ArticleFrontiers in plant science2023
Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Investigation and Rapid Determination of Storage-Induced Multidimensional Quality Deterioration of Wenyujin Rhizoma Concisum.Foods (Basel, Switzerland) · 2026Article
- The integration of machine learning into traditional Chinese medicine.Journal of pharmaceutical analysis · 2025Review
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.Frontiers in plant science · 2024Article
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Authors and funding
9 authors.
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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 (R
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