dataesr/scientific-paragraphs-categorization huggingface dataset:
"Atmospheric Pressure Chemical Ionisation and Photoionisation" for more details). The ionised substances can then be fragmented using DDA or DIA methods (see Sect. "Precursor selection: Data dependent versus data independent acquisition"). By having access to the intact molecular ion information, workflows that have been demonstrated to work well for LC-HRMS, where database matching usually begins with a search for molecules that match the exact mass, can also be employed for GC-APCI-HRMS/ MS (Sects. "Compound identification/confirmation" and "Quantification and semi-quantification of suspects and unknowns"). In addition, accurate mass and isotopologue information improve the molecular formula prediction and thus also structure elucidation. Indeed, wide-scope target screening of several hundred non-polar compounds by GC-APCI-HRMS/MS has already been successfully applied  [184, 185]  and a suspect list was compiled (NORMAN S65), whereas NTS workflows are currently being established.
Unlike drift time, CCS is an instrument independent value, provided that the same drift gas and ion mobility calibration standards are used  [191, 198, 199]. Moreover, CCS is independent of the chromatographic conditions applied and is not affected by the sample matrix. In addition to RT, isotope pattern, precursor and fragment ion m/z, CCS values have shown potential as a further criterion for reliable identification of compounds  [200]. Furthermore, it has shown clear improvements in targeted screening of complex feed samples by reducing the number of false positives during automated analyte detection while keeping a high detection rate  [190]. The creation of empirical CCS databases is, therefore, of interest for an enhanced screening strategy. In general, empirical CCS has been shown to be robust (± 2% tolerance) across multiple platforms  [201] , and the role of external calibration strategies has been critically evaluated  [202].
At this stage, the occasional high deviations between expected and experimental CCS values indicates that these databases must be used with care and that the instrument type should be specified and considered when using them  [194, 198, 203]. Although CCS databases are being created, their utility is limited by the number of experimental CCS values available-one of the largest merged collections on PubChem currently hosts CCS values for multiple adducts of 5699 compounds from a range of open CCS databases  [204]. As a result, the prediction of CCS using machine learning tools has become an area of interest  [205] [206] [207]. In silico prediction of CCS facilitates the screening of many compounds for which no empirical values are available and improves the confidence of tentative identifications  [197, 205, 207].
Furthermore, it helps to refine non-target data processing by reducing the potential positive matches to be further investigated, i. e. , assisting with prioritisation of features of interest  [196]. IMS coupled to HRMS instruments is not yet widely implemented into environmental research laboratories and the number of publications applying IMS to the analysis of complex environmental samples is still limited  [200, 208, 209]. This is possibly related to their relatively recent emergence, the associated costs, and the limited options to handle the data obtained. The 4D data generated (RT, drift time, accurate m/z and peak intensity) is barely supported by open formats such as mzML and thus difficult to integrate into and process with open algorithms/software tools (e. g. , MZmine 3. 5, MS-DIAL and Skyline).
Over time, larger suspect screening lists (e. g. , compiled from REACH substances  [272, 273]  were used. In the meantime, suspect lists have become popular, although of widely varying quality, and a few platforms exist to exchange this information, including the NORMAN Suspect List Exchange (NORMAN-SLE) via https:// www. norman-netwo rk. com/ nds/ SLE/ [269] and the CompTox Chemicals Dashboard [274] via https:// compt ox. epa. gov/ dashb oard/ chemi cal_ lists. PubChem [275] (https:// pubch em. ncbi. nlm. nih. gov/) also offers functionality that can be leveraged to create suspect lists (https:// pubch em. ncbi. nlm. nih. gov/ class ifica tion/# hid= 101). Interest has also increased in creating merged lists. For instance, the NORMAN-SLE was merged on popular request to form the combined NORMAN SusDat database [276], STOFF-IDENT combines several individual data sets  [272] , and the HBM4EU project compiled their own suspect list of chemicals of emerging concern for screening in human biological samples (CECscreen), which also includes simulated phase I metabolites  [277, 278].
