Chromatographic Retention Times Research Articles

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Chromatographic Assessment of Organic Compounds Using Carbon Nanotubes: The Relationship between Affinity and Dispersibility.

The affinity between carbon nanotubes (CNTs) and organic compounds is of substantial importance since it strongly relates to the dispersibility of CNTs in those compounds. Several affinity evaluation methods have been developed so far, and the concept of the Hansen solubility parameter is a representative method widely used in the field of nanocarbon materials. Here, we demonstrate that CNT-loaded silica columns can effectively assess the affinity of organic compounds for CNT surface by exploiting the chromatographic retention time as a criterion. Obtained trends of the affinity of organic compounds for CNT were compared to those based on Hansen solubility parameter distance values. Most organic compounds showed similar trends, but one exceptional compound was observed. Simple CNT dispersion tests were conducted with these organic compounds to demonstrate the advantage of the chromatographic assessment. Further, we conducted comparison experiments using a pyrene-functionalized column and other CNT-loaded columns to elucidate the characteristics of each CNT column. The chromatographic approaches using CNT columns would be beneficial for realizing CNT suspensions with improved CNT dispersibility.

Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

This study explores how machine-learning can be used to predict chromatographic retention times (RT) for the analysis of small molecules, with the objective of identifying a machine-learning framework with the robustness required to support a chemical synthesis production platform. We used internally generated data from high-throughput parallel synthesis in context of pharmaceutical drug discovery projects. We tested machine-learning models from the following frameworks: XGBoost, ChemProp, and DeepChem, using a dataset of 7552 small molecules. Our findings show that two specific models, AttentiveFP and ChemProp, performed better than XGBoost and a regular neural network in predicting RT accurately. We also assessed how well these models performed over time and found that molecular graph neural networks consistently gave accurate predictions for new chemical series. In addition, when we applied ChemProp on the publicly available METLIN SMRT dataset, it performed impressively with an average error of 38.70 s. These results highlight the efficacy of molecular graph neural networks, especially ChemProp, in diverse RT prediction scenarios, thereby enhancing the efficiency of chromatographic analysis.

Detection of emerging disinfection by-products by reversed-phase ion-pair chromatography electrospray-ionization mass spectrometry in drinking water

A sensitive and specific methodology that allows unequivocal identification of polydiallyldimethylammonium chloride (PDADMAC) disinfection by-product it monomer diallyldimethylammonium chloride (DADMAC) in drinking water samples based on reversed-phase ion-pair chromatography–electrospray ionization – mass spectrometry was optimized. Ion-pair reagent heptafluorobutyric (HFBA) was found to play a crucial role in the optimization of chromatographic parameters. The chromatographic retention time was optimized by varying concentrations of ion-pair reagent, flow rate, and column temperature to enable good chromatographic retention and sensitivity of disinfection by-product of interest DADMAC in positive electrospray ionization and using selected ion monitoring (SIM) mode. The method was validated and showed good linear regression of 0.99 and a detection limit of 0.008 mg L−1. The developed method was applied in a drinking water treatment plant operated by Mgeni Water in Durban, South Africa, to detect trace levels of the disinfection by-product of interest (DADMAC) at various points of the treatment process. The concentration of DADMAC in the water treatment plant ranged from not detected up to 0.65 mg L−1. In addition, this work through mass spectrometry provides the first evidence of ion-pairing (465 m/z peak) between DADMAC and the reagent, which is a confirmation of online derivatization. Also, the use of ion-pair prevented dimerization of DADMAC (286 m/z peak) inside the ionization chamber leading to high sensitivity.

Isomeric Separation of α2,3/α2,6-Linked 2-Aminobenzamide (2AB)-Labeled Sialoglycopeptides by C18-LC-MS/MS.

Determination of the relative expression levels of the α2,3/α2,6-sialic acid linkage isomers on glycoproteins is critical to the analysis of various human diseases such as cancer, inflammation, and viral infection. However, it remains a challenge to separate and differentiate site-specific linkage isomers at the glycopeptide level. Some derivatization methods on the carboxyl group of sialic acid have been developed to generate mass differences between linkage isomers. In this study, we utilized chemical derivatization that occurred on the vicinal diol of sialic acid to separate linkage isomers on a reverse-phase column using a relatively short time. 2-Aminobenzamide (2AB) labeling derivatization, including periodate oxidation and reductive amination, took only ∼3 h and achieved high labeling efficiency (>90%). Within a 66 min gradient, the sialic acid linkage isomers of 2AB-labeled glycopeptides from model glycoproteins can be efficiently resolved compared to native glycopeptides. Two different methods, neuraminidase digestion and higher-energy collision dissociation tandem mass spectrometry (HCD-MS2) fragmentation, were utilized to differentiate those isomeric peaks. By calculating the diagnostic oxonium ion ratio of Gal2ABNeuAc and 2ABNeuAc fragments, significant differences in chromatographic retention times and in mass spectral peak abundances were observed between linkage isomers. Their corresponding MS2 PCA plots also helped to elucidate the linkage information. This method was successfully applied to human blood serum. A total of 514 2AB-labeled glycopeptide structures, including 152 sets of isomers, were identified, proving the applicability of this method in linkage-specific structural characterization and relative quantification of sialic acid isomers.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.Graphical

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics.

