Deep Learning and Mass Spectrum Based Analysis of Vocs Components Article Swipe
Lifeng Liu
,
Xuxia Zhao
,
Xin Lv
,
Jian-Kang Mu
,
Ping Cheng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.57237/j.wjese.2023.02.006
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.57237/j.wjese.2023.02.006
The composition and concentration of Volatile Organic Compounds (VOCs) in the atmosphere can reflect the quality of the air, and the environmental quality changes with the quantity of these compounds. When unknown VOCs are encountered, researchers usually use gas chromatography-mass spectrometry (GC-MS) to measure and analyze them. This discriminative mode requires data analysts with a certain theoretical and practical foundation, is demanding and labor-intensive, and may also introduce errors due to the numerous steps. In order to solve these problems, we propose a deep learning and mass spectrum based method for the analysis of Vocs components. Using the deep learning technique, first, a high-quality mass spectral library is constructed as a reference library using molecular fingerprint information, and then, the sample data obtained in the GC-MS gas chromatography-mass spectrometer is preprocessed with data to extract mass spectra that can represent the VOCs components; finally, the selected candidate mass spectra are library matched with the reference library to return high matching VOCs components results. The experimental results show that the method can accurately and quickly discriminate the components of VOCs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.57237/j.wjese.2023.02.006
- http://article.isciencegroup.com/pdf/10640024.pdf
- OA Status
- bronze
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386544493
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386544493Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.57237/j.wjese.2023.02.006Digital Object Identifier
- Title
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Deep Learning and Mass Spectrum Based Analysis of Vocs ComponentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-08Full publication date if available
- Authors
-
Lifeng Liu, Xuxia Zhao, Xin Lv, Jian-Kang Mu, Ping ChengList of authors in order
- Landing page
-
https://doi.org/10.57237/j.wjese.2023.02.006Publisher landing page
- PDF URL
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https://article.isciencegroup.com/pdf/10640024.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://article.isciencegroup.com/pdf/10640024.pdfDirect OA link when available
- Concepts
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Mass spectrum, Mass spectrometry, Discriminative model, Gas chromatography–mass spectrometry, Sample (material), Fingerprint (computing), Chemistry, Chromatography, Process engineering, Artificial intelligence, Computer science, Analytical Chemistry (journal), EngineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 10, 14, 17, 20, 25, 71, 91, 97, 119, 124, 140, 144, 153, 168, 175 |
| abstract_inverted_index.use | 37 |
| abstract_inverted_index.This | 47 |
| abstract_inverted_index.VOCs | 32, 141, 160 |
| abstract_inverted_index.Vocs | 94 |
| abstract_inverted_index.When | 30 |
| abstract_inverted_index.air, | 18 |
| abstract_inverted_index.also | 66 |
| abstract_inverted_index.data | 51, 121, 132 |
| abstract_inverted_index.deep | 83, 98 |
| abstract_inverted_index.high | 158 |
| abstract_inverted_index.mass | 86, 104, 135, 147 |
| abstract_inverted_index.mode | 49 |
| abstract_inverted_index.show | 166 |
| abstract_inverted_index.that | 137, 167 |
| abstract_inverted_index.with | 24, 53, 131, 152 |
| abstract_inverted_index.GC-MS | 125 |
| abstract_inverted_index.Using | 96 |
| abstract_inverted_index.VOCs. | 178 |
| abstract_inverted_index.based | 88 |
| abstract_inverted_index.order | 75 |
| abstract_inverted_index.solve | 77 |
| abstract_inverted_index.them. | 46 |
| abstract_inverted_index.then, | 118 |
| abstract_inverted_index.these | 28, 78 |
| abstract_inverted_index.using | 113 |
| abstract_inverted_index.(VOCs) | 8 |
| abstract_inverted_index.errors | 68 |
| abstract_inverted_index.first, | 101 |
| abstract_inverted_index.method | 89, 169 |
| abstract_inverted_index.return | 157 |
| abstract_inverted_index.sample | 120 |
| abstract_inverted_index.steps. | 73 |
| abstract_inverted_index.(GC-MS) | 41 |
| abstract_inverted_index.Organic | 6 |
| abstract_inverted_index.analyze | 45 |
| abstract_inverted_index.certain | 55 |
| abstract_inverted_index.changes | 23 |
| abstract_inverted_index.extract | 134 |
| abstract_inverted_index.library | 106, 112, 150, 155 |
| abstract_inverted_index.matched | 151 |
| abstract_inverted_index.measure | 43 |
| abstract_inverted_index.propose | 81 |
| abstract_inverted_index.quality | 15, 22 |
| abstract_inverted_index.quickly | 173 |
| abstract_inverted_index.reflect | 13 |
| abstract_inverted_index.results | 165 |
| abstract_inverted_index.spectra | 136, 148 |
| abstract_inverted_index.unknown | 31 |
| abstract_inverted_index.usually | 36 |
| abstract_inverted_index.Volatile | 5 |
| abstract_inverted_index.analysis | 92 |
| abstract_inverted_index.analysts | 52 |
| abstract_inverted_index.finally, | 143 |
| abstract_inverted_index.learning | 84, 99 |
| abstract_inverted_index.matching | 159 |
| abstract_inverted_index.numerous | 72 |
| abstract_inverted_index.obtained | 122 |
| abstract_inverted_index.quantity | 26 |
| abstract_inverted_index.requires | 50 |
| abstract_inverted_index.results. | 162 |
| abstract_inverted_index.selected | 145 |
| abstract_inverted_index.spectral | 105 |
| abstract_inverted_index.spectrum | 87 |
| abstract_inverted_index.Compounds | 7 |
| abstract_inverted_index.candidate | 146 |
| abstract_inverted_index.demanding | 61 |
| abstract_inverted_index.introduce | 67 |
| abstract_inverted_index.molecular | 114 |
| abstract_inverted_index.practical | 58 |
| abstract_inverted_index.problems, | 79 |
| abstract_inverted_index.reference | 111, 154 |
| abstract_inverted_index.represent | 139 |
| abstract_inverted_index.accurately | 171 |
| abstract_inverted_index.atmosphere | 11 |
| abstract_inverted_index.components | 161, 176 |
| abstract_inverted_index.compounds. | 29 |
| abstract_inverted_index.technique, | 100 |
| abstract_inverted_index.components. | 95 |
| abstract_inverted_index.components; | 142 |
| abstract_inverted_index.composition | 1 |
| abstract_inverted_index.constructed | 108 |
| abstract_inverted_index.fingerprint | 115 |
| abstract_inverted_index.foundation, | 59 |
| abstract_inverted_index.researchers | 35 |
| abstract_inverted_index.theoretical | 56 |
| abstract_inverted_index.discriminate | 174 |
| abstract_inverted_index.encountered, | 34 |
| abstract_inverted_index.experimental | 164 |
| abstract_inverted_index.high-quality | 103 |
| abstract_inverted_index.information, | 116 |
| abstract_inverted_index.preprocessed | 130 |
| abstract_inverted_index.spectrometer | 128 |
| abstract_inverted_index.spectrometry | 40 |
| abstract_inverted_index.concentration | 3 |
| abstract_inverted_index.environmental | 21 |
| abstract_inverted_index.discriminative | 48 |
| abstract_inverted_index.labor-intensive, | 63 |
| abstract_inverted_index.chromatography-mass | 39, 127 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5039773551 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I113940042 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.13727078 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |