Johnson Hayden, Tipirneni-Sajja Aaryani
Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
Metabolites. 2024 Dec 1;14(12):666. doi: 10.3390/metabo14120666.
The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy.
台式核磁共振（NMR）仪器的引入使NMR光谱学成为研究和工业领域更易获得且价格合理的选择，但在低磁场光谱仪上采集的信号具有较低的光谱分辨率和信噪比，这可能会使光谱的定量分析变得复杂。在这项工作中，我们比较了多种神经网络架构在将模拟的100 MHz NMR光谱转换为400 MHz光谱任务中的性能，目的是提高用于分析物定量的低场光谱的质量。多层感知器网络也用于直接对模拟的100 MHz和400 MHz光谱中的代谢物进行定量，以便进行比较。在本研究中，Transformer网络是唯一能够可靠地将21种和87种代谢物混合物中的低场NMR光谱转换为高场光谱的架构。与高场转换光谱相比，直接处理低场光谱时基于多层感知器的代谢物定量略为准确，至少在当前研究中，这排除了从低场到高场光谱转换的必要性；然而，这种低场和高场定量的比较需要进一步的研究、比较和实验验证。NMR数据处理的Transformer方法在将低场模拟光谱转换为高场以用于代谢组学应用方面是有效的，并且可以进一步探索以实现NMR光谱学其他领域的自动化处理。