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

Retention Time Trajectory Matching for Peak Identification in Chromatographic Analysis.

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak matching and identification across different chromatograms is critical prior to any subsequent analysis but is challenging without using mass spectrometry. The purpose of this work was to describe and validate a peak matching and identification method called retention time trajectory (RTT) matching that can be used in targeted analyses free of mass spectrometry. This method uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. An RTT is a two-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a sample under test and those pre-installed in a library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak-alignment methods, no mathematical warping or transformation is involved. Based on the experimentally characterized RTT, an RTT hybridization method was also developed to rapidly generate more RTTs and expand the library without performing actual time-consuming chromatographic measurements, which enables successful peak matching even for chromatograms with severe retention time drifts. Additionally, 3.15 × 105 tests using experimentally obtained gas chromatograms and 2 × 1012 tests using two publicly available fruit metabolomics datasets validated the proposed method, demonstrating real-time peak/interferent identification.

Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer.

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).

Per- and Polyfluoroalkyl Substances Enrichment in the Surface Microlayer of a Freshwater System Impacted by Aqueous Film-Forming Foams

Accumulation of per- and polyfluoroalkyl substances (PFAS) in the surface microlayer (SML) of open water bodies has received little attention. Various SML sampling methods exist, yet none have been systematically evaluated for PFAS. Three glass-based techniques were deployed for SML sampling at 10 sites on a freshwater system impacted by aqueous film-forming foam (AFFF). Samples of SML and underlying bulk water (3 and 30 cm) were analyzed for 50 target PFAS by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF). Single dipped microscope slides yielded field measurements of SML thicknesses and enrichment factors for eight PFAS and used to compute field-measured air–water adsorption coefficients. Large glass plates collected a greater volume of SML, gave lower variability, and lower limits of detection and quantification. Up to 19 PFAS (concentrations up to 13,000 ng/L) were associated with the SML, while only 15 PFAS were in underlying bulk water. Enrichment factors (EFs) ranged from 1 to 20 and increased with chromatographic retention time, a proxy for relative hydrophobicity. Field-measured air–water adsorption coefficients were consistent with monolayer adsorption as described by the Gibbs adsorption equation, which indicates that only adsorption to the air–water interface is needed to explain the observed PFAS enrichment in the SML.

Profiling of cool-season forage arabinoxylans via a validated HPAEC-PAD method.

Cool-season pasture grasses contain arabinoxylans (AX) as their major cell wall hemicellulosic polysaccharide. AX structural differences may influence enzymatic degradability, but this relationship has not been fully explored in the AX from the vegetative tissues of cool-season forages, primarily because only limited AX structural characterization has been performed in pasture grasses. Structural profiling of forage AX is a necessary foundation for future work assessing enzymatic degradability and may also be useful for assessing forage quality and suitability for ruminant feed. The main objective of this study was to optimize and validate a high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) method for the simultaneous quantification of 10 endoxylanase-released xylooligosaccharides (XOS) and arabinoxylan oligosaccharides (AXOS) in cool-season forage cell wall material. The following analytical parameters were determined or optimized: chromatographic separation and retention time (RT), internal standard suitability, working concentration range (CR), limit of detection (LOD), limit of quantification (LOQ), relative response factor (RRF), and quadratic calibration curves. The developed method was used to profile the AX structure of four cool-season grasses commonly grown in pastures (timothy, Phleum pratense L.; perennial ryegrass, Lolium perenne L.; tall fescue, Schedonorus arundinaceus (Schreb.) Dumort.; and Kentucky bluegrass, Poa pratensis L.). In addition, the cell wall monosaccharide and ester-linked hydroxycinnamic acid contents were determined for each grass. The developed method revealed unique structural aspects of the AX structure of these forage grass samples that complemented the results of the cell wall monosaccharide analysis. For example, xylotriose, representing an unsubstituted portion of the AX polysaccharide backbone, was the most abundantly-released oligosaccharide in all the species. Perennial rye samples tended to have greater amounts of released oligosaccharides compared to the other species. This method is ideally suited to monitor structural changes of AX in forages as a result of plant breeding, pasture management, and fermentation of plant material.

