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  • 1
    Online Resource
    Online Resource
    Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS
    MDPI AG ; 2022
    In:  Molecules Vol. 27, No. 3 ( 2022-02-02), p. 1013-
    Details
    In: Molecules, MDPI AG, Vol. 27, No. 3 ( 2022-02-02), p. 1013-
    Abstract: LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules27031013
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2008644-1
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  • 2
    Online Resource
    Online Resource
    Uncertainty estimation strategies for quantitative non-targeted analysis
    Springer Science and Business Media LLC ; 2022
    In:  Analytical and Bioanalytical Chemistry Vol. 414, No. 17 ( 2022-07), p. 4919-4933
    Details
    In: Analytical and Bioanalytical Chemistry, Springer Science and Business Media LLC, Vol. 414, No. 17 ( 2022-07), p. 4919-4933
    Type of Medium: Online Resource
    ISSN: 1618-2642 , 1618-2650
    URL: Article
    DOI: 10.1007/s00216-022-04118-z
    RVK:
    VA 4300
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1459122-4
    detail.hit.zdb_id: 2071767-2
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Tutorial review on validation of liquid chromatography–mass spectrometry methods: Part II
    Elsevier BV ; 2015
    In:  Analytica Chimica Acta Vol. 870 ( 2015-04), p. 8-28
    Details
    In: Analytica Chimica Acta, Elsevier BV, Vol. 870 ( 2015-04), p. 8-28
    Type of Medium: Online Resource
    ISSN: 0003-2670
    URL: Article
    DOI: 10.1016/j.aca.2015.02.016
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 52-8
    detail.hit.zdb_id: 1483436-4
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  • 4
    Online Resource
    Online Resource
    Study of liquid chromatography/electrospray ionization mass spectrometry matrix effect on the example of glyphosate analysis from cereals
    Wiley ; 2011
    In:  Rapid Communications in Mass Spectrometry Vol. 25, No. 21 ( 2011-11-15), p. 3252-3258
    Details
    In: Rapid Communications in Mass Spectrometry, Wiley, Vol. 25, No. 21 ( 2011-11-15), p. 3252-3258
    Abstract: Glyphosate is one of the most common pesticides used in the pre‐harvest treatment of cereals. This paper examines the matrix effect of glyphosate liquid chromatography/electrospray ionization mass spectrometric (LC/ESI‐MS) analysis in wheat and rye. The matrix effect (ionization suppression) was found to be dependent on sample particle size taken for the extraction. If samples are ground to very small particles severe ionization suppression occurs. For lower glyphosate contents ( 〈 1 mg/kg) the signal may even be suppressed by more than 90%. The matrix effect was found to be dependent on the matrix – rye showed significantly stronger ionization suppression than wheat, although these matrices are not very different. The matrix effect also depends on the concentration of glyphosate in the post‐extraction spiked samples. It is demonstrated that the isotope‐labelled standard 13 C 2 ‐glyphosate undergoes different ionization suppression than glyphosate and is therefore not efficient in compensating for matrix effect. At the same time the extrapolative dilution approach allows to efficiently compensate for matrix effect. Copyright © 2011 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0951-4198 , 1097-0231
    URL: Issue
    URL: Article
    DOI: 10.1002/rcm.v25.21
    DOI: 10.1002/rcm.5222
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 2002158-6
    detail.hit.zdb_id: 58731-X
    SSG: 11
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  • 5
    Online Resource
    Online Resource
    Quantitative electrospray ionization efficiency scale: 10 years after
    Wiley ; 2021
    In:  Rapid Communications in Mass Spectrometry Vol. 35, No. 21 ( 2021-11-15)
    Details
    In: Rapid Communications in Mass Spectrometry, Wiley, Vol. 35, No. 21 ( 2021-11-15)
    Abstract: The first comprehensive quantitative scale of the efficiency of electrospray ionization (ESI) in the positive mode by monoprotonation, containing 62 compounds, was published in 2010. Several trends were found between the compound structure and ionization efficiency (IE) but, possibly because of the limited diversity of the compounds, some questions remained. This work undertakes to align the new data with the originally published IE scale and carry out statistical analysis of the resulting more extensive and diverse data set to derive more grounded relationships and offer a possibility of predicting log IE values. Methods Recently, several new IE studies with numerous compounds have been conducted. In several of them, more detailed investigations of the influence of compound structure, solvent properties, or instrument settings have been conducted. IE data from these studies and results from this work were combined, and the multilinear regression method was applied to relate IE to various compound parameters. Results The most comprehensive IE scale available, containing 334 compounds of highly diverse chemical nature and spanning 6 orders of magnitude of IE, has been compiled. Several useful trends were revealed. Conclusions The ESI ionization efficiency of a compound by protonation is mainly affected by three factors: basicity (expressed by p K aH in water), molecular size (expressed by molar volume or surface area), and hydrophobicity of the ion (expressed by charge delocalization in the ion or its partition coefficient between a water–acetonitrile mixture and hexane). The presented models can be used for tentative prediction of log IE of new compounds (under the used conditions) from parameters that can be computed using commercially available software. The root mean square error of prediction is in the range of 0.7–0.8 log units.
