Compressed chromatographic fingerprint of Artemisiae argyi Folium empowered by 1D-CNN: Reduce mobile phase consumption using chemometric algorithm

Introduction

High-Performance Liquid Chromatography (HPLC) is widely used for its high sensitivity, stability, and accuracy. Nonetheless, it often involves lengthy analysis times and considerable solvent consumption, especially when dealing with complex systems and quality control, posing challenges to green and eco-friendly analytical practices.

Objective

This study proposes a compressed fingerprint chromatogram analysis technique that combines a one-dimensional convolutional neural network (1D-CNN) with HPLC, aiming to improve the analytical efficiency of various compounds in complex systems while reducing the use of organic solvents.

Materials and Methods

The natural product Artemisiae argyi Folium (AAF) was selected as the experimental subject. Firstly, HPLC fingerprints of AAF were developed based on conventional programs. Next, a compressed fingerprint was obtained without losing compound information. Finally, a 1D-CNN deep learning model was used to analyze and identify the compressed chromatograms, enabling quantitative analysis of 10 compounds in complex systems.

Results

The results indicate that the 1D-CNN model can effectively extract features from complex data, reducing the analysis time for each sample by about 40 min. In addition, the consumption of mobile phase has significantly decreased by 78 % compared to before. Among the ten compounds to be analyzed, nine of them achieved good results, with the highest correlation coefficient reaching above 0.95, indicating that the model has strong explanatory power.

Conclusion

The proposed compressed fingerprint chromatograms recognition technique enhances the environmental sustainability and efficiency of traditional HPLC methods, offering valuable insights for future advancements in analytical methodologies and equipment development.