Design and Validation of a Portable Machine Learning-Based Electronic Nose
<p>Comparison of relative sensitivity of 10 candidate metal-oxide sensors with respect to the detectability of 17 chemical species. Sensor selection was determined based on wider selection of chemical species with strongest sensitivities.</p> "> Figure 2
<p>Functional block diagram of the e-nose. STM32F031 was used as the microcontroller unit that controls the fan and the heater by pulse width modulation (PWM). The 12-bit ADC receives the senso- array readings and transfers the data to the Raspberry Pi4 via a USB serial adapter.</p> "> Figure 3
<p>Circuit diagram of the proposed system. (<b>A</b>) STM32 microcontroller was used to control and collect all the data. (<b>B</b>) Each sensor is powered by a 5 V signal and enable pin, while a potentiometer (R2) is used to provide a voltage divider circuit. Shown here is the MICS-5524 sensor, which, unlike other sensors, requires a heater to operate. (<b>C</b>) The voltage regulator part is responsible for charging the battery. (<b>D</b>) The proportional control of heater and fan via transistors.</p> "> Figure 4
<p>Photo of actual e-nose system. (<b>A</b>) Schematics of the functional composition of the overall system. (<b>B</b>) Inside view of top half, where each sensor is aligned to the side of the EMLA. (<b>C</b>) Photo of the fully assembled system with labels representing the control and access ports. (<b>D</b>) Inside view of the bottom half showing battery and microcontroller.</p> "> Figure 5
<p>Calibration results using isopropyl alcohol. (<b>A</b>) Time-dependent sensor output for four modules (MQ4, MQ5, MQ6, and TGS2602) that showed significant response to the volatile sample of 35% isopropyl alcohol. The sample tray is positioned under the e-nose unit at time 0. (<b>B</b>) The response curve for three different concentrations. MQ6 and MQ4 show good linearity, with R2 values of 0.998 and 0.997, respectively.</p> "> Figure 6
<p>Experimental setup. (<b>A</b>) The actual setup. A heater set to 50 deg Celsius was used to control the volatility of the oil sample. (<b>B</b>) The response dynamics of the sensor for wine samples; different sensors show variations in time constants when samples were positioned in and out of the EMLA. (<b>C</b>) Schematics of the setup where sample tray, platform, enclosure for the EMLA were 3D-printed. (<b>D</b>) Actual dimensions of the disposable 3D-printed sample tray: a circle 30 mm in diameter with a 5 mm rim, printed with polylactic acid (PLA) filament.</p> "> Figure 7
<p>Training set results. (<b>A</b>) Confusion matrix of the training data. (<b>B</b>) ROC curve for cabernet as a positive sample by linear SVM.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sensor Selection
2.2. Circuit Design
2.3. Data Acquisition
2.4. Calibration
2.5. Sample Preparation
2.6. Data Analysis
3. Results
3.1. E-Nose System
3.2. Calibration Experiment
3.3. Wine Experiment
3.4. Oil Experiment
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Classifier | Type | Sensitivity | Specificity | PPV | NPV | Accuracy |
---|---|---|---|---|---|---|
Linear discriminant | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
Pinot noir | 1 | 0.8889 | 1 | 0.9643 | 0.9722 | |
Merlot | 0.963 | 1 | 0.9 | 1 | 0.9722 | |
Quadratic discriminant | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
Pinot noir | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
Merlot | 0.833 | 1 | 0.6667 | 1 | 0.875 | |
Linear SVM | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
Pinot noir | 1 | 0.944 | 1 | 0.9818 | 0.9861 | |
Merlot | 0.9815 | 1 | 0.9474 | 1 | 0.9861 | |
Quadratic SVM | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
Pinot noir | 1 | 0.944 | 1 | 0.9818 | 0.9861 | |
Merlot | 0.9815 | 1 | 0.9474 | 1 | 0.9861 | |
Bayes Gaussian | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
Pinot noir | 1 | 0.833 | 1 | 0.9474 | 0.9583 | |
Merlot | 0.7778 | 1 | 0.6 | 1 | 0.8333 | |
KNN fine | Zinfandel | 1 | 1 | 1 | 1 | 1 |
Cabernet sauvignon | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
Pinot noir | 1 | 0.9444 | 1 | 0.9818 | 0.9861 | |
Merlot | 0.8148 | 1 | 0.6429 | 1 | 0.8611 |
Classifier. | Type | Sensitivity | Specificity | PPV | NPV | Accuracy |
---|---|---|---|---|---|---|
Quadratic discriminant | Grapeseed oil | 1 | 0.2 | 1 | 0.7143 | 0.7333 |
Peanut oil | 0.115 | 0.95 | 0.3493 | 0.8214 | 0.3933 | |
Olive oil | 0.96 | 0 | 0 | 0.6575 | 0.64 | |
Quadratic SVM | Grapeseed oil | 1 | 1 | 1 | 1 | 1 |
Peanut oil | 0.945 | 0.91 | 0.8922 | 0.9545 | 0.9333 | |
Olive oil | 0.955 | 0.89 | 0.9082 | 0.9455 | 0.9333 | |
Cubic SVM | Grapeseed oil | 1 | 1 | 1 | 1 | 1 |
Peanut oil | 0.855 | 0.4 | 0.5797 | 0.7403 | 0.7033 | |
Olive oil | 0.7 | 0.71 | 0.542 | 0.8284 | 0.7033 | |
Fine Tree | Grapeseed oil | 1 | 1 | 1 | 1 | 1 |
Peanut oil | 0.57 | 0.86 | 0.5 | 0.8906 | 0.6667 | |
Olive oil | 0.93 | 0.14 | 0.5 | 0.6838 | 0.6667 | |
KNN fine | Grapeseed oil | 1 | 0.89 | 1 | 0.9479 | 0.9633 |
Peanut oil | 0.86 | 0.54 | 0.6585 | 0.789 | 0.7533 | |
Olive oil | 0.715 | 0.72 | 0.5581 | 0.8363 | 0.7167 |
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Huang, Y.; Doh, I.-J.; Bae, E. Design and Validation of a Portable Machine Learning-Based Electronic Nose. Sensors 2021, 21, 3923. https://doi.org/10.3390/s21113923
Huang Y, Doh I-J, Bae E. Design and Validation of a Portable Machine Learning-Based Electronic Nose. Sensors. 2021; 21(11):3923. https://doi.org/10.3390/s21113923
Chicago/Turabian StyleHuang, Yixu, Iyll-Joon Doh, and Euiwon Bae. 2021. "Design and Validation of a Portable Machine Learning-Based Electronic Nose" Sensors 21, no. 11: 3923. https://doi.org/10.3390/s21113923