Development of a Retention Time Interpolation scale (RTi) for liquid chromatography coupled to mass spectrometry in both positive and negative ionization modes

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Are preserved coastal water bodies in Spanish Mediterranean basin impacted by human activity? Water quality evaluation using chemical and biological analyses

The Spanish Mediterranean basin is particularly susceptible to climate change and human activities, making it vulnerable to the influence of anthropogenic contaminants. Therefore, conducting comprehensive and exhaustive water quality assessment in relevant water bodies of this basin is pivotal. In this work, surface water samples from coastal lagoons or estuaries were collected across the Spanish Mediterranean coastline and subjected to target and suspect screening of 1,585 organic micropollutants by liquid chromatography coupled to ion mobility separation and high resolution mass spectrometry. In total, 91 organic micropollutants could be confirmed and 5 were tentatively identified, with pharmaceuticals and pesticides being the most prevalent groups of chemicals. Chemical analysis data was compared with data on bioanalysis of those samples (recurrent aryl hydrocarbon receptor (AhR) activation, and estrogenic receptor (ER) inhibition in wetland samples affected by wastewater streams). The number of identified organic contaminants containing aromatic rings could explain the AhR activation observed. For the ER antagonistic effects, predictions on estrogenic inhibition potency for the detected compounds were used to explain the activities observed. The integration of chemical analysis with bioanalytical observations allowed a comprehensive overview of the quality of the water bodies under study.

Applying an instrument-agnostizing methodology for the standardization of pesticide quantitation using different liquid chromatography-mass spectrometry platforms: A case study

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful technique commonly used for pesticide residue analysis in agri-food matrices. Despite the fact it has several advantages, one of the main problems is the transferability of the data from one analytical equipment to another for identification and quantitation purposes. In this study, instrument-agnostizing methodology was used to set standard retention scores (SRSs), which was utilized as a parameter for the identification of 74 targeted compounds when different instruments are used. The SRS variation was lower than 5% for most of the compounds included in this study, which is much lower than those obtained when retention times were compared, correcting the elution shift between LC instruments. Additionally, this methodology was also tested for quantitation purposes, and normalized areas were used as analytical responses, allowing for the determination of the concentrations of the targeted compounds in samples injected in one equipment using the analytical responses of standards from another one. The applicability of this approach was tested at two concentrations, 0.06 and 0.15 mg/kg, and less than 10 out of 74 compounds were quantified with an error higher than 40% at 0.06 mg/kg and 0.15 mg/kg, showing that this methodology could be useful to minimize differences between LC-MS systems.

The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment

Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Suspect and non-target screening: the last frontier in environmental analysis

Suspect and non-target screening (SNTS) techniques are arising as new analytical strategies useful to disentangle the environmental occurrence of the thousands of exogenous chemicals present in our ecosystems. The unbiased discovery of the wide number of substances present over environmental analysis needs to find a consensus with powerful technical and computational requirements, as well as with the time-consuming unequivocal identification of discovered analytes. Within these boundaries, the potential applications of SNTS include the studies of environmental pollution in aquatic, atmospheric, solid and biological samples, the assessment of new compounds, transformation products and metabolites, contaminant prioritization, bioremediation or soil/water treatment evaluation, and retrospective data analysis, among many others. In this review, we evaluate the state of the art of SNTS techniques going over the normalized workflow from sampling and sample treatment to instrumental analysis, data processing and a brief review of the more recent applications of SNTS in environmental occurrence and exposure to xenobiotics. The main issues related to harmonization and knowledge gaps are critically evaluated and the challenges of their implementation are assessed in order to ensure a proper use of these promising techniques in the near future.

Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation

Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.

