Design and Validation of a Portable Machine Learning-Based Electronic Nose
Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas ch...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3923 |
| _version_ | 1797530987721129984 |
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| author | Yixu Huang Iyll-Joon Doh Euiwon Bae |
| author_facet | Yixu Huang Iyll-Joon Doh Euiwon Bae |
| author_sort | Yixu Huang |
| collection | DOAJ |
| description | Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications. |
| first_indexed | 2024-03-10T10:37:42Z |
| format | Article |
| id | doaj.art-070b1ac877dd4d719d482dc909f5fb63 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:37:42Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-070b1ac877dd4d719d482dc909f5fb632023-11-21T23:08:57ZengMDPI AGSensors1424-82202021-06-012111392310.3390/s21113923Design and Validation of a Portable Machine Learning-Based Electronic NoseYixu Huang0Iyll-Joon Doh1Euiwon Bae2Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USAApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USAApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USAVolatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.https://www.mdpi.com/1424-8220/21/11/3923metal-oxide sensorolfactoryportable instrumentfood authenticationmachine-learningelectronic nose |
| spellingShingle | Yixu Huang Iyll-Joon Doh Euiwon Bae Design and Validation of a Portable Machine Learning-Based Electronic Nose Sensors metal-oxide sensor olfactory portable instrument food authentication machine-learning electronic nose |
| title | Design and Validation of a Portable Machine Learning-Based Electronic Nose |
| title_full | Design and Validation of a Portable Machine Learning-Based Electronic Nose |
| title_fullStr | Design and Validation of a Portable Machine Learning-Based Electronic Nose |
| title_full_unstemmed | Design and Validation of a Portable Machine Learning-Based Electronic Nose |
| title_short | Design and Validation of a Portable Machine Learning-Based Electronic Nose |
| title_sort | design and validation of a portable machine learning based electronic nose |
| topic | metal-oxide sensor olfactory portable instrument food authentication machine-learning electronic nose |
| url | https://www.mdpi.com/1424-8220/21/11/3923 |
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