Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level Article Swipe
Salih Okur
,
Chun Li
,
Zejun Zhang
,
Sahi Vaidurya Pratap
,
Mohammed Sarheed
,
Adnan Kanbar
,
Leonard Franke
,
Felix Geislhöringer
,
Lars Heinke
,
Uli Lemmer
,
Peter Nick
,
Christof Wöll
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/chemosensors9070171
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/chemosensors9070171
Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/chemosensors9070171
- https://www.mdpi.com/2227-9040/9/7/171/pdf?version=1625620544
- OA Status
- gold
- Cited By
- 7
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3180969799
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3180969799Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/chemosensors9070171Digital Object Identifier
- Title
-
Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus LevelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-06Full publication date if available
- Authors
-
Salih Okur, Chun Li, Zejun Zhang, Sahi Vaidurya Pratap, Mohammed Sarheed, Adnan Kanbar, Leonard Franke, Felix Geislhöringer, Lars Heinke, Uli Lemmer, Peter Nick, Christof WöllList of authors in order
- Landing page
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https://doi.org/10.3390/chemosensors9070171Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-9040/9/7/171/pdf?version=1625620544Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2227-9040/9/7/171/pdf?version=1625620544Direct OA link when available
- Concepts
-
Electronic nose, Lamiaceae, Quartz crystal microbalance, Linear discriminant analysis, Genus, Biology, Pattern recognition (psychology), Botany, Artificial intelligence, Computer science, Chemistry, Neuroscience, Organic chemistry, AdsorptionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 2, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.flowering | 7 |
| abstract_inverted_index.medicinal | 20 |
| abstract_inverted_index.outgroup. | 139 |
| abstract_inverted_index.safeguard | 247 |
| abstract_inverted_index.similarly | 133 |
| abstract_inverted_index.strategy, | 118 |
| abstract_inverted_index.unrelated | 138 |
| abstract_inverted_index.Prediction | 196 |
| abstract_inverted_index.QCM-based, | 181 |
| abstract_inverted_index.SURMOF/QCM | 154 |
| abstract_inverted_index.analytics, | 74 |
| abstract_inverted_index.commercial | 44 |
| abstract_inverted_index.cumbersome | 73 |
| abstract_inverted_index.discerning | 61 |
| abstract_inverted_index.electronic | 91 |
| abstract_inverted_index.globalized | 254 |
| abstract_inverted_index.(QCM)-based | 90 |
| abstract_inverted_index.demonstrate | 86 |
| abstract_inverted_index.lemongrass, | 130 |
| abstract_inverted_index.speciation, | 37 |
| abstract_inverted_index.statistical | 167 |
| abstract_inverted_index.surrogation | 54 |
| abstract_inverted_index.adulteration | 52 |
| abstract_inverted_index.alternative, | 97 |
| abstract_inverted_index.applications | 21 |
| abstract_inverted_index.discriminant | 191 |
| abstract_inverted_index.discriminate | 228 |
| abstract_inverted_index.measurements | 201 |
| abstract_inverted_index.microbalance | 89 |
| abstract_inverted_index.characterized | 50 |
| abstract_inverted_index.demonstrating | 237 |
| abstract_inverted_index.differentiate | 141 |
| abstract_inverted_index.spectroscopy. | 83 |
| abstract_inverted_index.Authenticating | 59 |
| abstract_inverted_index.classification | 194 |
| abstract_inverted_index.cost-efficient | 96 |
| abstract_inverted_index.discrimination | 102 |
| abstract_inverted_index.quartz-crystal | 88 |
| abstract_inverted_index.unambiguously, | 147 |
| abstract_inverted_index.chromatography, | 78 |
| abstract_inverted_index.species-richest | 4 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5088116908 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I102335020 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[1].score | 0.49000000953674316 |
| sustainable_development_goals[1].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.60653267 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |