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Predicting the Content of the Main Components of Gardeniae Fructus Praeparatus Based on Deep Learning

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Abstract

Gardeniae Fructus (GF) and its stir-fried product, Gardeniae Fructus Praeparatus (GFP), are commonly used herbal medicines in traditional Chinese clinic. However, it is challenging to measure the content of GFP’s main components rapidly during processing. In this paper, an MLP-based method for GFP component content prediction is proposed. 10 deep learning models including CNN and Transformer are used to extract features from the built image dataset. Combined with the measured component content data, the extracted feature data are used to train the MLP regression model and evaluate the effect. It is demonstrated that the proposed method can be used for the rapid and nondestructive determination of the content of the main components of Chinese herbal pieces. This study provides insights for similar studies.

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Funding

This research was funded by Scientific and technological innovation project of China Academy of Chinese Medical Sciences (No. CI2021A04204), the National Natural Science Foundation of China projects (Nos. 82173979 and 81873010), and project of NATCM for traditional Chinese medicine processing technology inheritance base (National Science and Technology of Traditional Chinese Medicine-Chinese Materia Medica [2022] No. 59).

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Authors and Affiliations

  1. School of Technology, Beijing Forestry University, Beijing, 100083, China

    Chongyang Wang & Pengle Cheng

  2. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Yun Wang & Cun Zhang

  3. Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND, 58102, USA

    Ying Huang

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  1. Chongyang Wang
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  2. Yun Wang
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  3. Pengle Cheng
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  4. Cun Zhang
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Correspondence to Pengle Cheng or Cun Zhang.

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Wang, C., Wang, Y., Cheng, P. et al. Predicting the Content of the Main Components of Gardeniae Fructus Praeparatus Based on Deep Learning. Stat Biosci (2024). https://doi.org/10.1007/s12561-024-09421-0

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