Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability Article Swipe
Gerrit Renner
,
Max Reuschenbach
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00216-023-04776-7
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00216-023-04776-7
Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.
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- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00216-023-04776-7
- https://link.springer.com/content/pdf/10.1007/s00216-023-04776-7.pdf
- OA Status
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- Cited By
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- OpenAlex ID
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Raw OpenAlex JSON
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https://doi.org/10.1007/s00216-023-04776-7Digital Object Identifier
- Title
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Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparabilityWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-29Full publication date if available
- Authors
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Gerrit Renner, Max ReuschenbachList of authors in order
- Landing page
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https://doi.org/10.1007/s00216-023-04776-7Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s00216-023-04776-7.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s00216-023-04776-7.pdfDirect OA link when available
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Comparability, Computer science, Management science, Data science, Algorithm, Data mining, Engineering, Mathematics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
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26Total citation count in OpenAlex
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2025: 10, 2024: 16Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.need | 90, 122 |
| abstract_inverted_index.peak | 37, 62 |
| abstract_inverted_index.such | 130 |
| abstract_inverted_index.user | 82 |
| abstract_inverted_index.(NTS) | 3 |
| abstract_inverted_index.(XIC) | 59 |
| abstract_inverted_index.input | 83 |
| abstract_inverted_index.offer | 143 |
| abstract_inverted_index.these | 161 |
| abstract_inverted_index.users | 151 |
| abstract_inverted_index.across | 185 |
| abstract_inverted_index.field, | 175 |
| abstract_inverted_index.future | 144 |
| abstract_inverted_index.review | 43 |
| abstract_inverted_index.address | 96 |
| abstract_inverted_index.advance | 173 |
| abstract_inverted_index.complex | 19 |
| abstract_inverted_index.created | 29 |
| abstract_inverted_index.discuss | 71 |
| abstract_inverted_index.enhance | 182 |
| abstract_inverted_index.feature | 40 |
| abstract_inverted_index.improve | 176 |
| abstract_inverted_index.issues, | 101 |
| abstract_inverted_index.propose | 127 |
| abstract_inverted_index.quality | 100, 112 |
| abstract_inverted_index.unknown | 16 |
| abstract_inverted_index.various | 77 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.approach | 11 |
| abstract_inverted_index.enhanced | 25 |
| abstract_inverted_index.exchange | 138 |
| abstract_inverted_index.focusing | 53 |
| abstract_inverted_index.in-depth | 46 |
| abstract_inverted_index.methods, | 52 |
| abstract_inverted_index.powerful | 6 |
| abstract_inverted_index.provides | 44 |
| abstract_inverted_index.results, | 87, 180 |
| abstract_inverted_index.samples. | 20 |
| abstract_inverted_index.studies. | 187 |
| abstract_inverted_index.analysis, | 33 |
| abstract_inverted_index.automated | 92 |
| abstract_inverted_index.building, | 60 |
| abstract_inverted_index.chemistry | 10 |
| abstract_inverted_index.community | 171 |
| abstract_inverted_index.compounds | 17 |
| abstract_inverted_index.detecting | 13 |
| abstract_inverted_index.different | 186 |
| abstract_inverted_index.extracted | 56 |
| abstract_inverted_index.features. | 69 |
| abstract_inverted_index.highlight | 120 |
| abstract_inverted_index.including | 34 |
| abstract_inverted_index.influence | 80 |
| abstract_inverted_index.intervals | 108 |
| abstract_inverted_index.parameter | 93 |
| abstract_inverted_index.potential | 128 |
| abstract_inverted_index.screening | 2 |
| abstract_inverted_index.strengths | 73 |
| abstract_inverted_index.utilizing | 132 |
| abstract_inverted_index.Non-target | 1 |
| abstract_inverted_index.addressing | 160 |
| abstract_inverted_index.algorithms | 156 |
| abstract_inverted_index.alignment, | 64 |
| abstract_inverted_index.analytical | 9 |
| abstract_inverted_index.assessment | 113 |
| abstract_inverted_index.challenges | 30, 162 |
| abstract_inverted_index.confidence | 107 |
| abstract_inverted_index.detection, | 38 |
| abstract_inverted_index.developers | 149 |
| abstract_inverted_index.importance | 104 |
| abstract_inverted_index.parameters | 84 |
| abstract_inverted_index.platforms. | 139 |
| abstract_inverted_index.presented, | 168 |
| abstract_inverted_index.processing | 51, 116, 155 |
| abstract_inverted_index.solutions, | 129 |
| abstract_inverted_index.statistics | 134 |
| abstract_inverted_index.weaknesses | 75 |
| abstract_inverted_index.workflows. | 117, 158 |
| abstract_inverted_index.algorithms, | 78 |
| abstract_inverted_index.conclusion, | 141 |
| abstract_inverted_index.cross-study | 124 |
| abstract_inverted_index.emphasizing | 102 |
| abstract_inverted_index.extraction. | 41 |
| abstract_inverted_index.identifying | 15 |
| abstract_inverted_index.open-access | 136 |
| abstract_inverted_index.reliability | 178 |
| abstract_inverted_index.uncertainty | 97 |
| abstract_inverted_index.Furthermore, | 118 |
| abstract_inverted_index.capabilities | 27 |
| abstract_inverted_index.capitalizing | 164 |
| abstract_inverted_index.centroiding, | 55 |
| abstract_inverted_index.chromatogram | 58 |
| abstract_inverted_index.perspectives | 145 |
| abstract_inverted_index.spectrometry | 23 |
| abstract_inverted_index.standardized | 133 |
| abstract_inverted_index.comparability | 125, 184 |
| abstract_inverted_index.environmental | 7 |
| abstract_inverted_index.incorporating | 106 |
| abstract_inverted_index.opportunities | 167 |
| abstract_inverted_index.optimization. | 94 |
| abstract_inverted_index.understanding | 47 |
| abstract_inverted_index.preprocessing, | 36 |
| abstract_inverted_index.prioritization | 67 |
| abstract_inverted_index.High-resolution | 21 |
| abstract_inverted_index.chromatographic | 61 |
| abstract_inverted_index.recommendations | 147 |
| abstract_inverted_index.characterization, | 63 |
| abstract_inverted_index.componentization, | 65 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.95541676 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |