Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0630001(2023)
Application of Improved Auto-Encoding Network Feature Extraction Method in Near Infrared Spectral Quantitative Analysis
[1] Qin Y H, Ding X Q, Gong H L. High dimensional feature selection in near infrared spectroscopy classification[J]. Infrared and Laser Engineering, 42, 1355-1359(2013).
[2] Hu J, Feng Y Z, Wang Y J et al. Detection of umami substances and umami intensity based on near-infrared spectroscopy[J]. Acta Optica Sinica, 42, 0130002(2022).
[3] Chen Z H, Luan X L, Liu F. Near-infrared fault detection based on stacked regularized auto-encoder network[J]. Chemometrics and Intelligent Laboratory Systems, 204, 104101(2020).
[4] Mees C, Souard F, Delporte C et al. Identification of coffee leaves using FT-NIR spectroscopy and SIMCA[J]. Talanta, 177, 4-11(2018).
[5] Luo W, Du Y Z, Zhang H L. Discrimination of varieties of cabbage with near infrared spectra based on principal component analysis and successive projections algorithm[J]. Spectroscopy and Spectral Analysis, 36, 3536-3541(2016).
[6] Liu J, Yang Z, Liu Y et al. Hyperspectral remote sensing images deep feature extraction based on mixed feature and convolutional neural networks[J]. Remote Sensing, 13, 2599-2606(2021).
[7] He Y, Li X L. Discriminating varieties of waxberry using near infrared spectra[J]. Journal of Infrared and Millimeter Waves, 25, 192-194, 212(2006).
[8] Gao Q X, Xie D Y, Xu H et al. Supervised feature extraction based on information fusion of local structure and diversity information[J]. Acta Automatica Sinica, 36, 1107-1114(2010).
[9] Lu M Y, Yang K, Song P F et al. The study of classification modeling method for near infrared spectroscopy of tobacco leaves based on convolution neural network[J]. Spectroscopy and Spectral Analysis, 38, 3724-3728(2018).
[10] Li Q Q, Zeng J Q, Lin L et al. Mid-infrared spectra feature extraction and visualization by convolutional neural network for sugar adulteration identification of honey and real-world application[J]. LWT, 140, 110856(2021).
[11] Cui G X, Li D K. Overview on deep learning based on automatic encoder algorithms[J]. Computer Systems & Applications, 27, 47-51(2018).
[12] Ji Y, Gong L R, Fu S et al. Automatic phase recognition method based on convolutional neural network[J]. Laser & Optoelectronics Progress, 59, 0617026(2022).
[13] Zhang X N, Xiang Z, Tang C H. A deep convolutional auto-encoding neural network and its application in bearing fault diagnosis[J]. Journal of Xi’an Jiaotong University, 52, 1-8, 59(2018).
[14] Luo R Z, Wang R J, Zhang K et al. Image denoising method of residual convolution auto-encoder network[J]. Computer Simulation, 38, 455-461(2021).
[15] Lei Y, Yan X J. Denoising of the near infrared spectral based on deep denoising autoencoder neural network[J]. Techniques of Automation and Applications, 40, 15-18(2021).
[16] Zhang S H. Bearing condition dynamic monitoring based on multi-way sparse autocoder[J]. Journal of Vibration and Shock, 35, 125-131(2016).
[17] Cheng Z, Zhao N J, Yin G F et al. Identification method of planktonic algae community based on multi-task convolutional neural network[J]. Acta Optica Sinica, 42, 0530002(2022).
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Zhiyong Luo, Yuhua Qin, Shijie Wang, Susu He, Haitao Zhang. Application of Improved Auto-Encoding Network Feature Extraction Method in Near Infrared Spectral Quantitative Analysis[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0630001
Category: Spectroscopy
Received: Feb. 15, 2022
Accepted: Mar. 29, 2022
Published Online: Mar. 7, 2023
The Author Email: Qin Yuhua (yuu71@163.com)