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

Frontiers in Plant Science

Plant Science

Tingting Wang¹  Huaxing Xu¹  Yunpeng Wei¹  Zhenyu Xu¹  Huiqiang Hu²  Xiaobo Mao²  Luqi Huang³  Shiyu Cao⁴  Ling Fu⁴ 

[1] School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China;School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China;Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China;School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China;Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China;State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China;School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China;

关键词: Puerariae Thomsonii Radix;    isoflavones and starch content;    hyperspectral imaging;    deep learning;    one-dimensional convolutional neural network;   

DOI  :  10.3389/fpls.2023.1271320

 received in 2023-08-02, accepted in 2023-10-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

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 harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

【 授权许可】

Unknown   
Copyright © 2023 Hu, Wang, Wei, Xu, Cao, Fu, Xu, Mao and Huang