the FiehnLib library. ConclusionsThe main reason for biased results is that the information about the scan range is not stored in the metadata of library records. As a result, in the case of AMDIS and ChromaTOF software, some unrecorded peaks are considered as missing during the library search, the respective compound is penalized, and the correct answer falls outside the top five or even top 10 hits. At the same time, the default algorithm for selecting the list of considered m/z values implemented in MS Search is free from such unexpected behavior.

The limit of detection of gas chromatography–electron ionization mass spectrometry (in scanning in the full ion current mode) is 10 –11 to 10 –10 g, depending on the substance. A full electron ionization mass spectrum of a substance can be recorded for 10 –10 to 10 –9 g, while the peak of the molecular ion in these mass spectra is often either absent or of low intensity. The minimum amount of a substance that can be detected by gas chromatography–atmospheric pressure photoionization mass spectrometry is 10 –12 to 10 –10 g and by gas chromatography–atmospheric pressure photochemical ionization mass spectrometry is 10 –14 to 10 –12 g, depending on the analyte and the reagent substance. For the amount of substance in the range from 10 –9 to 10 –10 g and up to the limit of detection, a mass spectrum consisting of the peaks of the M + ^∙ or MH + ion or both of them is recorded. It gives selective information about the molecular weight of the analyte throughout the studied range. Identifying trace amounts of unknown components in complex mixtures can be achieved by sharing data from the mass spectra of electron ionization and atmospheric pressure photoionization.

The determination of impurities in high-purity substances is an urgent task. A combination of chromadistillation with atmospheric pressure photoionization mass spectrometry can help in its solution. A combination of these methods ensures recording mass spectra of impurities contained in high-purity organic substances without separating them from the main component. At that, ions of the main component, formed as a result of atmospheric-pressure photoionization, act as a reagent substance for the desired impurities in the atmospheric pressure photochemical ionization mode. It is possible to choose conditions under which the number of peaks recorded in the mass spectrum of a mixture corresponds to the number of mixture components, and each peak corresponds to M + or MH + ions of the mixture component. The information obtained is the number of impurities in the test substance (in this work, in methyl methacrylate) and their integer molecular weights.

It was revealed that nominal mass spectra extracted from the same NetCDF file using different gas chromatography/mass spectrometry (GC/MS) software products are not identical. This phenomenon is caused by differences in algorithms used for rounding floating-point m/z values to integers. It was found that all programs under consideration (AMDIS, ChemStation, ChromaTOF, MS Search, OpenChrom) use different procedures. It is necessary to know how fractional parts of accurate m/z values of ions are distributed to determine which algorithm yields more robust results. We estimated the respective distribution using two databases (PubChem and NIST). As a result, we came up with a procedure that minimizes the influence of random errors on rounding to integer m/z values. The procedure we suggest is to sum intensities of all floating-point m/z values in a bin [MZ - 0.38; MZ + 0.62] and assign MZ as a nominal m/z value, where MZ is an integer m/z value.

Exhaled breath condensate (EBC) as a test material in metabolomic studies continues to receive the attention of analysts. The exhaled air condensate is a sample whose composition reflects the characteristics of biochemical processes in the body; this is a relatively pure matrix, as compared to blood, which contains more polar components (including nonvolatile ones) unlike exhaled air. In addition, EBC sampling is noninvasive. The silylation products of lyophilized EBC samples from healthy volunteers and patients with bronchial asthma (BA) and chronic obstructive pulmonary disease (COPD) were studied using gas chromatography–mass spectrometry with electron ionization. The total ion current chromatograms corresponding to the samples of BA and COPD patients exhibited silyl derivatives of mono-D-fructose and disaccharides. It was hypothesized that D-fructose can be a marker of biochemical processes occurring in the patients diagnosed with BA and COPD.

A number of aromatic metabolites of tyrosine and phenylalanine have been investigated as new perspective markers of infectious complications in the critically ill patients of intensive care units (ICUs). The goal of our research was to build a multivariate model for predicting the outcome of critically ill patients regardless of the main pathology on the day of admission to the ICU. Eight aromatic metabolites were detected in serum using gas chromatography-mass spectrometry. The samples were obtained from the critically ill patients (n = 79), including survivors (n = 44) and non-survivors (n = 35), and healthy volunteers (n = 52). The concentrations of aromatic metabolites were statistically different in the critically ill patients and healthy volunteers. A univariate model for predicting the outcome of the critically ill patients was based on 3-(4-hydroxyphenyl)lactic acid (p-HPhLA). Two multivariate classification models were built based on aromatic metabolites using SIMCA method. The predictive models were compared with the clinical APACHE II scale using ROC analysis. For all of the predictive models the areas under the ROC curve were close to one. The aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcome of critically ill patients on the day of admission to the ICU.