Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Material
2.2. Spectra Acquisition
2.3. Spectral Pretreatment
2.4. Data Fusion
2.5. Modeling of Origin Classification
3. Results and Discussion
3.1. Spectral Analysis
3.2. Spectral Preprocessing
3.3. Extraction of Characteristics
3.4. Spectral Data Fusion
3.5. Model Evaluation Metrics
3.6. Confusion Matrix
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Preprocessing | Ultraviolet Spectrum | Mid-Infrared Spectrum | ||
---|---|---|---|---|---|
Train (%) | Test (%) | Train (%) | Test (%) | ||
SVC | None | 100 | 87.31 | 100 | 40.00 |
SG | 100 | 100 | 100 | 40.00 | |
MSC | 100 | 92.41 | 100 | 19.31 | |
SNV | 100 | 100 | 100 | 94 | |
RF | None | 100 | 89.44 | 100 | 94.67 |
SG | 100 | 92.46 | 100 | 95.44 | |
MSC | 100 | 94.44 | 100 | 97.59 | |
SNV | 100 | 91.42 | 100 | 96.00 | |
ANN | None | 100 | 93.45 | 26.84 | 18.66 |
SG | 100 | 100 | 26.84 | 18.66 | |
MSC | 100 | 97.32 | 26.84 | 18.66 | |
SNV | 100 | 97.65 | 92.44 | 84.00 | |
XGboost | None | 100 | 92.67 | 100 | 90.67 |
SG | 96.32 | 93.29 | 100 | 85.33 | |
MSC | 100 | 95.34 | 100 | 91.43 | |
SNV | 100 | 93.52 | 100 | 90.67 |
Model | Preprocessing | Ultraviolet Spectrum | Mid-Infrared Spectrum | ||
---|---|---|---|---|---|
Train (%) | Test (%) | Train (%) | Test (%) | ||
SVC | None | 100 | 87.31 | 100 | 40.00 |
SG-GA | 100 | 96.05 | 100 | 26.66 | |
MSC-GA | 100 | 96.68 | 100 | 28.45 | |
SNV-GA | 100 | 98.54 | 100 | 97.33 | |
RF | None | 100 | 89.44 | 100 | 94.67 |
SG-GA | 100 | 98.56 | 100 | 97.34 | |
MSC-GA | 100 | 93.47 | 100 | 94.67 | |
SNV-GA | 100 | 99.16 | 100 | 97.34 | |
ANN | None | 100 | 93.45 | 26.84 | 18.66 |
SG-GA | 100 | 96.54 | 26.74 | 18.43 | |
MSC-GA | 97.62 | 95.65 | 26.74 | 18.66 | |
SNV-GA | 100 | 99.73 | 87.28 | 80.00 | |
XGBoost | None | 100 | 92.67 | 100 | 90.67 |
SG-GA | 100 | 97.92 | 100 | 90.56 | |
MSC-GA | 100 | 96.38 | 100 | 85.34 | |
SNV-GA | 100 | 97.41 | 100 | 89.33 |
Model | Preprocessing | Fusion Spectrum | |
---|---|---|---|
Train (%) | Test (%) | ||
SVC | SNV-GA | 100 | 100 |
RF | SNV-GA | 100 | 100 |
ANN | SNV-GA | 100 | 100 |
XGBoost | SNV-GA | 100 | 100 |
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Han, H.; Sha, R.; Dai, J.; Wang, Z.; Mao, J.; Cai, M. Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information. Foods 2024, 13, 1016. https://doi.org/10.3390/foods13071016
Han H, Sha R, Dai J, Wang Z, Mao J, Cai M. Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information. Foods. 2024; 13(7):1016. https://doi.org/10.3390/foods13071016
Chicago/Turabian StyleHan, Hao, Ruyi Sha, Jing Dai, Zhenzhen Wang, Jianwei Mao, and Min Cai. 2024. "Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information" Foods 13, no. 7: 1016. https://doi.org/10.3390/foods13071016
APA StyleHan, H., Sha, R., Dai, J., Wang, Z., Mao, J., & Cai, M. (2024). Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information. Foods, 13(7), 1016. https://doi.org/10.3390/foods13071016