目的 基于文獻計量學方法分析近紅外光譜（NIRS）在中藥質量控制領域的研究現(xiàn)狀，把握NIRS技術未來研究方向和發(fā)展趨勢，為其深入應用提供參考。方法 檢索中國學術期刊全文數(shù)據庫（CNKI）、萬方數(shù)據知識服務平臺（Wanfang）、維普數(shù)據庫（VIP）、Web of Science Core Collection（WOSCC）、PubMed數(shù)據庫中收錄的2000年1月1日—2024年12月31日有關NIRS技術在中藥質量控制中的相關文獻，利用VOSviewer1.6.20和CiteSpace6.3.R1軟件對年發(fā)文量、國家、機構、作者、關鍵詞等方面進行可視化分析。結果 最終納入1 161篇中文文獻和251篇英文文獻，年發(fā)文量呈現(xiàn)出波動上升的趨勢。發(fā)文量最多的3個國家分別是中國、美國和澳大利亞，中國是主要的研究力量。北京中醫(yī)藥大學和浙江大學分別是中、英文文獻發(fā)文量最多的機構。喬延江、Li Wenlong分別為中、英文發(fā)文量最多的核心作者。研究熱點聚焦于中藥的定性和定量分析、生產過程的在線監(jiān)測以及化學計量學模型的開發(fā)與優(yōu)化等方面。突現(xiàn)分析顯示，利用機器學習以及多技術聯(lián)用方法對產地溯源、中藥制造等領域進行中藥質量評價可能是未來的發(fā)展趨勢。結論 NIRS技術在中藥質量控制中應用廣泛，但需加強中藥制劑質量控制及研究成果轉化。未來應優(yōu)化算法，推動跨學科融合，構建智能檢測體系。

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

山東省技術創(chuàng)新引導計劃（中央引導地方科技發(fā)展資金）項目（YDZX2023027）