Irrespective of whether "screen big" or "screen smart" is performed, further confirmation efforts are necessary, and essentially must be in line with those required for non-target identification efforts (see Fig. 1 ). Screening with suspect lists of tens of thousands of suspects is effectively equivalent to non-target screening that uses a small database for assignment of candidate compounds, as several matches could be possible for each mass  [269]. Many suspect lists are now supported by additional data to enhance the ability to prioritise, select or interpret potential suspect matches. Typical values such as calculated properties are either saved in the lists or can be retrieved in a number of different ways, either from large databases, such as PubChem, or via interfaces such as EPISuite  [279]  or ChemAxon  [280, 281].
Increasingly, calculated toxicity values have also been included in, e. g. , the CompTox Chemicals Dashboard and NORMAN SusDat. Exposure data are also useful and included in suspect lists in a variety of ways, from predicted exposure in the CompTox Chemicals Dashboard  [274] , to REACH tonnage estimates in several  through to exposure indices provided by the Swedish Chemicals Agency, KEMI  [282, 283]. Incorporating additional data into suspect lists comes with a new set of challenges, including how to deal with various salt and mixture forms of the chemicals of interest. Exposure and hazard data may be available for products (e. g. , salt or mixtures), whereas the detected form in the HRMS experiments will be an individual species without counterions or other mixture components. Some mixture components may also be visible with the chosen method (as would be the case for homologous series of surfactants), whereas metallic or small organic counterions will usually not be visible.
PubChem has implemented a socalled "parent" compound, which can be used to find the major component in salts and mixtures, for example, via the PubChem Identifier Exchange service (ID Exchange) or programmatically  [286, 287]. In the PubChemLite approach, all forms related to a given "parent compound" with annotation content of interest were totalled and collapsed into one entry (maintaining the connections to the individual entries) to form a very efficient database for screening  [288]. While the handling of salts has improved recently, mixtures and organometallics remain a challenge for workflows and are areas requiring cheminformatics developments in the coming years  [289, 290]. Several efforts are now investigating using predicted TPs to support suspect screening  [210, 277]. Open pathway prediction software available for this includes enviPath for microbial transformation  [291] , or BioTransformer  [292]  for mammalian (specifically human) and microbial transformation.
A good suspect list requires high-quality information, with a clear provenance, where the data were obtained. At least one, preferably more, structural identifiers should be provided to accompany the name and additional database identifiers. The primary identifier should also be clear, such that inconsistencies can be fixed in accordance with the hierarchy of suspect list preparation. Ideally, a suspect list should contain the chemical name and one or more database identifiers, along with at least two pieces of structural information. Identifiers include the PubChem compound identifier (PubChem CID), the Distributed Structure-Searchable Toxicity (DSSTox) substance identifier (DTXSID) used in CompTox and Chemical Abstract Service (CAS) registry numbers commonly used in regulatory lists. Structural information should include at least two of the Simplified Molecular-Input Line-Entry System (SMILES), the International Chemical Identifier (InChI) or it is hashed form InChIKey (note the InChIKey can be calculated from SMILES or InChI, but not vice versa).
It is difficult to find the right balance between not enough, enough and too much information. Many large databases provide mapping services to obtain extra data in real-time, including the CompTox Batch Search [295] and the PubChem ID Exchange  [286]. Ideally, the suspect list should contain as much specifically relevant information as possible, without overwhelming users with too many databaseobtained synonyms and secondary identifiers. Similarly, many users find exact masses from all possible adduct combinations very useful in suspect lists. While this offers quick and efficient look-up opportunities, the number of possible adducts and the difficulty in predicting which adducts are likely to be detected (see Sect. "Compound identification/confirmation") can lead to an overwhelming presentation of thousands of possible masses in suspect lists, which greatly increases the risk of false positives.
