ArticleScientific reports2019
Ultra-Clean Pure Shift
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deriving three one dimensional NMR spectra from a single experiment through machine learning.Nature communications · 2025Article
- Practical Aspects of NMR-Based Metabolomics.Handbook of experimental pharmacology · 2023Article
- NMR methods for the analysis of mixtures.Chemical communications (Cambridge, England) · 2022Review
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
- Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts.Metabolites · 2021Article
- Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives.Frontiers in molecular biosciences · 2021Review
- PSYCHE-A Valuable Experiment in Plant NMR-Metabolomics.Molecules (Basel, Switzerland) · 2020Article
- NMR-Based Metabolic Profiling of Edible Olives-Determination of Quality Parameters.Molecules (Basel, Switzerland) · 2020Article
- Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization.International journal of molecular sciences · 2020Article
- Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis.Metabolites · 2020Review
- NMR Spectroscopy for Metabolomics Research.Metabolites · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.
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