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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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Materials informatics approach using domain modelling for exploring structure-property relationships of polymers. Sci Rep 2022; 12:10558. [PMID: 35732681 PMCID: PMC9217937 DOI: 10.1038/s41598-022-14394-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022] Open
Abstract
In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, 1H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the 1H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T2 relaxation time for domains with different mobility. The average T2 relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D. Metabolites 2021; 11:metabo11070452. [PMID: 34357346 PMCID: PMC8305572 DOI: 10.3390/metabo11070452] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/03/2021] [Accepted: 07/12/2021] [Indexed: 11/17/2022] Open
Abstract
NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity.
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Kurotani A, Kakiuchi T, Kikuchi J. Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN). ACS OMEGA 2021; 6:14278-14287. [PMID: 34124451 PMCID: PMC8190808 DOI: 10.1021/acsomega.1c01035] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/28/2021] [Indexed: 06/12/2023]
Abstract
Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".
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Affiliation(s)
- Atsushi Kurotani
- RIKEN
Center for Sustainable Resource Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Toshifumi Kakiuchi
- AGC
Yokohama Technical Center, 1-1 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN
Center for Sustainable Resource Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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Snyder DA. Covariance NMR: Theoretical concerns, practical considerations, contemporary applications and related techniques. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2021; 122:1-10. [PMID: 33632414 DOI: 10.1016/j.pnmrs.2020.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/23/2020] [Accepted: 09/29/2020] [Indexed: 06/12/2023]
Abstract
The family of resolution enhancement and spectral reconstruction techniques collectively known as covariance NMR continues to expand, along with the list of applications for these techniques. Recent advances in covariance NMR include the utilization of covariance to reconstruct pure shift NMR spectra, and the growing use of covariance NMR in processing non-uniformly sampled data, especially in solid state NMR and metabolomics. This review describes theoretical and practical considerations for direct and indirect covariance NMR techniques, and summarizes recent additions to the covariance NMR family. The review also outlines some of the applications of covariance NMR, and places covariance NMR in the larger context of methods that use statistical and algebraic approaches to enhance and combine various kinds of spectroscopic data, including tensor-based approaches for multidimensional NMR and heterocovariance spectroscopy.
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Affiliation(s)
- David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University of NJ, United States.
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Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials. Int J Mol Sci 2021; 22:ijms22031086. [PMID: 33499371 PMCID: PMC7865946 DOI: 10.3390/ijms22031086] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 01/15/2021] [Accepted: 01/17/2021] [Indexed: 01/19/2023] Open
Abstract
Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.
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Wu K, Luo J, Zeng Q, Dong X, Chen J, Zhan C, Chen Z, Lin Y. Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet. Anal Chem 2020; 93:1377-1382. [DOI: 10.1021/acs.analchem.0c03087] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ke Wu
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jie Luo
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Qing Zeng
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Xi Dong
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jinyong Chen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Chaoqun Zhan
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Zhong Chen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Yanqin Lin
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
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Bhattacharjya S, Yang D, Yoon HS. Special Issue "Selected Papers from the 8th Asia-Pacific NMR (APNMR) Symposium: Recent Advances in NMR Spectroscopy". Int J Mol Sci 2020; 21:ijms21124419. [PMID: 32580280 PMCID: PMC7352290 DOI: 10.3390/ijms21124419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 06/19/2020] [Indexed: 11/29/2022] Open
Affiliation(s)
- Surajit Bhattacharjya
- School of Biological Sciences, 60 Nanyang Drive, Nanyang Technological University, Singapore 637551, Singapore
- Correspondence: (S.B.); (D.Y.); (H.S.Y.); Tel.: +65-6316-7997 (S.B.); +65-6516-1014 (D.Y.); +65-6316-2846 (H.S.Y.)
| | - Daiwen Yang
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558, Singapore
- Correspondence: (S.B.); (D.Y.); (H.S.Y.); Tel.: +65-6316-7997 (S.B.); +65-6516-1014 (D.Y.); +65-6316-2846 (H.S.Y.)
| | - Ho Sup Yoon
- School of Biological Sciences, 60 Nanyang Drive, Nanyang Technological University, Singapore 637551, Singapore
- Correspondence: (S.B.); (D.Y.); (H.S.Y.); Tel.: +65-6316-7997 (S.B.); +65-6516-1014 (D.Y.); +65-6316-2846 (H.S.Y.)
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