Phase Model-Driven Deep Learning for Robust Phase Correction in High-Throughput NMR-Based Metabolomics.

Journal: The journal of physical chemistry letters
Published Date: Jan 8, 2026

Abstract

High-throughput NMR, a key metabolomics tool, enables efficient, noninvasive profiling of large biological samples. Automatic data processing ensures scalable, consistent high-throughput NMR. A key workflow step is phase correction, critical for obtaining pure absorption-mode spectra necessary for accurate quantitative analysis. This study proposes the Phase Model-Driven Residual Attention Network (PD-RAN), a robust phase correction method that combines deep neural networks with a physically informed model. By learning low-dimensional phase features grounded in physical principles from one-dimensional NMR spectra containing thousands of data points (high-dimensional data representation), PD-RAN delivers precise and reliable phase correction. Experimental results show consistent superiority over conventional methods across diverse metabolomics samples, including brain extracts, plasma, and urine. The method demonstrates remarkable efficiency, processing 1,000 spectra in just 20 ms, rendering it highly suitable for high-throughput NMR metabolomics applications. Ablation studies further validate the effectiveness of the phase model-driven component and its robustness to noise and baseline distortions.

Authors

  • Chuanwen Zhao
    State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
  • Gang Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Caixiang Liu
    State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
  • Zhao Li
    Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China. [email protected].
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Peng Sun
    Department of Microelectronics, Nankai University, Tianjin, 300350, PR China.
  • Hongkang Chu
    State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
  • Maili Liu
    State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, PR China; University of Chinese Academy of Sciences, 10049 Beijing, PR China.
  • Peijun Song
    School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China.
  • Qingjia Bao
    Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China.
  • Chaoyang Liu
    Wuhan Institute of Physics and Mathematics, Innovation Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.

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