A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Preparation
2.2. E-nose Analysis and Data Acquisition
2.3. Adaptive Moment Estimation Algorithm
2.4. Synthetic Minority Oversampling Technique
2.5. Back-Propagation Neural Network
2.6. Extreme Learning Machine
2.7. Radial Basis Function Neural Network
3. Proposed Method
Algorithm 1. BP-ELMNN |
Input: training data. |
Output: predicted category. |
Begin |
Step 1: train the BPNN part on the training data using the Adam algorithm. |
Step 2: randomly select the input weights and thresholds of the Dense 3 layer. |
Step 3: calculate the input weight of the output layer using Equation (7). |
Step 4: test the BP-ELMNN model on the test data. |
Step 5: output the classification results. |
End |
4. Result and Discussion
4.1. Data Analysis and Sample Supplementation
4.2. Principal Component Analysis
4.3. Comparison of the Classification Results of the Four Methods
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Class | Standard | * | ||
---|---|---|---|---|
Appearance | Core | Flesh | ||
0 | Good | Good | Good | - |
1 | Good | Light brown | Good | No |
2 | Good | Brown | Good | No |
3 | Good | Dark brown | Good | No |
4 | Brown | Dark brown | Dark brown | Yes |
5 | Dark brown | Dark brown | Dark brown | Yes |
Number | Sensor | Substance Sensitivity |
---|---|---|
MOS1 | W1C | Aroma constituent |
MOS2 | W5S | Sensitive to nitride oxides |
MOS3 | W3C | Ammonia, aroma constituent |
MOS4 | W6S | Hydrogen |
MOS5 | W5C | Alkane, aroma constituent |
MOS6 | W1S | Sensitive to methane |
MOS7 | W1W | Sensitive to sulfide |
MOS8 | W2S | Sensitive to alcohol |
MOS9 | W2W | Aroma constituent, organic sulfur compounds |
MOS10 | W3S | Sensitive to alkane |
Training Samples. | Test Samples | ||
---|---|---|---|
Class | Number | Class | Number |
0 | 9 | 0 | 3 |
1 | 21 | 1 | 8 |
2 | 42 | 2 | 15 |
3 | 77 | 3 | 38 |
4 | 49 | 4 | 20 |
5 | 42 | 5 | 18 |
Class | TR | TE | ||||
---|---|---|---|---|---|---|
The Original Number of Samples | Added Number of Samples | New Number of Samples | The Original Number of Samples | Added Number of Samples | New Number of Samples | |
0 | 540 | 4080 | 4620 | 180 | 2100 | 2280 |
1 | 1260 | 3360 | 4620 | 480 | 1800 | 2280 |
2 | 2520 | 2100 | 4620 | 900 | 1380 | 2280 |
3 | 4620 | 0 | 4620 | 2280 | 0 | 2280 |
4 | 2940 | 1680 | 4620 | 1200 | 1080 | 2280 |
5 | 2520 | 2100 | 4620 | 1080 | 1200 | 2280 |
Methods | Accuracy | Macro-Precision | Macro-Recall | Macro-F1 Score |
---|---|---|---|---|
BPNN | 0.7623 | 0.6732 | 0.7623 | 0.7150 |
RBFNN | 0.3504 | 0.2347 | 0.3504 | 0.2811 |
ELM | 0.9190 | 0.9272 | 0.9190 | 0.9231 |
BP-ELMNN | 0.9683 | 0.9688 | 0.9683 | 0.9685 |
Methods | Accuracy | Macro-Precision | Macro-Recall | Macro-F1score |
---|---|---|---|---|
BPNN | 0.7399 | 0.6398 | 0.7055 | 0.6711 |
RBFNN | 0.3493 | 0.1255 | 0.2153 | 0.1586 |
ELM | 0.8809 | 0.8653 | 0.8677 | 0.8665 |
BP-ELMNN | 0.9285 | 0.9188 | 0.9017 | 0.9089 |
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Wei, H.; Gu, Y. A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose. Sensors 2020, 20, 4499. https://doi.org/10.3390/s20164499
Wei H, Gu Y. A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose. Sensors. 2020; 20(16):4499. https://doi.org/10.3390/s20164499
Chicago/Turabian StyleWei, Hao, and Yu Gu. 2020. "A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose" Sensors 20, no. 16: 4499. https://doi.org/10.3390/s20164499
APA StyleWei, H., & Gu, Y. (2020). A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose. Sensors, 20(16), 4499. https://doi.org/10.3390/s20164499