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- Title
Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix.
- Authors
Huiqiang Hu; Tingting Wang; Yunpeng Wei; Zhenyu Xu; Shiyu Cao; Ling Fu; Huaxing Xu; Xiaobo Mao; Luqi Huang
- 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 nondestructive 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.
- Subjects
ISOFLAVONES; DEEP learning; CONVOLUTIONAL neural networks; PARTIAL least squares regression; SPECTRAL imaging; STARCH
- Publication
Frontiers in Plant Science, 2023, p1
- ISSN
1664-462X
- Publication type
Article
- DOI
10.3389/fpls.2023.1271320