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Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats.InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification further down the workflow. Commonly used methods for deconvolution of fragment signals rely on the presence of a time domain (e.g., peak apex retention time difference and correlation analysis). However, when there is no or insufficient MS2 information in the time domain, these methods are unusable and only the mass domain remains. A probability based cumulative neutral loss (CNL) model for fragment deconvolution using the mass domain information was thus developed to allow deconvolution for such cases. The optimized model, with a mass tolerance of 0.005 Da and a CNL score threshold of -0.95, was able to achieve true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 25.6%, and a reduction rate of 39.9%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 95% with FD rates between 45% and 77% and reduction rates between 10% and 24%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 93.1%, a FDr of 57.2%, and a reduction rate of 42.6%.

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

The great potential gains in separation power and analysis time that can result from rigorously optimizing LC-MS and 2D-LC-MS methods for routine measurements has prompted many scientists to develop computer-aided method-development tools. The applicability of these has been proven in numerous applications, but their proliferation is still limited. Arguably, the majority of LC methods are still developed in a conventional manner, i.e. by analysts who rely on their knowledge and experience. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC separations. A closed-loop workflow was constructed that interacted directly with the LC and ran unsupervised in an automated fashion. The algorithm was tested using two newly designed strategies. The first utilized retention modeling, whereas the second used the Bayesian-optimization machine-learning approach. In both cases, the algorithm could arrive within ten iterations at an optimum of the objective function, which included resolution and measurement time. The design of the algorithm was modular, so as to facilitate compatibility with previous works in literature and its performance thus hinged on each module (e.g., signal processing, choice of retention model, objective function). Key focus areas for further improvement were identified. Bayesian optimization did not require any peak tracking or retention modeling. Accurate prediction of elution profiles was found to be indispensable for the strategy using retention modeling. This is the first interpretive algorithm demonstrated with complex samples. Peak tracking was conducted using UV-Vis absorbance detection, but use of MS detection is expected to significantly broaden the applicability of the workflow.

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses the elemental mass defects of six elemental ratios and successfully identified isotopologues for both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 ​Da mass difference method - CAMERA). For the theoretical isotopologues, the classification model outperformed an “in-house” mass difference method with a true positive rate (TPr) of 99.0% and false positive rate (FPr) of 1.8% compared to a TPr of 16.2% and an FPr of 0.02%, assuming no error. As for the wastewater influent samples, the classification model, with a TPr of 99.8% and false detection rate (FDr) of 0.5%, again performed better than the mass difference method, with a TPr of 96.3% and FDr of 4.8%. Therefore, it can be concluded that the classification model can be used for isotopologue identification, requiring no thresholds or information on the molecular formula.

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses elemental mass defects of six elemental ratios and can successfully identify isotopologues in both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 Da mass difference method - CAMERA).

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments., Measurement(s)chemical • drinking waterTechnology Type(s)high resolution mass spectrometry • non-target analysis • InterlaboratoryFactor Type(s)methodSample Characteristic - Environmentlaboratory environment Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665