目的 基于文献计量学方法分析近红外光谱（NIRS）在中药质量控制领域的研究现状，把握NIRS技术未来研究方向和发展趋势，为其深入应用提供参考。方法 检索中国学术期刊全文数据库（CNKI）、万方数据知识服务平台（Wanfang）、维普数据库（VIP）、Web of Science Core Collection（WOSCC）、PubMed数据库中收录的2000年1月1日—2024年12月31日有关NIRS技术在中药质量控制中的相关文献，利用VOSviewer1.6.20和CiteSpace6.3.R1软件对年发文量、国家、机构、作者、关键词等方面进行可视化分析。结果 最终纳入1 161篇中文文献和251篇英文文献，年发文量呈现出波动上升的趋势。发文量最多的3个国家分别是中国、美国和澳大利亚，中国是主要的研究力量。北京中医药大学和浙江大学分别是中、英文文献发文量最多的机构。乔延江、Li Wenlong分别为中、英文发文量最多的核心作者。研究热点聚焦于中药的定性和定量分析、生产过程的在线监测以及化学计量学模型的开发与优化等方面。突现分析显示，利用机器学习以及多技术联用方法对产地溯源、中药制造等领域进行中药质量评价可能是未来的发展趋势。结论 NIRS技术在中药质量控制中应用广泛，但需加强中药制剂质量控制及研究成果转化。未来应优化算法，推动跨学科融合，构建智能检测体系。

Objective To analyze the research status, future directions and development trends in the application of near-infrared spectroscopy (NIRS) for quality control of traditional Chinese medicine (TCM) through bibliometric analysis, and to provide reference for its further development. Methods Databases such as CNKI, Wanfang, VIP, Web of Science Core Collection (WoSCC), and PubMed were searched to collect relevant literature on this topic from 2000 to 2024. The annual publication volume was statistically analyzed based on the final included studies. Bibliometric tools VOSviewer 1.6.20 and CiteSpace 6.3.R1 were employed for visualization analysis of countries, institutions, authors, and keywords. Results A total of 1 161 Chinese literatures and 251 English literatures were included in this study. Annual publications demonstrated a fluctuating upward trend. The top three countries in terms of publication output were China, the United States, and Australia, with China being the dominant contributor. Beijing University of Chinese Medicine and Zhejiang University were the most productive institutions in Chinese and English literature, respectively. Qiao Yanjiang and Li Wenlong emerged as the core authors with the highest publication counts in Chinese and English literatures. Research hotspots focused on the qualitative and quantitative analysis of TCM, on-line monitoring of the production process, and the development and optimization of chemometric models. Burst analysis showed that the use of machine learning and multi-technology integration methods for the quality evaluation of TCM in fields such as origin traceability and TCM manufacturing may be the future development trend. Conclusion NIRS is widely applied in the quality control of traditional Chinese medicine, but it is necessary to enhance the quality control of traditional Chinese medicine preparations and the transformation of research results. In the future, algorithms should be optimized, interdisciplinary integration promoted, and an intelligent detection system constructed.

R284.1

山东省技术创新引导计划（中央引导地方科技发展资金）项目（YDZX2023027）