The tools GenForm (formerly MOLGEN-MSMS),  [231, 232] ,  SIRIUS [298]  and ZODIAC [299] also use the MS2 information with increasingly sophisticated algorithms for the formula annotation, with corresponding improvements in performance. While many vendors will offer molecular formula calculations in their software, it is best to treat these with caution unless the settings have been carefully selected; many formulas reported using vendor software in the NORMAN Collaborative Trial did not match the formulas of the candidates proposed by the participants based on their expert knowledge  [10]. The majority of top-ranked candidates from prediction software combined with database searches have to be classified as level 3 (tentative identification), because the information available from the relatively sparse MS2 spectra usually does not allow for an unambiguous structure assignment.
Recently, a simplified and automated identification point (IP) system scaled from 0 to 1 and compatible with the five level system was proposed for the communication of the evidence for the identification level by. The weighting of the parameters mass accuracy (< 5 ppm, mandatory), isotopic fit (at least one isotope, 0. 2), MS2 fragmentation (0. 2-0. 4 dependent on the number of matching experimental or in silico fragments), and predicted retention time index (0. 15) was derived from a machine learning model trained on data generated by four laboratories equipped with different instrumentation. The evidence-based scoring is intended to improve the precision and reproducibility in reporting while being suitable for automation. In the automated system, DIA spectral acquisition is penalised compared to DDA as the spectra are less specific due to the presence of many precursor ions (see Sect.
EI-MS libraries As mentioned above, spectral libraries for GC-EI-MS are well-established due to the reproducible nature of the spectra across laboratories. The main library in use is the NIST library, which comes with free search software (for both EI-MS and MS2 data) but requires a licence for the spectral library itself. The NIST spectral format (. msp) is widely used in vendor and open software for exchanging spectral data and many vendors provide direct integration of the NIST library in their software (often including the licence for the EI-MS and MS2 libraries). NIST is also working on ways to integrate their libraries in automatic open workflows for those who have a licence. The NIST/EPA/NIH EI-MS Library, 2020 release (NIST20 for short) contains 350,704 EI-MS of 306,643 compounds, including 43,774 replicate spectra  [6].
Thermo's mzCloud can be viewed online or accessed via Thermo software (with a licence) and includes 10,545,159 spectra of 20,944 compounds as of  12 March 2023 [313]. In terms of the open libraries (where the data are available for download and integration in open workflows), MassBank Europe  (MassBankEU [314, 315] ), MassBank of North America  (MoNA [316] ) and the Global Natural Products Social Molecular Networking  (GNPS [317, 318] ) are the most relevant, although the Human Metabolome Database  (HMDB [319] ) and related resources such as DrugBank also contain some spectra of interest. All libraries contain a mixture of natural and anthropogenic chemicals, although some allow the creation of subsets for more specific searching. PubChem now integrates many MS2 spectra, including previews and top 3 or 5 peaks for easy browsing and viewing.
In terms of numbers  (12 March 2023)  they contain: MassBankEU 90,471 spectra of  16,881 compounds; MoNA 2,049,395 spectra (1,844,353 in silico, 205,042 experimental) of 650,263 compounds; GNPS: 587,959 spectra (~ 27,000 compounds in January 2022). Many other libraries contain mostly endogenous metabolites and narrow substance classes that are not so suitable for NTS of anthropogenic chemicals. Several of the libraries mentioned above, especially those with licences, are not freely accessible but sometimes included in vendor software (e. g. ,  METLIN, NIST, mzCloud). Some of the databases can only be searched online (single or batch search), whereas others can be downloaded and included in workflows (e. g. , MassBank, MoNA, GNPS). Especially for the open libraries that allow user contributions (MassBank, MoNA and GNPS), the quality of the spectra can vary enormously (low resolution vs.
high resolution, noisy spectra vs. spectra curated manually or by algorithms, such as RMassBank) [234] and this should be considered carefully. All three open databases mentioned also allow the creation of subsets to address this issue (so that advanced users can select the data of interest). Several software programs for MS1 and MS2 searches come with the vendor software of the instruments. Furthermore, NIST MS search (see previous section), MS-DIAL [213], OpenMS [320], mzMine  3 [216]  and other free workflows are available for this purpose. For instance, the MassBank database can be downloaded in a variety of formats and used with NIST MS search, MS-DIAL, patRoon [210], MetFrag [284] and others. Due to the variability of fragmentation behaviour between instruments and the wide range of collision energies used, caution is still needed when matching MS2 spectra and the thresholds should be adjusted accordingly (and resulting matches/reverse matches interpreted with care).
