Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Detection of Aflatoxin B1 Content
2.3. Spectral Acquisition
2.4. Data Analyses Methods
2.4.1. Spectral Augmentation
2.4.2. Markov Transition Field
2.4.3. Convolution Neural Network
2.5. Figures of Merit
3. Results
3.1. Division of Calibration Set and Prediction Set
3.2. The Training Results of CNN Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sample Sets | Sample Number | g·kg−1 | Minimum/g·kg−1 | g·kg−1 | g·kg−1 |
---|---|---|---|---|---|
Calibration set | 450 | 63.0195 | 2.6214 | 24.4588 | 20.4806 |
Prediction set | 150 | 61.9111 | 2.7252 | 24.4746 | 20.3720 |
Models | Layers | Size | Number | Activation | Output Shape | Parameters |
---|---|---|---|---|---|---|
1D-CNN | Input | (215,1) | - | - | - | - |
Conv1 | 3×1 | 32 | Relu | (213,32) | 128 | |
Max pooling | 3×1 | - | - | (71,32) | 0 | |
Conv2 | 3×1 | 64 | Relu | (69,64) | 6208 | |
Max pooling | 3×1 | - | - | (23,64) | 0 | |
Conv3 | 3×1 | 64 | Relu | (21,64) | 12,352 | |
Max pooling | 3×1 | - | - | (7,64) | 0 | |
Conv4 | 3×1 | 64 | Relu | (5,64) | 12,352 | |
Max pooling | 2×1 | - | - | (2,64) | 0 | |
Flatten | - | - | - | 128 | 0 | |
Dense | 1 | - | Linear | 1 | 129 | |
2D-MTF-CNN | ||||||
Input | (215,215,1) | |||||
Conv1 | 11×11 | 6 | Relu | (206,206,6) | 732 | |
Max pooling | 2×2 | - | - | (103,103,6) | 0 | |
Conv2 | 11×11 | 32 | Relu | (93,93,32) | 23,264 | |
Max pooling | 3×3 | - | - | (31,31,32) | 0 | |
Flatten | - | - | - | 30,752 | 0 | |
Dense1 | 10 | - | Relu | 10 | 307,530 | |
Dense2 | 10 | - | Relu | 10 | 110 | |
Dense3 | 1 | - | Relu | 1 | 11 |
Models | Input Shape | kg−1 | kg−1 | RPD | ||
---|---|---|---|---|---|---|
1D-CNN | (215,1) | 3.7397 | 0.9637 | 5.5360 | 0.9227 | 3.8101 |
2D-MTF-CNN | (215,215,1) | 0.6799 | 0.9989 | 1.3591 | 0.9955 | 14.9386 |
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Wang, B.; Deng, J.; Jiang, H. Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize. Foods 2022, 11, 2210. https://doi.org/10.3390/foods11152210
Wang B, Deng J, Jiang H. Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize. Foods. 2022; 11(15):2210. https://doi.org/10.3390/foods11152210
Chicago/Turabian StyleWang, Bo, Jihong Deng, and Hui Jiang. 2022. "Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize" Foods 11, no. 15: 2210. https://doi.org/10.3390/foods11152210
APA StyleWang, B., Deng, J., & Jiang, H. (2022). Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize. Foods, 11(15), 2210. https://doi.org/10.3390/foods11152210