Research Progress of NMR in Natural Product Quantification

Wang, Zhifan; You, Yu-Lin; Li, Feifei; Kong, Wen-Ru; Wang, Shuqi

doi:10.3390/molecules26206308

Cited by 27 publications

(20 citation statements)

References 151 publications

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“…For the 1 H‐NMR experiment, we used the 8a proton (6), from the indolic ring, and its intensity was related to that obtained with the three protons of acetonitrile (CN‐CH 3 ), used as internal standard. The solution containing cis ‐WOOH was spiked with a known concentration of acetonitrile, and it was quantified simultaneously by NMR (26), iodometry and xylenol‐orange assay (Table 4). The ratio between the calculated concentrations was 1.1:1 for RMN:iodometric quantifications and 1.3:1 for RMN:ferric‐xylenol orange measurements, showing that for this specific hydroperoxides, both colorimetric methods would work for quantification, although the iodometry method is more accurate.…”

Section: Resultsmentioning

confidence: 99%

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†

Jayme

Prado

Massafera

et al. 2022

Photochem & Photobiology

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The reaction of singlet oxygen (1O2) with the amino acids tryptophan and tyrosine, either free or inserted in peptides or proteins, gives rise to hydroperoxides. To understand the impact of these hydroperoxides in complex biological systems, methods allowing their characterization and accurate quantification must be available. In this work, hydroperoxides derived from tryptophan and tyrosine and from peptides containing these amino acids were synthesized by photooxidation, and characterized by high‐resolution mass spectrometry. In addition, experiments were carried out to compare two colorimetric methods commonly used for quantification of peroxides, namely the iodometric and the ferric‐xylenol orange assays. For the tryptophan hydroperoxide, the quantifications obtained by colorimetric methods were then compared to that obtained by NMR. The results showed that for the ferric‐xylenol orange method, the stoichiometry between peroxide and Fe3+ ions varies considerably. On the other hand, for the iodometric assay, the stoichiometry peroxide:I3− ions is always 1:1. However, the kinetics of the reactions of peroxides with I− vary, and the assay must be performed in anaerobic conditions. Thus, the iodometric method is more appropriate for precise quantification of a given peroxide. The characterization and accurate quantification of biological peroxides is key to understand the mechanisms involved in redox processes.

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Section: Resultsmentioning

confidence: 99%

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†

Jayme

Prado

Massafera

et al. 2022

Photochem & Photobiology

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“…Molecules 2022, 27, 3653 2 of 16 metabolomics [7][8][9], drug discovery [10][11][12], food [13,14], natural products [15,16], flavors [17], environments [18], forensic [19], cultural heritage [20], etc. It is mainly used for three tasks: identification, verification and quantification [21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

et al. 2022

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Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

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“…This is due to the low concentrations of polyphenols in the large compound matrix of the algae. 36,46,47 Furthermore, the diversity of seaweed polyphenols complicates data collection and standardisation of procedures. 9 Being able to quantify the polyphenolic content in crude seaweed materials with higher accuracy is important in order to fully explore seaweeds' potential applications.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing methods for polyphenolic analysis have significant shortcomings, such as lack of exactness at the molecular level. This is due to the low concentrations of polyphenols in the large compound matrix of the algae 36,46,47 . Furthermore, the diversity of seaweed polyphenols complicates data collection and standardisation of procedures 9 .…”

Section: Introductionmentioning

confidence: 99%

Advancing quantification methods for polyphenols in brown seaweeds—applying a selective qNMR method compared with the TPC assay

Wekre

Brunvoll

Jordheim

2022

Phytochemical Analysis

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Introduction: Brown seaweeds are a sustainable biomass with a potential for various industrial applications. Polyphenols are an important contributor to this potential.Objective: The aim was total quantification of polyphenols in brown seaweeds from different tidal zones, using a selective 1 H quantitative NMR (qNMR) method, comparing the results with the colorimetric Folin-Ciocalteu total phenolic content (TPC) assay.Method: qNMR was performed with integration of selected peaks in the aromatic region (7-5.5 ppm). Deselection of non-polyphenolic 1 H signals was based on information from 2D ( 1 H-13 C, 1 H-15 N) NMR spectra. 13 C NMR phlorotannin characterisation facilitated the average number of protons expected to be found per aromatic ring used for the 1 H quantification.Results: Selective qNMR and the TPC assay showed similar results for the three sublittoral growing species from the Laminariaceae; lower amounts for Laminaria hyperborea and Laminaria digitata (qNMR: 0.4%-0.6%; TPC: 0.6%-0.8%, phloroglucinol equivalents (PGE), dry weight (DW)) and higher amounts for Saccharina latissima (qNMR: 1.2%; TPC: 1.5%, PGE, DW). For the eulittoral Fucaceae, Fucus vesiculosus (qNMR: 1.1%; TPC: 4.1%; PGE, DW) and Ascophyllum nodosum (qNMR: 0.9%; TPC: 2.0%; PGE, DW), the TPC results were found to be up to three times higher than the qNMR results. The 13 C NMR characterisation showed the highest phlorotannin polymerisation degree for F. vesiculosus. Conclusion:The TPC assay provided similar polyphenolic amounts to the selective qNMR method for sublittoral species. For eulittoral growing species, the TPC method showed amounts up to three times higher than the qNMR method-most likely illustrating the lack of selectivity in the TPC assay.

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Research Progress of NMR in Natural Product Quantification

Cited by 27 publications

References 151 publications

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Advancing quantification methods for polyphenols in brown seaweeds—applying a selective qNMR method compared with the TPC assay

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Research Progress of NMR in Natural Product Quantification

Cited by 27 publications

References 151 publications

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides†

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides†

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Advancing quantification methods for polyphenols in brown seaweeds—applying a selective qNMR method compared with the TPC assay

Contact Info

Product

Resources

About

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†

Characterization and Quantification of Tryptophan‐ and Tyrosine‐derived Hydroperoxides^†