Candidate structure search The chemical space applicable to environmental applications is extremely large, such that the choice of database to search for candidates plays a significant role in likelihood of downstream success of tentative identification while minimising the risk of both false positives (incorrect structure identified) and false negatives (correct structure missing). The largest chemical registry, CAS, now has > 204 million structures  [2]  but is not accessible to open workflows. The updated Global Inventory of > 350,000 substances is a subset of regulated substances, but does not yet have structures associated with it  [1]. The use of suspect lists was covered in Sect. "Candidate structure search and selection"; some large suspect lists or the consolidated NORMAN SusDat collection (> 100,000 compounds) can also be used as compound databases for candidate searching.
Over the last decade, the most popular databases for NTS candidate search have been the two largest open collections, PubChem and ChemSpider, due to their comprehensive, open collections and the combination of website and programmatic access options. As of March 2023, PubChem contains 115 million chemicals [275], while ChemSpider contains 118 million chemicals  [325]. While ChemSpider is still integrated in many vendor workflows, relatively recent changes to their programmatic access conditions means that user quotas are often insufficient to integrate ChemSpider in high throughput NTS workflows, such that PubChem is now the easiest of the two large databases to work with. However, queries to these databases often result in thousands to tens of thousands of matching candidates. The release of the US EPA's CompTox Chemicals Dashboard (hereafter CompTox) in 2016 introduced a new compound database of high relevance for environmental applications, with 1,200,059 chemicals and extensive environmental and toxicological information available  [274].
The PubChemLite for Exposomics collection is a selection of ~ 450,000 chemicals (updated monthly) relevant for environmental, metabolomics and exposomics applications formed from major annotation categories in PubChem [288], including TPs. Candidate searches with CompTox or PubChemLite tend to return tens to hundreds of candidates per exact mass or formula, considerably less than searching the entire PubChem or ChemSpider. The additional information provided with both can help support candidate selection further (see Sect. "Candidate structure search and selection"). Although compound databases are often criticised for lacking information about TPs, there are now concerted efforts to address this via the  288, 326] , with FAIR templates available for community submission of this data  [293].
Any database search is clearly restricted to the chemicals present in the database and can thus not identify any true unknowns that have not yet been documented. Although it is technically possible to generate structures of interest via structure generation approaches [173], this is not generally feasible for routine NTS applications due to the combinatorial explosion resulting in billions of candidates if generation cannot be constrained sufficiently. In LC-HRMS, structure generation has been applied to very tightly constrained cases, such as transformation of benzotriazoles, where the benzotriazole substructure helped constrain candidate numbers sufficiently for practical application  [327]. Although two open structure generators have been published recently  [328, 329] , these do not yet offer sufficient substructure functionality for MS applications, which have so far been coupled with the MOLGEN suite of structure generators  [330].
Recent developments in deep learning have opened up opportunities for autoencoders to support de novo structure elucidation from MS, demonstrated with MSNovelist  [331]. There are two main ways to search compound databases in MS-based NTS-by molecular formula or mass. If exact mass is available, it is usually most computationally efficient to search by exact mass, where the narrower the error margin in the mass range, the better the results (see Table  8 ). If only nominal mass is available, searching by molecular formula is preferable as too many candidates will be retrieved by nominal mass. Isotope and fragment information can be used to restrict candidates (when searching by exact mass) or possible molecular formulas (again, see Table  8 ). Several approaches are available to calculate molecular formulas for HRMS data, including GenForm  [231, 232] , the Seven Golden Rules [297], SIRIUS  [298]  and ZODIAC  [332].
