Work

Real-Time Correction Algorithm for a Chromatographic Background Based on Numerical Algorithm

Year: 2026

Type: article

Abstract: Although numerous baseline correction methods exist, most are confined to static post-elution processing and fail to meet real-time analysis requirements. To address this, we propose a real-time baseline estimation method based on the Informer time-series prediction model that performs correction during data acquisition without waiting for complete elution. Our work focuses on three key aspects: chromatographic dataset construction, model training, and baseline prediction. Simulation experiments demonstrate that the proposed method achieves comparable accuracy to conventional static processing approaches while exhibiting significant real-time advantages. In processing real chromatographic data, the model achieves a 98.3% chromatographic peak retention rate, with a single computation time of approximately 35 ms—substantially shorter than typical chromatographic sampling cycles (600–900 ms), thus fully satisfying the quantitative analysis requirements for real-time background subtraction.

Source: Separations

DOI: 10.3390/separations13010034

Authors: Jinlin Chen, Yiquan Wu, Xinmei Xu

Institutions: Huainan Normal University, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Analytical Chemistry and Chromatography

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China