In-syringe dispersive solid phase filter extraction cleanup followed by liquid chromatography-triple quadrupole mass spectrometry for fast determination of colchicine in plasma/urine

As an effective treatment for acute gouty arthritis and cardiovascular disease, colchicine is also a toxic alkaloid and may cause poisoning or even death in overdose. The study of colchicine elimination and the diagnosis of poisoning etiology need the rapid and accurate quantitative analysis method in biological matrix. An analytical method was developed for colchicine in plasma and urine by in-syringe dispersive solid phase extraction (DSPE) followed by liquid chromatography-triple quadrupole mass spectrometry (LC-MS/MS). Sample extraction and protein precipitation were proceeded with acetonitrile. The extract was cleaned by in-syringe DSPE. An XBridge™ BEH C18 column（100 mm × 2.1 mm, 2.5 µm）was used to separate colchicine by gradient elution with mobile phase of 0.01% (v/v) ammonia-methanol. The amount and filling sequence of magnesium sulfate (MgSO4) and primary secondary amine (PSA) suitable for in-syringe DSPE were studied. Scopolamine was screened as the quantitative internal standard (IS) for colchicine analysis according to the consistency of recovery rate, chromatographic retention time and matrix effects. The limits of detection for colchicine in plasma and urine were both 0.06 ng mL−1 and the limits of quantitation were both 0.2 ng mL−1. The linear range was 0.04 – 20 ng mL−1 (Equivalent to 0.2–100 ng mL−1 in plasma or urine) with a correlation coefficient r > 0.999. By IS calibration, the average recoveries at three spiking levels in plasma and urine were 95.3–102.68% and 93.9–94.8% with the relative standard deviations (RSDs) of 2.9–5.7% and 2.3–3.4%, respectively. The matrix effects, stability, dilution effects and carryover for determination of colchicine in plasma and urine were also evaluated. The elimination of colchicine within 72–384 h post-ingestion was studied for a poisoning patient with the doses of 1 mg d−1 for 39 days and then 3 mg d−1 for 15 days).

Simple In Vitro 18O Labeling for Improved Mass Spectrometry-Based Drug Metabolites Identification: Deep Drug Metabolism Study.

The identification of drug metabolites formed with different in vitro systems by HPLC-MS is a standard step in preclinical research. In vitro systems allow modeling of real metabolic pathways of a drug candidate. Despite the emergence of various software and databases, identification of compounds is still a complex task. Measurement of the accurate mass, correlation of chromatographic retention times and fragmentation spectra are often insufficient for identification of compounds especially in the absence of reference materials. Metabolites can "slip under the nose", since it is often not possible to reliably confirm that a signal belongs to a metabolite and not to other compounds in complex systems. Isotope labeling has proved to be a tool that aids in small molecule identification. The introduction of heavy isotopes is done with isotope exchange reactions or with complicated synthetic schemes. Here, we present an approach based on the biocatalytic insertion of oxygen-18 isotope under the action of liver microsomes enzymes in the presence of 18O2. Using the local anesthetic bupivacaine as an example, more than 20 previously unknown metabolites were reliably discovered and annotated in the absence of the reference materials. In combination with high-resolution mass spectrometry and modern methods of mass spectrometric metabolism data processing, we demonstrated the ability of the proposed approach to increase the degree of confidence in interpretating metabolism data.

Integrating Effect-Directed Analysis and Chemically Indicative Mass Spectral Fragmentation to Screen for Toxic Organophosphorus Compounds.

Analytical chemists are often challenged to screen for bioactive compounds in complex matrices, sometimes without a priori knowledge of the exact compound of interest. Therefore, "flagging" techniques, highlighting common characteristics of bioactive compounds, are highly sought after. In this work, we demonstrate a double flagging method, where unknown organophosphorus acetylcholinesterase inhibitors are "flagged" out of a complex matrix by the presence of organophosphorus-indicative ions as well as their acetylcholinesterase inhibition. This is accomplished by flagging the LC chromatographic retention time of phosphorus-indicative ions using accurate mass high-energy in-source CID products, and the retention time of acetylcholinesterase inhibiting compounds using a parallel microfractionation-based bioassay. We successfully apply this method to screen VX, VM, and RVX nerve agents as well as methomyl, a carbamate pesticide, out of soil and whole blood samples at low μM to sub-μM concentrations. This methodology can be easily extended to diverse chemical families and biological activities of interest.

