College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China.
Anal Methods. 2022 Jun 1;14(21):2051-2062. doi: 10.1039/d2ay00358a.
Chromatographic fingerprinting provides effective technical means for quality evaluation of traditional Chinese medicine. In this work, a novel multi-wavelength fusion column fingerprint was obtained by intelligent selection of chromatographic peaks from different wavelengths, which displayed the maximum peak area information under the optimal wavelength at the same retention time. Here, the root was selected as a sample. The multi-wavelength fusion column fingerprint graph of the root was constructed from five wavelengths (203 nm, 210 nm, 238 nm, 250 nm and 330 nm). The peak capacity, peak resolution, the number of common peaks and similarity were used to evaluate the performance. The 19 batches of root were classified into three categories with clear distinction between origin categories based on the multi-wavelength fusion column fingerprint combined with chemometrics, including hierarchical cluster analysis and principal component analysis. Nine markers of variation that led to differences between batches were screened by orthogonal partial least squares discriminant analysis. This study demonstrated that the classification model based on the multi-wavelength fusion column fingerprint was better than that on a single-wavelength, and the fusion fingerprint was suitable for the identification and quality control of traditional Chinese medicine with more comprehensive chemical composition information and more accurate prediction ability.
色谱指纹图谱为中药质量评价提供了有效的技术手段。本工作通过智能选择不同波长的色谱峰，获得了一种新的多波长融合柱指纹图谱，该图谱在同一保留时间下显示了最佳波长下的最大峰面积信息。本文以 根为样品，构建了 根的五波长（203nm、210nm、238nm、250nm 和 330nm）多波长融合柱指纹图谱。采用峰容量、峰分辨率、共有峰数和相似度对其性能进行评价。基于多波长融合柱指纹图谱结合化学计量学（包括层次聚类分析和主成分分析），将 19 批 根分为三类，其产地分类清晰。通过正交偏最小二乘判别分析筛选出导致批次间差异的 9 个变异标志物。该研究表明，基于多波长融合柱指纹的分类模型优于单波长模型，融合指纹适用于具有更全面化学成分信息和更准确预测能力的中药鉴别和质量控制。
