Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy

Geoderma ◽  
2010 ◽  
Vol 158 (1-2) ◽  
pp. 23-31 ◽  
Author(s):  
A.M. Mouazen ◽  
B. Kuang ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Soil Properties ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Network Analyses ◽  
Accuracy Of Measurement

Fast and Non-Destructiveness Discrimination of Varieties of Fragrant Mushroom Using Near Infrared Spectroscopy

2011 ◽  
Vol 460-461 ◽  
pp. 159-164
Author(s):  
Peng Cheng Nie ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Di Wu ◽  
Yong He
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
High Speed ◽  
Near Infrared ◽  
Recognition Rate ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Hidden Layer

Visible/near-infrared spectroscopy (NIRS) is the millimeter wave ,It is the high speed and non-destructiveness method, high precision and reliable detection data, is a rapid and non-destructiveness method for discrimination varieties of Fragrant mushrooms by means of VIS/NIR spectroscopy was developed in this study. The relationship between the reflectance spectra and Fragrant mushrooms varieties was established. The spectral data was compressed by the wavelet transform (WT). The features from WT can be visualized in principal component (PC) space, appeared to provide a reasonable clustering of the varieties of Fragrant mushrooms. The fivet principal components computed by PCA had been applied as inputs to a back propagation neural network(BP) with one hidden layer. The 220 samples of four varieties were selected randomly to build BP model. This model was used to predict the varieties of 40 unknown samples. The predict recognition rate has achieved 99.5%. This model was reliable and practicable.

Rapid Discrimination of Fermented Cordyceps Mycelium Powder by Near-Infrared Spectroscopy (NIRS)

2013 ◽  
Vol 302 ◽  
pp. 189-193 ◽  
Author(s):  
Ning Xu ◽  
Wei Qiang Luo ◽  
Hai Qing Yang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Clustering Analysis ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Recognition Method ◽  
Hyper Spectral ◽  
Rapid Discrimination

The potential of near-infrared spectroscopy (NIRS) was investigated for its ability to rapidly discriminate the various brands of fermented Cordyceps mycelium powder. Relationship between mycelium powder varieties and the absorbance spectra was well established with the spectra region of 12500-4000 cm-1. Spectra preprocessing was performed using 1st derivative. Principal component analysis (PCA) was adopted for the clustering analysis and re-expressing of the hyper spectral data, and then, the obtained principal components (PCs) were used as the input of back-propagation artificial neural network (BPANN) to build PCA-BPANN model for the variety discrimination. The unknown samples in prediction set were precisely identified with the correlation coefficient R of 0.9959 and root-mean-square error of prediction (RMSEP) of 0.1007, which suggests that the NIR spectroscopy, if coupled with appropriate pattern recognition method, is very promising for rapid and nondestructive discrimination of fermented Cordyceps mycelium powder.

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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