    Type of Medium: Online Resource
    ISSN: 0951-4198 , 1097-0231
    URL: Issue
    URL: Article
    DOI: 10.1002/rcm.v35.21
    DOI: 10.1002/rcm.9178
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2002158-6
    detail.hit.zdb_id: 58731-X
    SSG: 11
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  • 6
    Online Resource
    Online Resource
    Meet the Associate Editors: Anneli Kruve
    Wiley ; 2019
    In:  Rapid Communications in Mass Spectrometry Vol. 33, No. S3 ( 2019-07), p. 18-19
    Details
    In: Rapid Communications in Mass Spectrometry, Wiley, Vol. 33, No. S3 ( 2019-07), p. 18-19
    Type of Medium: Online Resource
    ISSN: 0951-4198 , 1097-0231
    URL: Issue
    URL: Article
    DOI: 10.1002/rcm.v33.S3
    DOI: 10.1002/rcm.8333
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2002158-6
    detail.hit.zdb_id: 58731-X
    SSG: 11
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  • 7
    Online Resource
    Online Resource
    Ionization efficiency ladders as tools for choosing ionization mode and solvent in liquid chromatography/mass spectrometry
    Wiley ; 2019
    In:  Rapid Communications in Mass Spectrometry Vol. 33, No. 23 ( 2019-12-15), p. 1834-1843
    Details
    In: Rapid Communications in Mass Spectrometry, Wiley, Vol. 33, No. 23 ( 2019-12-15), p. 1834-1843
    Abstract: The choice of mobile phase components and optimal ion source, mainly electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), is a crucial part in liquid chromatography/mass spectrometry (LC/MS) method development to achieve higher sensitivity and lower detection limits. In this study we demonstrate how to rigorously solve these questions by using ionization efficiency scales. Methods Four ionization efficiency scales are used: recorded with both APCI and ESI sources and using both methanol‐ and acetonitrile‐containing mobile phases. Each scale contains altogether more than 50 compounds. In addition, measurements with a chromatographic column were also performed. Results We observed a correlation between calibration graph slopes under LC conditions and log IE values in ESI (but not APCI) thereby validating the use of log IE values for choosing the ion source. Most of the studied compounds preferred ESI as an ion source and methanol as mobile organic phase. APCI remains the ion source of choice for polycyclic aromatic hydrocarbons. For APCI, both acetonitrile and methanol provide similar ionization efficiencies with few exceptions. Conclusions Overall the results of this work give a concise guideline for practitioners in choosing an ion source for LC/MS analysis on the basis of the chemical nature of the analytes.
    Type of Medium: Online Resource
    ISSN: 0951-4198 , 1097-0231
    URL: Issue
    URL: Article
    DOI: 10.1002/rcm.v33.23
    DOI: 10.1002/rcm.8545
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2002158-6
    detail.hit.zdb_id: 58731-X
    SSG: 11
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  • 8
    Online Resource
    Online Resource
    Imine-based [2]catenanes in water
    Royal Society of Chemistry (RSC) ; 2018
    In:  Chemical Science Vol. 9, No. 5 ( 2018), p. 1317-1322
    Details
    In: Chemical Science, Royal Society of Chemistry (RSC), Vol. 9, No. 5 ( 2018), p. 1317-1322
    Type of Medium: Online Resource
    ISSN: 2041-6520 , 2041-6539
    URL: Article
    DOI: 10.1039/C7SC04901C
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2018
    detail.hit.zdb_id: 2559110-1
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  • 9
    Online Resource
    Online Resource
    Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS
    MDPI AG ; 2021
    In:  Molecules Vol. 26, No. 12 ( 2021-06-09), p. 3524-
    Details
    In: Molecules, MDPI AG, Vol. 26, No. 12 ( 2021-06-09), p. 3524-
    Abstract: Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules26123524
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 10
    Online Resource
    Online Resource
    Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater
    Springer Science and Business Media LLC ; 2021
    In:  Analytical and Bioanalytical Chemistry Vol. 413, No. 6 ( 2021-03), p. 1549-1559
    Details
    In: Analytical and Bioanalytical Chemistry, Springer Science and Business Media LLC, Vol. 413, No. 6 ( 2021-03), p. 1549-1559
    Abstract: A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.
    Type of Medium: Online Resource
    ISSN: 1618-2642 , 1618-2650
    URL: Article
    DOI: 10.1007/s00216-020-03109-2
    RVK:
    VA 4300
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1459122-4
    detail.hit.zdb_id: 2071767-2
    SSG: 12
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