Determination of Saponin Compounds of Panax notoginseng Roots Using FT-IR Spectroscopy

By using Fourier transform near-infrared (FT-NIR) spectroscopy, researchers can reduce the time and improve the accuracy in assessing saponin compounds of Panax notoginseng, according to a recent study published in Vibrational Spectroscopy (1).

P. notoginseng, a well-known traditional Chinese medicine, is highly valued for its rich saponin content, which is extensively used in various clinical treatments, including patients with ischemic stroke (2). However, traditional methods for analyzing the saponin content are not without drawbacks. These methods often involve destructive testing and are time-consuming, making them impractical for large-scale or rapid testing needs (1). This has created a significant bottleneck in the quality control of medicinal materials, prompting the need for more efficient and non-destructive analytical techniques (1).

Researchers from the Yunnan Academy of Chinese Medicine and the Yunnan Academy of Agricultural Sciences explored this topic. In their study, the research team explored the feasibility of using FT-NIR spectroscopy to quickly assess the saponin content in P. notoginseng (1). FT-NIR spectroscopy is a well-established technique used in various industries such as pharmaceutical, food, and agriculture that offers non-destructive analysis, providing rapid results without compromising the integrity of the samples (2). By analyzing spectral data from 252 samples of P. notoginseng, the researchers aimed to develop a reliable prediction model for saponin content.

To achieve this, the team utilized partial least squares regression (PLSR), a statistical method known for its efficacy in handling complex data sets. Various variable selection methods were employed to enhance the model’s accuracy, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE), and correlation coefficients (1).

Among the variable selection methods tested, the correlation coefficient method proved to be the most effective. The correlation-PLSR model exhibited high coefficients of determination and low root mean square errors, indicating excellent predictive performance (1). These results demonstrate that the correlation-PLSR model can accurately and rapidly predict the saponin content in P. notoginseng, providing a significant improvement over traditional method (1).

The researchers highlighted that correlation-PLSR model outperformed the full-spectra PLSR model in terms of accuracy and generalization (1). As a result, the study demonstrated that this model is more suitable for practical applications. This advancement provides a robust framework for the quick and precise determination of the chemical composition of medicinal materials, setting a new standard for quality control in the field (1).

This study also opens avenues for future research. The linear regression approach was demonstrated in this study to be effective, so the researchers suggested that a future study could explore non-linear regression models, which could further enhance the accuracy and reliability of saponin content determination in P. notoginseng and offer additional insights and improvements to the methodological framework (1)

This study marks a significant milestone in the field of traditional Chinese medicine, presenting a novel and efficient method for the rapid assessment of saponin compounds in P. notoginseng. As the quest for better and faster methods continues, this study stands as a testament to the innovative spirit driving the evolution of traditional medicine.

References

(1) Li, C.; Zuo, Z.; Wang, Y. Optimization of Fourier Transform Near-Infrared Spectroscopy Model in Determining Saponin Compounds of Panax notoginseng Roots. Vib. Spectrosc. 2024, 130, 103615. DOI: 10.1016/j.vibspec.2023.103615

(2) Wu, L.; Song, H.; Zhang, C. Efficacy and Safety of Panax notoginseng Saponins in the Treatment of Adults with Ischemic Stroke in China: A randomized Clinical Trial. JAMA Netw Open. 2023, 6 (6), e2317574. DOI: 10.1001/jamanetworkopen.2023.17574