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

Chongyang Wang (), Yun Wang (), Pengle Cheng (), Cun Zhang () and Ying Huang ()
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Chongyang Wang: Beijing Forestry University
Yun Wang: China Academy of Chinese Medical Sciences
Pengle Cheng: Beijing Forestry University
Cun Zhang: China Academy of Chinese Medical Sciences
Ying Huang: North Dakota State University

Statistics in Biosciences, 2024, vol. 16, issue 3, No 13, 823 pages

Abstract: 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.

Keywords: Gardeniae Fructus Praeparatus; Computer vision; Deep learning; Regression; Content prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-024-09421-0

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