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Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring. Processes (Basel) 2022. [DOI: 10.3390/pr10071264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers 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.
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Lindner S, Burger R, Rutledge DN, Do XT, Rumpf J, Diehl BWK, Schulze M, Monakhova YB. Is the Calibration Transfer of Multivariate Calibration Models between High- and Low-Field NMR Instruments Possible? A Case Study of Lignin Molecular Weight. Anal Chem 2022; 94:3997-4004. [PMID: 35201769 DOI: 10.1021/acs.analchem.1c05125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Although several successful applications of benchtop nuclear magnetic resonance (NMR) spectroscopy in quantitative mixture analysis exist, the possibility of calibration transfer remains mostly unexplored, especially between high- and low-field NMR. This study investigates for the first time the calibration transfer of partial least squares regressions [weight average molecular weight (Mw) of lignin] between high-field (600 MHz) NMR and benchtop NMR devices (43 and 60 MHz). For the transfer, piecewise direct standardization, calibration transfer based on canonical correlation analysis, and transfer via the extreme learning machine auto-encoder method are employed. Despite the immense resolution difference between high-field and low-field NMR instruments, the results demonstrate that the calibration transfer from high- to low-field is feasible in the case of a physical property, namely, the molecular weight, achieving validation errors close to the original calibration (down to only 1.2 times higher root mean square errors). These results introduce new perspectives for applications of benchtop NMR, in which existing calibrations from expensive high-field instruments can be transferred to cheaper benchtop instruments to economize.
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Affiliation(s)
- Simon Lindner
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - René Burger
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Douglas N Rutledge
- Université Paris-Saclay, INRAE, AgroParisTech, UMR SayFood, Paris 75005, France.,National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia
| | - Xuan Tung Do
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Jessica Rumpf
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Bernd W K Diehl
- Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany
| | - Margit Schulze
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Yulia B Monakhova
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany.,Institute of Chemistry, Saratov State University, Saratov 410012, Russia
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