Suspect and non-targeted screening of chemicals of emerging concern for human biomonitoring, environmental health studies and support to risk assessment: From promises to challenges and harmonisation issues

Large-scale suspect and non-targeted screening approaches based on high-resolution mass spectrometry (HRMS) are today available for chemical profiling and holistic characterisation of biological samples. These advanced techniques allow the simultaneous detection of a large number of chemical features, including markers of human chemical exposure. Such markers are of interest for biomonitoring, environmental health studies and support to risk assessment. Furthermore, these screening approaches have the promising capability to detect chemicals of emerging concern (CECs), document the extent of human chemical exposure, generate new research hypotheses and provide early warning support to policy. Whilst of growing importance in the environment and food safety areas, respectively, CECs remain poorly addressed in the field of human biomonitoring. This shortfall is due to several scientific and methodological reasons, including a global lack of harmonisation. In this context, the main aim of this paper is to present an overview of the basic principles, promises and challenges of suspect and non-targeted screening approaches applied to human samples as this specific field introduce major specificities compared to other fields. Focused on liquid chromatography coupled to HRMS-based data acquisition methods, this overview addresses all steps of these new analytical workflows. Beyond this general picture, the main activities carried out on this topic within the particular framework of the European Human Biomonitoring initiative (project HBM4EU, 2017-2021) are described, with an emphasis on harmonisation measures.

Zirconium metal-organic framework assisted miniaturized solid phase extraction of phenylurea herbicides in natural products by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

The zirconium metal-organic framework (Zr-MOF) was used as a novel and effective adsorbent material for the enrichment of five phenylurea herbicides (fenuron, monuron, diuron, linuron and pencycuron) in natural products. The target analytes were determined by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Some crucial experimental parameters, such as type of adsorbents, amount of adsorbent, type of eluent solvents and adsorption capacity were investigated and optimized. Under the optimum extraction conditions, the enrichment factors of fenuron, monuron, diuron, linuron and pencycuron were 90, 128, 148, 204 and 295 times, respectively. A good linearity was obtained in different concentration levels of target analytes with the determination coefficients (r²) larger than 0.993. In addition, the limits of detection varied from 0.05 to 0.36 ng/mL and the recoveries of the analytes were in the range of 85.19-99.13 %. The results demonstrated that the proposed miniaturized solid-phase extraction procedure coupled with Zr-MOF could become an effective tool to analyze phenylurea herbicides and would have the vast application prospect for the extraction of pesticide residue and more organic pollutants from Hawthorn, Dendrobii Officinalis Caulis and Salviae Miltiorrhizae Radix et Rhizoma samples.

Combining linear retention index and electron ionization mass spectrometry for a reliable identification in nano liquid chromatography

The present research is focused on the object to improve identification capability in liquid chromatography (LC), by creating a system as similar as possible to gas chromatography (GC), where the combination/complementarity of Linear Retention Index (LRI) and Electron Ionization Mass Spectrometry (EI-MS) data makes the identification process easy, automatic and reliable. Conversely, in LC the untargeted characterization of real-world samples is still a challenge, due to the not repeatable and poorly informative nature of typical atmospheric pressure ionization mass spectrometry, normally hyphenated to LC. In the last decades the miniaturization of LC instrumentation together with the considerable progresses in MS vacuum pump capability has made the LC-EI-MS hyphenation more feasible. In the present work, a prototypal nanoLC-EI-MS system was used for the determination of typically LC-amenable compunds, such as coumarins, furocoumarins and polymethoxyflavones in citrus essential oils. All the compounds provided high quality EI-MS spectra, evaluated by the comparison with thousands of spectra present in commercial EI-MS libraries. Spectral similarities major than 80% were achieved. Furthermore, an LRI system, based on the use of an alkyl aryl ketone homologue reference series, was proposed as additional filter to achieve a univocal identification. Then, a novel dual-filter LRI/EI-MS library was built and resulted very helpful in the case of isomeric compounds characterized by identical EI-MS spectrum, but different retention behaviour. The very low inter-day variability attained for each LRI value, together with the satisfactory chromatographic resolution of the developed method, led to a 100% reliability of the identification process based on LRI.