Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(<i>n</i>)‑DEA)

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C­(n = 0–23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(<i>n</i>)‑DEA)

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C­(n = 0–23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography–Atmospheric Pressure Chemical Ionization–Quadrupole Time of Flight–Mass Spectrometry

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure–property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/

Trapped Ion Mobility Incorporated in LC–HRMS Workflows as an Integral Analytical Platform of High Sensitivity: Targeted and Untargeted 4D-Metabolomics in Extra Virgin Olive Oil

Trapped ion mobility spectrometry (TIMS) is a promising technique for the separation of isomers based on their mobility. In the present work, TIMS coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) was applied as a comprehensive analytical platform to address authenticity challenges, focusing on extra virgin olive oil (EVOO). Isomers detected in EVOO’s phenolic fraction, classified into secoiridoids group, were successfully separated. Thanks to parallel accumulation serial fragmentation (PASEF) acquisition mode, high-quality spectra were obtained, facilitating identification. Moreover, a four-dimensional (4D) untargeted metabolomics approach was implemented to evaluate EVOO’s global profile in cases of both variety and geographical origin discrimination. Potential authenticity markers, attributed to isomers, were successfully identified through the proposed workflow that incorporates ion mobility information along with LC–HRMS analytical evidence (i.e., mass accuracy, retention time, isotopic pattern, MS/MS fragmentation). Our study establishes LC–TIMS–HRMS in food authenticity and highlights mobility-enhanced metabolomics in four dimensions

Quantitative Structure–Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples

Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing <i>k</i>-nearest neighborhood clustering, to build and validate the models’ internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms. Multiple linear regression, artificial neural networks, and support vector machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh–Tropsha acceptable model criteria, Euclidean based applicability domain, modified correlation coefficient (<i>r</i>_m²), and concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and nonlinear models for each data set were derived and used to predict the retention time of suspect compounds of a wide-scope survey, as the evaluation data set. For the efficient outlier detection and interpretation of the origin of the prediction error, a novel procedure and tool was developed and applied, enabling us to identify if the suspect compound was in the applicability domain or not

High-Resolution Mass Spectrometric Profiling of Stormwater in an Australian Creek

Urban stormwater runoff is a major source of pollutants into receiving water bodies. The pollutant profile of stormwater samples collected from an Australian creek during a major storm event in 2020 was investigated using high-resolution mass spectrometry and chemometric tools. The samples were solid phase-extracted and analyzed by liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (LC-QToF-MS/MS). The detected features were prioritized using two independent but complementary workflows to identify the highly abundant stormwater-related compounds. A total of 174 features were detected at elevated levels during the storm. Four compounds were identified to a confidence level of 1 and 11 at level 2, including nonpolymeric surfactants, plastic additives, rubber and resin-related products, and natural products. Forty two percent were characterized as oligomers such as poly­(ethylene glycol) (PEG)-related compounds and octylphenol ethoxylates. Due to a lack of database experimental data, many compounds remained unidentified. Compounds belonging to the same class were clustered using Global Natural Product Social (GNPS) Molecular Networking analysis, highlighting the benefit of this platform in environmental analysis. The prioritization workflow used here is characterized as an effective tool for assessing key stormwater-related compounds and identifying which should receive attention in assessing the environmental effects of stormwater-related chemicals

Well-Defined Homopolypeptides, Copolypeptides, and Hybrids of Poly(l-proline)

l-Proline is the only, out of 20 essential, amino acid that contains a cyclized substituted α-amino group (is formally an imino acid), which restricts its conformational shape. The synthesis of well-defined homo- and copolymers of l-proline has been plagued either by the low purity of the monomer or the inability of most initiating species to polymerize the corresponding N-carboxy anhydride (NCA) because they require a hydrogen on the 3-N position of the five-member ring of the NCA, which is missing. Herein, highly pure l-proline NCA was synthesized by using the Boc-protected, rather than the free amino acid. The protection of the amine group as well as the efficient purification method utilized resulted in the synthesis of highly pure l-proline NCA. The high purity of the monomer and the use of an amino initiator, which does not require the presence of the 3-N hydrogen, led for the first time to well-defined poly(l-proline) (PLP) homopolymers, poly(ethylene oxide)-b-poly(l-proline), and poly(l-proline)-b-poly(ethylene oxide)-b-poly(l-proline) hybrids, along with poly(γ-benzyl-l-glutamate)-b-poly(l-proline) and poly(Boc-l-lysine)-b-poly(l-proline) copolypeptides. The combined characterization (NMR, FTIR, and MS) that results for the l-proline NCA revealed its high purity. In addition, all synthesized polymers exhibit high molecular and compositional homogeneity

