文献类型: 外文期刊
作者: Dong, Jian-E 1 ; Zhang, Song 2 ; Li, Tao 3 ; Wang, Yuan-Zhong 4 ;
作者机构: 1.Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China
2.Linshu Cty Market Supervis Adm Shandong Prov, Linyi 276700, Shandong, Peoples R China
3.Yuxi Normal Univ, Coll Chem Biol & Environm, Yuxi 653100, Yunnan, Peoples R China
4.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China
关键词: Bolete; Species; Two-dimensional correlation spectroscopy (2DCOS); Convolutional neural networks(CNN); Residual convolutional neural network(ResNet); Blockchain
期刊名称:MICROCHEMICAL JOURNAL ( 影响因子:4.8; 五年影响因子:4.5 )
ISSN: 0026-265X
年卷期: 2022 年 177 卷
页码:
收录情况: SCI
摘要: Bolete mushrooms are well received by consumers for their rich nutrition and high medicinal value. However, the nutritional value and medicinal value of different species of bolete mushrooms are significantly different. Therefore, it is necessary to identify and trace the species of bolete mushrooms. In this study, Support Vector Machine (SVM) model and four deep learning models with different data sets were established to identify the species of boletes. By comparison, the accuracy of the train set, test set and external verification can reach 100% about the synchronous two-dimensional correlation spectroscopy (2DCOS) model, and the loss value of this model is 0.0257 which is close to zero. Therefore, the synchronous 2DCOS model has the best accuracy and generalization ability. Then, the results of species identification were uploaded to the blockchain platform that we build. Users can query and display the information after identity authentication, so as to realize the traceability of bolete mushrooms. The results show that our method is feasible. The traceability technology based on deep learning and blockchain has been used in the field of microbiology in this research, and it can be extended to other fields.
- 相关文献
作者其他论文 更多>>
-
Applications of chemical fingerprints and machine learning in plant ecology: Recent progress and future perspectives
作者:Zhong, Chen;Wang, Yuan-Zhong;Zhong, Chen;Li, Li
关键词:Chemical fingerprints; Chemometrics; Plant ecology; Analytical techniques; Machine learning algorithms
-
Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR
作者:He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词:
-
Suitable habitat prediction and identification of origin of Lanxangia tsao-ko
作者:He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词:Medicinal plant; FT-NIR spectroscopy; Machine learning; Suitable habitats; Origin identification
-
Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species
作者:Li, Jie-Qing;Liu, Hong-Gao;Wang, Yuan-Zhong;Liu, Hong-Gao
关键词:boletes species; 2DCOS images; 3DCOS images; Alexnet; Resnet; large-screen visualization
-
Contributions of ectomycorrhizal fungi in a reclaimed poplar forest (Populus yunnanensis) in an abandoned metal mine tailings pond, southwest China
作者:Xiao, Yinrun;Liu, Conglong;Hu, Na;Wang, Bowen;Zhao, Zhiwei;Li, Tao;Xiao, Yinrun;Liu, Conglong;Hu, Na;Wang, Bowen;Li, Tao;Zheng, Kuanyu
关键词:Bovista limosa; Physiological responses; Heavy metal tolerance; Phytoremediation; Compartmentalization
-
Traditional uses, chemical compositions and pharmacological activities of Dendrobium: A review
作者:Li, Pei-Yuan;Wang, Yuan-Zhong;Li, Pei-Yuan;Li, Li
关键词:Dendrobium; Traditional use; Chemical composition; Pharmacological activity
-
A rapid identification based on FT-NIR spectroscopies and machine learning for drying temperatures of Amomum tsao-ko
作者:He, Gang;Lin, Qi;Yang, Shao-Bing;Wang, Yuan-Zhong;He, Gang;Lin, Qi
关键词:Identification research; FT-NIR spectroscopies; Machine learning; Chemometrics; Drying temperatures; Amomum tsao-ko