Construction and prediction of physical fingerprints of notoginseng total saponins extracts

Journal Title: China Powder Science and Technology - Year 2024, Vol 30, Issue 4

Abstract

Objective The current research on the quality consistency evaluation of traditional Chinese medicine mainly focuses on the chemical level, and often overlooks the examination of physical quality attributes. In the production process of traditional Chinese medicine formulations, the physical properties of raw materials significantly impact the formulation and molding quality. As a supplement to the evaluation of chemical properties, the construction of physical fingerprints can reflect the overall properties of raw materials, thereby providing data support for the application of design concepts in the quality control research of traditional Chinese medicine raw materials. Method This study takes the total saponin extract of Panax notoginseng saponins as the research object and evaluates the consistency of samples from different manufacturers from a physical perspective by constructing its physical fingerprint map. A NIR method was developed to address the issue of tedious and time-consuming determination process of physical property parameter, which is not conducive to real-time release during production. This method is used to quickly identify the physical property quality of different raw materials and establish a fast prediction model for important physical property parameters. Results This method can accurately analyze the physical quality of different batches of Panax ginseng total saponin extracts, maximizing the extraction of effective information from the physical property data and facilitating the selection of raw materials in the production process of formulations. It can be used by companies in the early development stage of process design. In addition, the data analysis method presented in this paper is effective in the process of product development and process optimization, as most results can be represented graphically, allowing production staff without a background in data modelling to easily understand the changing patterns of the data. Conclusion A NIR method has been developed to address the issue of tedious and time-consuming physical property parameter determination process, which is not conducive to real-time release during production. This method is used to quickly identify the physical property quality of different raw materials and establish a fast prediction model for important physical property parameters. The method developed in this study could accurately analyze the physical quality of different batches of Panax ginseng total saponin extracts, which could maximize the extraction of effective information from the physical property data and facilitate the selection of raw materials in the production process of formulations and could be used by companies in the early development stage of process design. In addition, the data analysis method presented in this paper is effective in the process of product development and process optimization as most of the results can be represented graphically so that production staff without a background in data modelling can easily understand the changing patterns of the data. This method can improve the production efficiency of enterprises and enhance the quality control of traditional Chinese medicine. At the same time, this research idea is not limited to the powder of Panax ginseng total saponin extract, but is also applicable to other raw materials of traditional Chinese medicine, as well as auxiliary materials and other powdered substances.

Authors and Affiliations

Xiaoyang ZHANG, Yizhe HOU, Pian LI, Zheng LI, Wenlong LI

Keywords

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  • EP ID EP749264
  • DOI 10.13732/j.issn.1008-5548.2024.04.011
  • Views 26
  • Downloads 0

How To Cite

Xiaoyang ZHANG, Yizhe HOU, Pian LI, Zheng LI, Wenlong LI (2024). Construction and prediction of physical fingerprints of notoginseng total saponins extracts. China Powder Science and Technology, 30(4), -. https://europub.co.uk/articles/-A-749264