School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chengdu Pushi Pharmaceutical Technology Co., Ltd, Chengdu, China.
Crit Rev Anal Chem. 2024;54(7):2618-2635. doi: 10.1080/10408347.2023.2189477. Epub 2023 Mar 26.
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.
中药材的真实性和质量直接影响临床疗效和安全性。由于需求增加和资源短缺，中药材质量评价（QATCM）是全球关注的问题。最近，现代分析技术已被广泛研究和应用于分析中药材的化学成分。然而，单一的分析技术存在一些局限性，仅从成分特征来判断中药材的质量不足以反映中药材的整体情况。因此，多源信息融合技术和机器学习（ML）的发展进一步提高了 QATCM。来自不同分析仪器的数据信息可以从多个方面更好地了解草药样本之间的联系。本综述重点介绍了数据融合（DF）和 ML 在 QATCM 中的应用，包括色谱、光谱和其他电子传感器。介绍了常见的数据结构和 DF 策略，以及包括快速发展的深度学习在内的 ML 方法。最后，讨论并说明了 DF 策略与 ML 方法相结合在中药材源识别、物种鉴定和含量预测等研究中的应用。本综述证明了基于 DF 和 ML 策略的 QATCM 的有效性和准确性，并为开发和应用 QATCM 方法提供了参考。
