Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays
Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography...
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/11/2547 |
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| author | Tuo Gao Yongchen Wang Chengwu Zhang Zachariah A. Pittman Alexandra M. Oliveira Kan Fu Jing Zhao Ranjan Srivastava Brian G. Willis |
| author_facet | Tuo Gao Yongchen Wang Chengwu Zhang Zachariah A. Pittman Alexandra M. Oliveira Kan Fu Jing Zhao Ranjan Srivastava Brian G. Willis |
| author_sort | Tuo Gao |
| collection | DOAJ |
| description | Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (Δ<i>R</i>/<i>R<sub>0</sub></i>), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products. |
| first_indexed | 2024-04-11T18:00:42Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:00:42Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-307d9c676cf640d8a23a86da574321632022-12-22T04:10:32ZengMDPI AGSensors1424-82202019-06-011911254710.3390/s19112547s19112547Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor ArraysTuo Gao0Yongchen Wang1Chengwu Zhang2Zachariah A. Pittman3Alexandra M. Oliveira4Kan Fu5Jing Zhao6Ranjan Srivastava7Brian G. Willis8Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Chemistry, University of Connecticut, Storrs, CT 06269, USADepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Materials Science and Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Chemistry, University of Connecticut, Storrs, CT 06269, USADepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USANanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (Δ<i>R</i>/<i>R<sub>0</sub></i>), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.https://www.mdpi.com/1424-8220/19/11/2547tea aroma sensinggold nanoparticles (AuNPs)chemiresistor arraylinear discriminant analysis (LDA)pattern recognition |
| spellingShingle | Tuo Gao Yongchen Wang Chengwu Zhang Zachariah A. Pittman Alexandra M. Oliveira Kan Fu Jing Zhao Ranjan Srivastava Brian G. Willis Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays Sensors tea aroma sensing gold nanoparticles (AuNPs) chemiresistor array linear discriminant analysis (LDA) pattern recognition |
| title | Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays |
| title_full | Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays |
| title_fullStr | Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays |
| title_full_unstemmed | Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays |
| title_short | Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays |
| title_sort | classification of tea aromas using multi nanoparticle based chemiresistor arrays |
| topic | tea aroma sensing gold nanoparticles (AuNPs) chemiresistor array linear discriminant analysis (LDA) pattern recognition |
| url | https://www.mdpi.com/1424-8220/19/11/2547 |
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