Identification of Mint Scents Using a QCM Based E-Nose
Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatogra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/9/2/31 |
| _version_ | 1797415271923712000 |
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| author | Salih Okur Mohammed Sarheed Robert Huber Zejun Zhang Lars Heinke Adnan Kanbar Christof Wöll Peter Nick Uli Lemmer |
| author_facet | Salih Okur Mohammed Sarheed Robert Huber Zejun Zhang Lars Heinke Adnan Kanbar Christof Wöll Peter Nick Uli Lemmer |
| author_sort | Salih Okur |
| collection | DOAJ |
| description | Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO<sub>2</sub>, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (<i>Mentha x piperita</i>, <i>Mentha spicata</i>, <i>Mentha spicata ssp. crispa, Mentha longifolia</i>, <i>Agastache rugosa, and Nepeta cataria</i>). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner. |
| first_indexed | 2024-03-09T05:46:25Z |
| format | Article |
| id | doaj.art-a7117db289b44db7ae21e42b07c10858 |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T05:46:25Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-a7117db289b44db7ae21e42b07c108582023-12-03T12:21:20ZengMDPI AGChemosensors2227-90402021-02-01923110.3390/chemosensors9020031Identification of Mint Scents Using a QCM Based E-NoseSalih Okur0Mohammed Sarheed1Robert Huber2Zejun Zhang3Lars Heinke4Adnan Kanbar5Christof Wöll6Peter Nick7Uli Lemmer8Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Hermann-Von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, GermanyKarlsruhe Institute of Technology, Botanical Institute, Molecular Cell Biology, Fritz-Haber-Weg, 76131 Karlsruhe, GermanyLight Technology Institute, Karlsruhe Institute of Technology, Engesserstraße 13, 76131 Karlsruhe, GermanyInstitute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Hermann-Von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, GermanyLight Technology Institute, Karlsruhe Institute of Technology, Engesserstraße 13, 76131 Karlsruhe, GermanyKarlsruhe Institute of Technology, Botanical Institute, Molecular Cell Biology, Fritz-Haber-Weg, 76131 Karlsruhe, GermanyInstitute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Hermann-Von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, GermanyKarlsruhe Institute of Technology, Botanical Institute, Molecular Cell Biology, Fritz-Haber-Weg, 76131 Karlsruhe, GermanyLight Technology Institute, Karlsruhe Institute of Technology, Engesserstraße 13, 76131 Karlsruhe, GermanyMints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO<sub>2</sub>, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (<i>Mentha x piperita</i>, <i>Mentha spicata</i>, <i>Mentha spicata ssp. crispa, Mentha longifolia</i>, <i>Agastache rugosa, and Nepeta cataria</i>). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.https://www.mdpi.com/2227-9040/9/2/31mintplant volatileselectronic noseprincipal component analysislinear discriminant analysisk-nearest-neighbors analysis |
| spellingShingle | Salih Okur Mohammed Sarheed Robert Huber Zejun Zhang Lars Heinke Adnan Kanbar Christof Wöll Peter Nick Uli Lemmer Identification of Mint Scents Using a QCM Based E-Nose Chemosensors mint plant volatiles electronic nose principal component analysis linear discriminant analysis k-nearest-neighbors analysis |
| title | Identification of Mint Scents Using a QCM Based E-Nose |
| title_full | Identification of Mint Scents Using a QCM Based E-Nose |
| title_fullStr | Identification of Mint Scents Using a QCM Based E-Nose |
| title_full_unstemmed | Identification of Mint Scents Using a QCM Based E-Nose |
| title_short | Identification of Mint Scents Using a QCM Based E-Nose |
| title_sort | identification of mint scents using a qcm based e nose |
| topic | mint plant volatiles electronic nose principal component analysis linear discriminant analysis k-nearest-neighbors analysis |
| url | https://www.mdpi.com/2227-9040/9/2/31 |
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