Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that...

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Bibliographic Details
Main Authors: Huiqiang Hu, Tingting Wang, Yunpeng Wei, Zhenyu Xu, Shiyu Cao, Ling Fu, Huaxing Xu, Xiaobo Mao, Luqi Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-10-01
Series:Frontiers in Plant Science
Subjects:
Puerariae Thomsonii Radix
isoflavones and starch content
hyperspectral imaging
deep learning
one-dimensional convolutional neural network
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2023.1271320/full

Internet

https://www.frontiersin.org/articles/10.3389/fpls.2023.1271320/full

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