Qualitative Multiresidue Screening Method for 143 Veterinary Drugs and Pharmaceuticals in Milk and Fish Tissue Using Liquid Chromatography Quadrupole-Time-of-Flight Mass Spectrometry

A wide-scope screening methodology has been developed for the identification of veterinary drugs and pharmaceuticals in fish tissue and milk using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). The method was validated using a qualitative approach at two concentration levels. The detection of the residues was accomplished by retention time, accurate mass, and the isotopic fit using an in-house database. Product-ion spectra were used for unequivocal identification of the compounds. Generic sample treatment was applied. The majority of the compounds were successfully detected and identified at concentration levels of 150 ng mL^–1 in milk and 200 μg kg^–1 in fish (>80% of the compounds in both matrices), whereas satisfactory results were also obtained at concentration levels of 15 ng mL^–1 in milk and 20 μg kg^–1 in fish (>60% of the compounds detected and identified)

Assessment of the Acute Toxicity, Uptake and Biotransformation Potential of Benzotriazoles in Zebrafish (<i>Danio rerio</i>) Larvae Combining HILIC- with RPLC-HRMS for High-Throughput Identification

The current study reports on the toxicity, uptake, and biotransformation potential of zebrafish (embryos and larvae) exposed to benzotriazoles (BTs). Acute toxicity assays were conducted. Cardiac function abnormalities (pericardial edema and poor blood circulation) were observed from the phenotypic analysis of early life zebrafish embryos after BTs exposure. For the uptake and biotransformation experiment, extracts of whole body larvae were analyzed using liquid chromatography–high-resolution tandem mass spectrometry (UPLC-Q-TOF-HRMS/MS). The utility of hydrophilic interaction liquid chromatography (HILIC) as complementary technique to reversed phase liquid chromatography (RPLC) in the identification process was investigated. Through HILIC analyses, additional biotransformation products (bio-TPs) were detected, because of the enhanced sensitivity and better separation efficiency of isomers. Therefore, reduction of false negative results was accomplished. Both oxidative (hydroxylation) and conjugative (glucuronidation, sulfation) metabolic reactions were observed, while direct sulfation proved the dominant biotransformation pathway. Overall, 26 bio-TPs were identified through suspect and nontarget screening workflows, 22 of them reported for the first time. 4-Methyl-1-<i>H</i>-benzotriazole (4-MeBT) demonstrated the highest toxicity potential and was more extensively biotransformed, compared to 1-<i>H</i>-benzotriazole (BT) and 5-methyl-1-<i>H</i>-benzotriazole (5-MeBT). The extent of biotransformation proved particularly informative in the current study, to explain and better understand the different toxicity potentials of BTs

Mass Loading and Fate of Linear and Cyclic Siloxanes in a Wastewater Treatment Plant in Greece

The occurrence and fate of 5 cyclic (D3 to D7) and 12 linear (L3 to L14) siloxanes were investigated in raw and treated wastewater (both particulate and dissolved phases) as well as in sludge from a wastewater treatment plant (WWTP) in Athens, Greece. Cyclic and linear siloxanes (except for L3) were detected in all influent wastewater and sludge samples at mean concentrations of (sum of 17 siloxanes) 20 μg L^–1 and 75 mg kg^–1, respectively. The predominant compounds in wastewater were L11 (24% of the total siloxane concentration), L10 (16%), and D5 (13%), and in sludge were D5 (20%) and L10 (15%). The distribution of siloxanes between particulate and dissolved phases in influents differed significantly for linear and cyclic siloxanes. Linear siloxanes showed higher solid–liquid distribution coefficients (log <i>K</i>_d) than did cyclic compounds. For 10 of the 16 compounds detected in influents, the removal efficiency was higher than 80%. Sorption to sludge and biodegradation and/or volatilization losses are important factors that affect the fate of siloxanes in WWTPs. The mean total mass of siloxanes that enter into the WWTP via influent was 15.1 kg per day^–1, and the mean total mass released into the environment via effluent was 2.67 kg per day^–1