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Archive > Volume 17 Issue 3 > 2025,17(3):484-487. DOI:https://doi.org/10.1016/j.chmed.2025.05.006 Prev Next

揭開中藥的秘密：成分分析的尖端技術(shù)

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Unveiling secrets of traditional Chinese medicine: Cutting-edge techniques in component analysis

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    摘要:
    化學(xué)成分分析是中藥研究中的關(guān)鍵挑戰(zhàn)，涉及對(duì)傳統(tǒng)中藥（TCM）中復(fù)雜成分的定性和定量鑒定。然而，傳統(tǒng)的分析方法對(duì)于中藥復(fù)雜成分的高效和全面分析來說是不夠的。在樣品制備、儀器技術(shù)、數(shù)據(jù)處理以及活性相關(guān)質(zhì)量標(biāo)志物研究方面都存在局限性。近期的進(jìn)展顯著提高了分析精度，能夠更全面地描繪中藥成分。新的預(yù)處理方法提高了提取效率和檢測(cè)靈敏度，而諸如質(zhì)譜成像之類的新型儀器技術(shù)則保留了均質(zhì)化過程中丟失的空間信息。人工智能增強(qiáng)了數(shù)據(jù)解釋，提高了準(zhǔn)確性和效率。在線活性分析將化學(xué)成分與生物活性聯(lián)系起來，克服了單純化學(xué)分析的局限性，能夠更全面地評(píng)估中藥的功效。這種觀點(diǎn)提供了對(duì)成分分析發(fā)展趨勢(shì)的概覽，旨在推動(dòng)該領(lǐng)域的發(fā)展并支持中醫(yī)藥現(xiàn)代化。

    Abstract:
    Chemical component analysis is a critical challenge in Chinese herbal medicine research, involving the qualitative and quantitative identification of complex constituents in traditional Chinese medicine (TCM). However, traditional analytical methods are insufficient for efficient and comprehensive analysis of complex composition of TCM. Limitations exist in sample preparation, instrumental technology, data processing, and activity-related quality marker research. Recent advancements have significantly improved analytical precision, enabling more comprehensive profiling of TCM components. New pretreatment methods improve extraction efficiency and detection sensitivity, while novel instrumental technologies, such as mass spectrometry imaging, preserve spatial information lost in homogenization. AI enhances data interpretation, improving accuracy and efficiency. Online activity analysis links chemical composition with bioactivity, overcoming the limitations of purely chemical profiling and enabling a more comprehensive evaluation of TCM efficacy. This perspective provides an overview of the development trends in component analysis, aiming to advance the field and support TCM modernization.

    關(guān)鍵詞:
    數(shù)據(jù)處理;儀器技術(shù);在線活動(dòng)分析;預(yù)處理方法;中藥成分分析

    Keywords:


    Project Supported:
    The research was supported by the National Natural Science Foundation of China (No. 82461160264, 82474056, 82404854), and the grant from Science and Technology Commission of Shanghai Municipality (No. 21S21902400), Shanghai Municipal Health Commission (No. 2022XD037), Shanghai Magnolia Talent Plan Pujiang Project (No. 23PJD113), the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2023ZD502605), and the Science and Technology Development Fund, Macau SAR (No. 0151/2024/AFJ).

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