Full
Outline
To achieve the inter and intraspecific identification of Caohonghua（Carthamus tinctorius L.）from Asteraceae，Xihonghua（Crocus sativus L.）from Iridaceae（originating from Iran）and Zanghonghua（Crocus sativus L.）（originating from Xizang）by using spectroscopic technologies coupled with machine learning methods.
Raman spectroscopy（RS）and terahertz（THz）spectral data was used to identify and characterize Xihonghua（Crocus sativus L.）（origin Iran），Caohonghua（Carthamus tinctorius L.）（origin Gansu）and Zanghonghua（Crocus sativus L.）（origin Xizang）by using machine learning methods including Principal Component Analysis（PCA），Support-Vector Machine（SVM），Random Forest（RF）and deep learning methods including Multilayer Perceptron（MLP），Convolutional Neural Network（CNN），Long Short Term Memory（LSTM）and Genetic Algorithm-Back Propagation（GA-BP）to analyze the THz and RSspectra of three types of samples.The rapid classification and recognition of Chinese herbal medicine with small sample size was realized.
（1）The classification accuracies of the SVM method on the RS data of the three samples before and after dimensionality reduction by PCA were 83.33% and 94.44%，respectively.The MLP classified the THz data before and after dimensionality reduction by PCA with accuracies of 94.00% and 96.79%，respectively.The results indicated that the classification accuracy was further improved by PCA，and THz spectra for deep learning had a higher accuracy than RSspectra.（2）The classification accuracy of neural networks such as MLP，CNN，RBF，GA-BP and LSTM on THz spectral data was higher than that on RSspectra，among which the classification accuracy of LSTM and RF on THz raw data was as high as 100%.Also，these models had high classification accuracy，high speed and good stability.
The combination of spectroscopic and machine learning methods enables inter and intraspecific identification of Xihonghua（Crocus sativus L.），Caohonghua（Carthamus tinctorius L.）and Zanghonghua（Crocus sativus L.），with high accuracy of THz spectroscopic identification.
28
12
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