Identification of in vitro metabolites of boldenone using Camel liver microsomes – A HR-LCMS approach

Anabolic steroids are widely abused in animal sports to improve their performance. The present study identifies the possible metabolites of boldenone in camel liver. A high-resolution accurate QE mass spectrometer was used to identify the parent boldenone and its metabolites. To investigate the phase 1 biotransformation of boldenone in camel, sodium phosphate buffer and NADPH were used. Chromatographic separation was carried out on a Thermo Hypersil C18 column using acetonitrile and formic acid as mobile phases. The current study, helped in unequivocal detection of six metabolites (Phase 1) for boldenone. The 17b-boldenone is an 3-oxo-Delta (1), Delta(4)-steroid substituted by an oxo group at position 3 and a beta-hydroxy group at position 17. It is prone to oxidation, which results in three hydroxylated metabolites with protonated parent ion of m/z 303.1954 (C19H27O3)+. Androsta-1,4-diene-3,17-dione [M+H]+ of m/z 285.1848 (C19H25O2+), hydroxyandrosta-1,4-diene-3,17-dione [M+H]+ of m/z 301.1797 (C19H25O3+) in addition to 17-hydroxy-androsta-1-en-3-one [M+H]+ of m/z 289.2162 (C19H29O2+) were also identified. The structures of the detected metabolites were identified based on their accurate mass, fragmentation pattern, and chromatographic retention time. In this research, camel liver was successfully used for in vitro experiments as an alternative to liver microsomes.

Segmented Flow Strategies for Integrating Liquid Chromatography-Mass Spectrometry with Nuclear Magnetic Resonance for Lipidomics.

Building an accurate lipid inventory relies on coordinated information from orthogonal analytical capabilities. Integrating the familiar workflow of liquid chromatography (LC), high-resolution mass spectrometry (HRMS), and tandem mass spectrometry (MS/MS) with proton nuclear magnetic resonance spectroscopy (1H NMR) would be ideal for building that inventory. For absolute lipid structural elucidation, LC-HRMS/MS can provide lower-level structural information with superior sensitivity, while 1H NMR can provide invaluable higher-order structural information for the disambiguation of isomers with absolute chemical specificity. Digitization of the LC eluent followed by splitting the microfractions into two flow paths in a defined ratio for HRMS/MS and NMR would be the ideal strategy to permit correlation of the MS and NMR data as a function of chromatographic retention time. Here, we report an active segmentation platform to transform analytical flow rate LC eluent into parallel microliter segmented flow queues for high confidence correlation of the MS, MS/MS, and NMR data. The practical details in implementing this strategy to achieve an integrated LC-MS-NMR platform are presented, including the development of an active segmentation technology using a four-port two-way valve to transform the LC eluent into parallel segmented flows for online MS analysis followed by offline segment-specific 1H NMR and optimization of the detector response toward segmented flow. To demonstrate the practicality of this novel platform, it was tested using lipid mixture samples.

Forensic chemical and chemicotoxicological examination of ricin poisoning by the HPLC-MS/MS method

Is to suggest the method of ricin determination in biological liquids during forensic medical and chemicotoxicological examination. This research describes the optimal conditions of sample processing of biological liquids, allowing to extract the components (ricinine and ricinoleic acid) of castor seeds. The recommended analysis conditions allow to perform research for 15 minutes by high resolution mass spectrometry method combined with high-value liquid chromatography on a chromato-mass spectrometer to detect ricinine and ricinoleic acid. The chromatographic (retention time) and mass-spectrometric parameters (mass spectra) were established for the exact high-quality determination of ricinine and ricinoleic acid.

Structure Analysis of the Interfering Substance N-methyl-2-phenylpropan-1-amine of Methamphetamine in Wastewater.

To analyze the chemical structure of the interfering substance that affects the result of methamphetamine analysis in wastewater. A combination of GC-MS and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was used to analyze the mass spectrum characteristics of the interfering substance that affects the result of methamphetamine analysis and to infer its possible structure. Liquid chromatography-triple quadrupole-mass spectrometry (LC-TQ-MS) was used to confirm the control material. Using LC-QTOF-MS in positive electrospray ionization (ESI+) mode, the mass-to-charge ratio (m/z) of quasi-molecular ion in the MS1 mass spectrometry of interfering substance was identical to that of methamphetamine, indicating that the interfering substance was probably an isomer of methamphetamine. The MS2 mass spectra obtained at three collision energies of 15 V, 30 V and 45 V were highly similar to methamphetamine, suggesting that the interfering substance contained methylamino and benzyl groups. Further analysis using GC-MS in electron impact (EI) ionization mode showed that the base peak in the mass spectrum of the interfering substance was at m/z 44. The interfering substance was confirmed to be N-methyl-2-phenylpropan-1-amine by compared with the standard reference. The chemical structure of N-methyl-2-phenylpropan-1-amine is highly similar to methamphetamine, which is easy to cause interference for the detection of trace amounts of methamphetamine in wastewater using LC-TQ-MS. Therefore, in the actual analysis, the chromatographic retention time can be used to distinguish between N-methyl-2-phenylpropan-1-amine and methamphetamine.

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.