The application of an artificial neural network in the identification of medicinal rhubarbs by near-infrared spectroscopy

2002 ◽  
Vol 13 (5) ◽  
pp. 272-276 ◽  
Author(s):  
Lan Xiang ◽  
Guoqiang Fan ◽  
Junhui Li ◽  
Hui Kang ◽  
Yanlu Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Artificial Neural

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

2001 ◽  
Vol 27 (7) ◽  
pp. 623-631 ◽  
Author(s):  
Yixin Chen ◽  
Shilpa S. Thosar ◽  
Reba A. Forbess ◽  
Mark S. Kemper ◽  
Ronald L. Rubinovitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Drug Content ◽  
Artificial Neural

Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network

2021 ◽  
pp. 004051752110075
Author(s):  
Wenxia Li ◽  
Zihan Wei ◽  
Zhengdong Liu ◽  
Yujun Du ◽  
Jiahui Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Infrared Spectrum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Back Propagation ◽  
Bp Network ◽  
Artificial Neural ◽  
Near Infrared Spectrum

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises.

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