Candidate selection: metadata/annotation information Finally, the use of additional information, often termed metadata, expert knowledge or annotation information, can be used to support the selection of candidates. Metadata such as the number of references associated with a candidate has been used for many years; reference counts are available for the major databases PubChem, Chem-Spider and CompTox. The combination of both reference and patent information was shown to be more powerful than reference information alone during evaluation of MetFrag  [284]. Both patent and reference counts are available for PubChem and PubChemLite. The influence of various scores on candidate selection for various in silico approaches was evaluated in the 2016 CASMI contest  [339]. During evaluation of PubChemLite, the addition of the annotation count score improved the ranking further; this can be used either as a scoring term or as a way of pre-selecting the candidates of interest according to their category, e.
Retention index use in GC In GC, retention indexes such as Kovats or Lee index are well-established  [348]  and are listed for many chemicals in the NIST database (see Sect. "EI-MS libraries"). They are based on a normalisation of the RT based on n-alkanes (Kovats) or PAHs  (Lee)  eluting before and after a chemical compound. Retention indices in GC exhibit excellent precision of < 0. 5% under reproducible measurement conditions  [349].
their RT. This procedure does not allow a comparison with other columns, gradients and laboratories and a sufficient number of standards is needed over the whole RT range (at least 20 compounds). The approach implemented in Pre-dRet overcomes the restrictions of linear regressions by applying generalised additive models (GAM) to enable a robust prediction  [351]. PredRet is continuously supplemented with new data and allows the prediction of the RTs of suspected or tentatively identified compounds for the custom chromatographic system by pairwise modelling compounds with known RTs against the systems included in the PredRet database (http:// predr et. org/). PredRet allows highly accurate predictions for a reasonable number of compounds  [352]. Multivariate QSRRs use additional descriptors, such as molecular mass, polarity, etc.
In newer approaches, machine learning (e. g. , neural networks) and large databases are used to improve the prediction of RT as well as also collision cross section based on the physico-chemical properties  [353] [354] [355] [356]. Several machine-learning-based QSRR methods for RP  [357] [358] [359] [360]  and a few for HILIC  [361, 362]  have been developed. More QSRR methods are proposed in the field of metabolomics, but are not necessarily fully transferable to anthropogenic compounds with a greater range of properties and elements. Halogenated compounds, especially highly fluorinated compounds, are notorious outliers in predictive methods.
The calibration set was evaluated by seven groups with their chromatographic systems including various, mostly C 18 -columns, different mobile phases with methanol and acetonitrile water gradients and different standard mixtures. The linearity of the calibration curves were found in most cases acceptable and the uncertainty for the RTI prediction for various test compounds low, with better prediction for methanol than acetonitrile gradients and ESI+ than. University of Athens provides a platform, where all RTI-related calculations with the models can be performed online (http:// rti. chem. uoa. gr/). The tool also provides information about the uncertainty and whether the selected compound falls into the applicability domain. Using this tool, the RTI predictions were calculated for > 65,000 substances listed in the NORMAN Substance Database.
XCMS2 was developed for processing MS2 data and comparing these results to the METLIN database. It can also provide structural information on features that could not be identified using the library by looking for characteristic fragment ions and neutral losses, which can then be compared to the database entries of compounds with different precursor masses  [370]. XCMS Online The online version of XCMS provides the same base functions as the desktop version with the added benefit of not needing a lot of computing power as data are uploaded for processing. Furthermore, it offers email notification when data are finished processing, additional visualisation and statistical options (e. g. , PCAs, RT correction curves, mirror plots) and the possibility to share data  [371]. SIRIUS was developed with a focus on identification of unknown compounds in non-target approaches, but recently received a "zero parameter feature detection" to simplify workflows (since version 4.