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Tobacco adulteration recognition study by hyperspectral data processing under machine learning

Hongliang Zhang

Hongliang Zhang

Hebei Baisha Tobacco Co., Ltd., Baoding Cigarette FactoryWangdu, China
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Sciendo|Google Scholar
Zhang, Hongliang
, 
Mei Chen

Mei Chen

Hebei Baisha Tobacco Co., Ltd., Baoding Cigarette FactoryWangdu, China
Suche nach diesem Autor auf
Sciendo|Google Scholar
Chen, Mei
, 
Xiangrong Chen

Xiangrong Chen

Hebei Baisha Tobacco Co., Ltd., Baoding Cigarette FactoryWangdu, China
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Sciendo|Google Scholar
Chen, Xiangrong
, 
Na Ma

Na Ma

Hebei Baisha Tobacco Co., Ltd., Baoding Cigarette FactoryWangdu, China
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Ma, Na
 und 
Xiang Wei

Xiang Wei

Hebei Baisha Tobacco Co., Ltd., Baoding Cigarette FactoryWangdu, China
Suche nach diesem Autor auf
Sciendo|Google Scholar
Wei, Xiang
  
17. März 2025
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Online veröffentlicht: 17. März 2025
Eingereicht: 13. Nov. 2024
Akzeptiert: 15. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0201
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© 2025 Hongliang Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Improved CNN recognition network structure
Improved CNN recognition network structure

Figure 2.

Visual results of tobacco pca
Visual results of tobacco pca

Figure 3.

The average spectral curve of three kinds of tobacco
The average spectral curve of three kinds of tobacco

Figure 4.

Different pretreatment method classification effect
Different pretreatment method classification effect

CNN model performance of different activation functions

Activation function Correct rate Average precision Average recall Average F1
correction set verification set test set
ReLU 0.977 0.963 0.925 0.96 0.936 0.937
LeakyReLU 0.989 0.997 0.913 0.848 0.844 0.845
Sigmod 0.982 0.971 0.919 0.919 0.912 0.936
Tanh 0.981 0.962 0.878 0.928 0.921 0.923

Modeling time

Methods Modeling Time(s)
SVM RF KNN This model
RAW 0.06 0.04 0.012 0.0003
SG 0.06 0.04 0.012 0.0003
GWS 0.062 0.043 0.011 0.00032
PCA 0.057 0.038 0.009 0.00021

Identification of tobacco origin

Num Model Accuracy Precision Recall F1
1 KNN 89.78% 89.58% 86.45% 87.41%
2 RF 92.78% 93.12% 92.56% 92.88^%
3 Improved CNN model 96.45% 97.42% 97.62% 97.88%
4 Artificial 81.23% 82.31% 84.51% 83.64%

Contrast between different convolution nuclei

Combination of number of convolution kernels Correct rate Average precision Average recall Average F1
correction set verification set test set
(128,128,128) 0.983 0.98 0.855 0.856 0.855 0.851
(128,128,64) 0.965 0.946 0.895 0.908 0.893 0.899
(128,64,64) 0.96 0.956 0.876 0.88 0.876 0.872
(128,64,32) 0.938 0.925 0.837 0.854 0.838 0.835
(64,64,32) 0.931 0.929 0.877 0.881 0.873 0.873
(64,32,32) 0.93 0.918 0.876 0.876 0.869 0.875
(64,32,16) 0.913 0.885 0.831 0.853 0.834 0.833
(32,32,16) 0.916 0.891 0.855 0.857 0.853 0.853

Contrast of different convolution nuclei

Convolution core size Correct rate Average precision Average recall Average F1
correction set verification set test set
(5,5,5) 0.975 0.966 0.874 0.871 0.874 0.873
(5,5,3) 0.965 0.964 0.905 0.904 0.904 0.904
(3,3,3) 0.974 0.968 0.921 0.942 0.936 0.935
(3,5,3) 0.954 0.928 0.832 0.856 0.835 0.834
(3,5,5) 0.962 0.971 0.874 0.882 0.871 0.872
Sprache:
Englisch
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Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere
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