Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology.

Food Chem

The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China. Electronic address:

Published: May 2024

AI Article Synopsis

  • Panax notoginseng powder (PNP) is a valuable medicinal product, but its effectiveness is compromised by widespread adulteration in the market.
  • This study aims to create a quality evaluation system using artificial intelligence sensory and multi-source information fusion technologies to accurately assess different grades of PNP and its adulterated varieties.
  • The results showed a 100% accuracy in identifying PNP grades and adulteration, with highly precise predictions for adulteration ratios and total saponin content, indicating a reliable method for evaluating PNP quality.

Article Abstract

Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.

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Source
http://dx.doi.org/10.1016/j.foodchem.2023.138210DOI Listing

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