Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example
文献类型: 外文期刊
第一作者: Li, Peiyuan
作者: Li, Peiyuan;Li, Li;Li, Peiyuan;Wang, Yuanzhong;Shen, Tao
作者机构:
关键词: Medicinal plant; Dendrobium officinale; ATR-FTIR; ResNet; Harvesting period; Anticipate
期刊名称:PLANT METHODS ( 影响因子:5.1; 五年影响因子:6.1 )
ISSN:
年卷期: 2024 年 20 卷 1 期
页码:
收录情况: SCI
摘要: Background Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period. Results The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R-2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R-2 = 0.99, RPD = 9.55. Conclusions These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.
分类号:
- 相关文献
作者其他论文 更多>>
-
Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning
作者:Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词:Gastrodia elata f. glauca; Fourier transform infrared spectroscopy; Deep learning; Data driven version of soft independent; modeling of class analogy
-
ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy
作者:Li, Xiaokun;Zeng, Pan;Wu, Xunxun;Yang, Xintong;Liu, Peizhong;Diao, Yong;Lin, Jingcang;Liu, Peizhong;Wang, Yuanzhong
关键词:Antioxidant activity; FT-IR; Gentian; Deep Learning; Chemometrics
-
ZmPTOX1, a plastid terminal oxidase, contributes to redox homeostasis during seed development and germination
作者:Peng, Yixuan;Liang, Zhi;Cai, Minghao;Wang, Jie;Chen, Quanquan;Du, Xuemei;Gu, Riliang;Wang, Jianhua;Li, Li;Li, Delin;Wang, Guoying;Schnable, Patrick S.;Li, Li
关键词:ZmPTOX1; ZmFNR1; plastid; carotenoid; seed development; germination; redox homeostasis
-
Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves
作者:Zhang, Yanying;Zhang, Yanying;Zhu, Xinyan;Wang, Yuanzhong
关键词:Machine learning; Eucommia ulmoides leaves; Geographical traceability; Content prediction; Quality evaluation
-
A genome-wide association study uncovers a ZmRap2.7-ZCN9/ZCN10 module to regulate ABA signalling and seed vigour in maize
作者:Guo, Shasha;Ai, Junmin;Zheng, Nannan;Hu, Hairui;Xu, Zhuoyi;Chen, Quanquan;Li, Li;Gu, Riliang;Wang, Jianhua;Du, Xuemei;Liu, Yunjun;Zhang, Hongwei;Fu, Junjie;Li, Jieping;Pan, Qingchun;Chen, Fanjun;Yuan, Lixing;Gu, Riliang
关键词:maize; seed vigour; accelerated ageing; ZmRap2.7; natural variation
-
Improving crop yield estimation by unified model parameters and state variable with Bayesian inference
作者:Song, Jianjian;Huang, Jianxi;Huang, Hai;Xiao, Guilong;Li, Xuecao;Li, Li;Su, Wei;Huang, Jianxi;Li, Xuecao;Li, Li;Su, Wei;Wu, Wenbin;Yang, Peng;Liang, Shunlin
关键词:Crop yield estimation; Data assimilation; Bayesian inference; Ensemble Kalman filter; WOFOST model
-
Immunogenicity and protective efficacy of a trimeric full-length S protein subunit vaccine for porcine epidemic diarrhea virus
作者:Guo, Weilu;Wang, Chuanhong;Song, Xu;Xu, Hong;Zhao, Shuqing;Gu, Jun;Qian, Jiali;Zhang, Xue;Guo, Rongli;Li, Jizong;Li, Li;Fan, Baochao;Li, Bin;Fan, Baochao;Li, Bin;Wang, Chuanhong;Song, Xu;Xu, Hong;Zhao, Shuqing;Gu, Jun;Qian, Jiali;Zhang, Xue;Guo, Rongli;Li, Jizong;Li, Li;Fan, Baochao;Li, Bin;Wang, Chuanhong;Hu, Zhaoyang;Li, Bin;Zou, Zhikun;Li, Jing;Guo, Weilu;Ren, Lili;Guo, Weilu;Li, Bin;Ren, Lili;Fan, Baochao;Li, Bin
关键词:PEDV; Trimeric; Full-length S protein; Subunit vaccines; Protection