Chemiresistive Sensor Array and Machine Learning Classification of Food
Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil...
| Main Authors: | , , , , , , |
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| Outros autores: | |
| Formato: | Artigo |
| Idioma: | English |
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American Chemical Society (ACS)
2020
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| Acceso en liña: | https://hdl.handle.net/1721.1/128141 |
| _version_ | 1826192600038637568 |
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| author | Schroeder, Vera Evans, Ethan Daniel Wu, You-Chi Mason Voll, Constantin-Chri Alexander McDonald, Benjamin Rebbeck Savagatrup, Suchol Swager, Timothy M |
| author2 | Massachusetts Institute of Technology. Department of Chemistry |
| author_facet | Massachusetts Institute of Technology. Department of Chemistry Schroeder, Vera Evans, Ethan Daniel Wu, You-Chi Mason Voll, Constantin-Chri Alexander McDonald, Benjamin Rebbeck Savagatrup, Suchol Swager, Timothy M |
| author_sort | Schroeder, Vera |
| collection | MIT |
| description | Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models - a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set. |
| first_indexed | 2024-09-23T09:24:06Z |
| format | Article |
| id | mit-1721.1/128141 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:24:06Z |
| publishDate | 2020 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1281412022-09-30T14:23:53Z Chemiresistive Sensor Array and Machine Learning Classification of Food Schroeder, Vera Evans, Ethan Daniel Wu, You-Chi Mason Voll, Constantin-Chri Alexander McDonald, Benjamin Rebbeck Savagatrup, Suchol Swager, Timothy M Massachusetts Institute of Technology. Department of Chemistry Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies Massachusetts Institute of Technology. Department of Biological Engineering Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models - a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set. National Science Foundation (Grant DMR-1410718) 2020-10-21T22:13:43Z 2020-10-21T22:13:43Z 2019-07 2019-05 2020-10-07T18:17:20Z Article http://purl.org/eprint/type/JournalArticle 2379-3694 https://hdl.handle.net/1721.1/128141 Schroeder, Vera et al. "Chemiresistive Sensor Array and Machine Learning Classification of Food." ACS Sensors 4, 8 (July 2019): 2101–2108 © 2019 American Chemical Society en http://dx.doi.org/10.1021/acssensors.9b00825 ACS Sensors Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) Prof. Swager via Ye Li |
| spellingShingle | Schroeder, Vera Evans, Ethan Daniel Wu, You-Chi Mason Voll, Constantin-Chri Alexander McDonald, Benjamin Rebbeck Savagatrup, Suchol Swager, Timothy M Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title | Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title_full | Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title_fullStr | Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title_full_unstemmed | Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title_short | Chemiresistive Sensor Array and Machine Learning Classification of Food |
| title_sort | chemiresistive sensor array and machine learning classification of food |
| url | https://hdl.handle.net/1721.1/128141 |
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