Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring

Koki Hara, Shunji Yamada, Eisuke Chikayama, Jun Kikuchi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spec-trometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra.
Original languageEnglish (US)
Pages (from-to)1264
Issue number7
StatePublished - Jun 27 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: The authors thank Yuuri Tsuboi and Atsushi Kurotani (RIKEN) for their support with the data acquisition and processing of NMR.The authors’ work described in this paper was supported, in part, by grants to J.K. from the Strategic Innovation Program from Cabinet Office (CAO) of Japan, and KAUST-RIKEN collaborative research for Marinomics, which is omics-based monitoring for marine ecology. S.Y. was supported by the promotion program for informatics and data science of RIKEN Center for Sustainable Resource Science.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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