Chromatographic fingerprinting is a time-consuming analytical method used to evaluate the overall chemical properties of natural products. While NIR spectroscopy is a rapid analytical tool, it is typically used to predict only a few quality markers. In this study, we proposed a multivariable signal conversion strategy that can rapidly assess the quality of natural products by reproducing HPLC signals from NIR spectra. The strategy converted the NIR spectroscopic fingerprints to chromatographic fingerprints using PLS and CNN, respectively, and evaluated the performances of conversion using Pearson correlation coefficient and nearness index. Hierarchical clustering analysis and correlation analysis were used to evaluate the similarity between the converted and real fingerprints to reflect the goodness of the conversion. The CNN models showed comparable and satisfactory results to PLS models in predicting quality markers and converting fingerprints. Moreover, CNN models did not require spectral pretreatment, which makes them more advantageous than PLS models. The proposed strategy combines the strengths of NIR spectroscopy, such as information-rich and high-speed analysis, and the benefits of chromatographic fingerprinting, which provides a better understanding of overall chemical properties. Therefore, it has great potential for application in the field of natural product analysis.

色谱指纹图谱是一种耗时的分析方法，用于评估天然产物的整体化学性质。虽然近红外光谱是一种快速分析工具，但它通常仅用于预测少数质量标记。在这项研究中，我们提出了一种多变量信号转换策略，可以通过从近红外光谱再现 HPLC 信号来快速评估天然产物的质量。该策略使用PLS将近红外光谱指纹转换为色谱指纹分别使用 Pearson 相关系数和邻近指数评估转换的性能。采用层次聚类分析和相关性分析来评价转换后的指纹与真实指纹的相似度，以反映转换的优劣。CNN 模型在预测质量标记和转换指纹方面显示出与 PLS 模型相当且令人满意的结果。此外，CNN 模型不需要光谱预处理，这使得它们比 PLS 模型更有优势。所提出的策略结合了近红外光谱的优势（例如信息丰富和高速分析）以及色谱指纹图谱的优势，可以更好地了解整体化学性质。因此，它在天然产物分析领域具有巨大的应用潜力。