A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method
Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recog...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/4886 |
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| author | Kun Ruan Shun Zhao Xueqin Jiang Yixuan Li Jianbo Fei Dinghua Ou Qiang Tang Zhiwei Lu Tao Liu Jianguo Xia |
| author_facet | Kun Ruan Shun Zhao Xueqin Jiang Yixuan Li Jianbo Fei Dinghua Ou Qiang Tang Zhiwei Lu Tao Liu Jianguo Xia |
| author_sort | Kun Ruan |
| collection | DOAJ |
| description | Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10<sup>−4</sup> (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis. |
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| language | English |
| last_indexed | 2024-03-10T03:24:32Z |
| publishDate | 2022-05-01 |
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| spelling | doaj.art-8c6364e9c04c4d46aa22ce149058a1b52023-11-23T09:54:38ZengMDPI AGApplied Sciences2076-34172022-05-011210488610.3390/app12104886A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis MethodKun Ruan0Shun Zhao1Xueqin Jiang2Yixuan Li3Jianbo Fei4Dinghua Ou5Qiang Tang6Zhiwei Lu7Tao Liu8Jianguo Xia9College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Environmental Sciences, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Resources, Sichuan Agricultural University, Chengdu 611130, ChinaCollege of Resources, Sichuan Agricultural University, Chengdu 611130, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Science, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Resources, Sichuan Agricultural University, Chengdu 611130, ChinaThree-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10<sup>−4</sup> (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.https://www.mdpi.com/2076-3417/12/10/48863D fluorescence3D-EEMsdeep learningconvolutional neural networkPARAFAC analysisVGG neural network |
| spellingShingle | Kun Ruan Shun Zhao Xueqin Jiang Yixuan Li Jianbo Fei Dinghua Ou Qiang Tang Zhiwei Lu Tao Liu Jianguo Xia A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method Applied Sciences 3D fluorescence 3D-EEMs deep learning convolutional neural network PARAFAC analysis VGG neural network |
| title | A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method |
| title_full | A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method |
| title_fullStr | A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method |
| title_full_unstemmed | A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method |
| title_short | A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method |
| title_sort | 3d fluorescence classification and component prediction method based on vgg convolutional neural network and parafac analysis method |
| topic | 3D fluorescence 3D-EEMs deep learning convolutional neural network PARAFAC analysis VGG neural network |
| url | https://www.mdpi.com/2076-3417/12/10/4886 |
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