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

Huiqiang Hu; Tingting Wang; Yunpeng Wei; Zhenyu Xu; Shiyu Cao; Ling Fu; Huaxing Xu; Xiaobo Mao; Luqi Huang

doi:10.3389/fpls.2023.1271320

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

Front Plant Sci. 2023 Oct 25:14:1271320. doi: 10.3389/fpls.2023.1271320. eCollection 2023.

Authors

Huiqiang Hu^{1

2}, Tingting Wang¹, Yunpeng Wei¹, Zhenyu Xu¹, Shiyu Cao³, Ling Fu³, Huaxing Xu¹, Xiaobo Mao^{1

2}, Luqi Huang^{1

2

4}

Affiliations

¹ School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.
² Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China.
³ School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
⁴ 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.

Abstract

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.

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

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Key Project at Central Government Level (grant numbers 2060302-2101-26) and the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (grant numbers ZYYCXTD-D-202205).