Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
Abstract
:1. Introduction
- (1)
- An e-nose system was designed for apple quality grading, with the merits of convenient sample treatment, low cost, and a good recognition effect.
- (2)
- Two different nasal cavity structures of the e-nose were designed, and the optimal one was selected for apple grading on the basis of computational fluid dynamics (CFD).
- (3)
- The features of the 18-dimensional gas data were extracted from the data collected by the designed e-nose, and then the KNN-SVM classifier was proposed in this study to achieve accurate and non-destructive apple grading.
2. Materials and Methods
2.1. Materials
2.2. Design of the Device
2.2.1. Overall Design of the Device
2.2.2. Nasal Cavity Structure
Gas Sensor Array
Steady Flow Plate
Air Inlet Dimensions
2.2.3. Experimental Verification by CFD Simulation
Construction of the 3D Electronic Nose Model
Governing Equations
Meshing
Setting Boundary Conditions
2.3. Data Analysis
2.3.1. Data Collection and Feature Extraction
2.3.2. Principal Component Analysis (PCA)
2.3.3. Linear Discriminant Analysis (LDA)
2.4. KNN-SVM Classifier
2.5. Classic Classifiers
3. Results and Discussion
3.1. Simulation Results of the Flow Fields in Two Nasal Cavity Structures
3.2. Results of Apple Quality Classification
3.2.1. Results of PCA and LDA
3.2.2. Rapid Classification of Apple
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Level | External Quality | Internal Quality |
---|---|---|
L1 | No stab wounds, broken skin, crushed wounds, disease wounds, insect wounds, rot, or shrinkage on the surface of apples; smooth and rosy surface. | No rot, shrinkage, or dryness inside the apples, which can be eaten normally. |
L2 | Slight skin damage, stab wounds, or frostbite appear on the surface of the apple, and there are a few black spots. | No rot, shrinkage, or dryness inside apples. |
L3 | The surface of the apple is obviously damaged, with pests and disease, and there is decay or shrinkage. | Rot, shrinkage, or dryness inside the apples, which cannot be eaten normally. |
Sensor | Target Gas | Manufacturer |
---|---|---|
MQ-9 | CO, CH4 | Hanwei Electronics Co., Ltd., Zhengzhou, China |
MQ-3 | alcohols | Hanwei Electronics Co., Ltd., Zhengzhou, China |
MQ-6 | C3H8, C4H10 | Hanwei Electronics Co., Ltd., Zhengzhou, China |
MQ-8 | H2 | Hanwei Electronics Co., Ltd., Zhengzhou, China |
MQ-2 | liquefied gas | Hanwei Electronics Co., Ltd., Zhengzhou, China |
MQ-135 | NH3, benzene vapors | Hanwei Electronics Co., Ltd., Zhengzhou, China |
Parameters | Values |
---|---|
Simulated state | Steady state |
model | Laminar |
Air density (kg/m3) | 1.225 |
Dynamic Viscosity (Pa·s) | 1.83 × 10−5 |
Inlet | Velocity—inlet |
Outlet | Pressure—outlet |
Component | Feature | Variance Contribution Rate (%) | Cumulative Contribution Rate (%) | |
---|---|---|---|---|
PCA | PC1 | 1.048 | 69.52 | 69.52 |
PC2 | 0.427 | 28.34 | 97.87 | |
PC3 | 0.015 | 1.00 | 98.87 | |
PC4 | 0.012 | 0.83 | 99.7 | |
LDA | LD1 | 3.046 | 95.68 | 95.68 |
LD2 | 0.137 | 4.28 | 99.96 |
Feature Vector | Training Time (Unit: t/s) | Recognition Rate (%) | |||
---|---|---|---|---|---|
L1 | L2 | L3 | Average Recognition Rate | ||
The original 18-dimensional feature vector | 3.653 | 93.35 | 92.45 | 94.19 | 93.33 |
PCA 2D feature vector | 0.618 | 96.66 | 95.45 | 97.24 | 96.45 |
LDA 2D feature vector | 0.614 | 98.50 | 96.66 | 98.18 | 97.78 |
Method | Training Time (Unit: s) | Recognition Rate (%) |
---|---|---|
KNN | 0.307 | 93.30 |
SVM | 0.858 | 83.00 |
Random Forest | 0.86 | 93.00 |
Decision Tree | 0.35 | 91.00 |
The proposed algorithm of this study | 0.614 | 97.78 |
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Zou, X.; Wang, C.; Luo, M.; Ren, Q.; Liu, Y.; Zhang, S.; Bai, Y.; Meng, J.; Zhang, W.; Su, S.W. Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. Sensors 2022, 22, 2997. https://doi.org/10.3390/s22082997
Zou X, Wang C, Luo M, Ren Q, Liu Y, Zhang S, Bai Y, Meng J, Zhang W, Su SW. Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. Sensors. 2022; 22(8):2997. https://doi.org/10.3390/s22082997
Chicago/Turabian StyleZou, Xiuguo, Chenyang Wang, Manman Luo, Qiaomu Ren, Yingying Liu, Shikai Zhang, Yungang Bai, Jiawei Meng, Wentian Zhang, and Steven W. Su. 2022. "Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine" Sensors 22, no. 8: 2997. https://doi.org/10.3390/s22082997
APA StyleZou, X., Wang, C., Luo, M., Ren, Q., Liu, Y., Zhang, S., Bai, Y., Meng, J., Zhang, W., & Su, S. W. (2022). Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. Sensors, 22(8), 2997. https://doi.org/10.3390/s22082997