Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

Harrington, Peter de B.; Wang, Xinyi

doi:10.1007/s41664-017-0003-y

Cited by 11 publications

(14 citation statements)

References 25 publications

(22 reference statements)

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“…The data comprised 60 spectra with 1000 chemical shift measurements from 0.5 to 7.0 ppm were used. This dataset used the magnitude spectrum as opposed to the real spectrum from the free induction decay because it is superior for pattern recognition . Because the GRBM and CRBM performed similarly as in the other studies, their performance was excluded from this paper.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…The proton NMR measurements were performed on a Bruker Avance III HD and Bruker Ascend 500‐MHz NMR spectrometer (Bruker BioSpin AG, Fällanden, Switzerland) equipped with a 5‐mm broadband multinuclear (PABBO) probe. Proton NMR spectra were acquired at 298.0 K. Sixteen scans and two prior dummy scans of 65 536 spectral measurements were acquired with a spectral range of 19.9923 ppm that took less than 2 minutes …”

Section: Methodsmentioning

confidence: 99%

“…Proton NMR spectra were acquired at 298.0 K. Sixteen scans and two prior dummy scans of 65 536 spectral measurements were acquired with a spectral range of 19.9923 ppm that took less than 2 minutes. 18 For all spectra, the only preprocessing that was used was to normalize the spectra to unit vector length, ie, 2-norm.…”

Section: Methodsmentioning

confidence: 99%

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Enhanced zippy restricted Boltzmann machine for feature expansion and improved classification of analytical data

Harrington

2020

Journal of Chemometrics

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Restricted Boltzmann machines (RBMs) are components found in many deep learning algorithms. RBMs originally were designed for binary image data. Some advances in RBM algorithms have been made so that they may accept real‐valued inputs that are typical for analytical chemistry measurements. However, these algorithms are difficult to train and require fine‐tuning of the parameters. The RBM algorithm was modified to furnish the enhanced zippy RBM (EZRBM) that trains reliably and robustly with respect to the parameters. In addition, feature augmentation (ie, fusing the RBM linear inputs and nonlinear outputs) improves the classification rate while reducing the dependence of the RBM training parameters. Two different classifiers were used, the support vector classifier (SVC) and super partial least squares–discriminant analysis (sPLS‐DA), to evaluate the performance. Classifiers built from the EZRBM outputs performed better than those built from the continuous RBM and the Gaussian RBM (GRBM) outputs when validated using 100 bootstraps with two Latin partitions. Three datasets were used. The first was an overdetermined set of eight fatty acid concentrations for 572 olive oils from nine regions of Italy. The second was 75 UV spectra of 15 Cannabis extracts with 101 measurements made from 200 to 400 nm. The third set comprised 60 proton nuclear magnetic resonance (NMR) spectra of 12 tea extracts that had 1000 chemical shift measurements from 0.5 to 7.0 ppm. In every evaluation, the augmented EZRBM had better classification performance than the classifiers without the RBM. The classifiers built with EZRBM outperformed the other RBM algorithms except for a single instance. Recently, RBMs have been considered a transform into a dual feature space.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Enhanced zippy restricted Boltzmann machine for feature expansion and improved classification of analytical data

Harrington

2020

Journal of Chemometrics

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“…The use of the magnitude spectrum showed good reproducibility in the analysis of 4 diverse natural product samples (12 tea extracts, 8 liquor samples, 9 hops extracts, and 25 cannabis extracts) using 1D HNMR at 500-MHz and various statistical tools [45].…”

Section: One-dimensional 1h Nmrmentioning

confidence: 99%

“…For the application of 1H NMR for pattern recognition, the use of the magnitude spectrum has been suggested [34]. The standard 1H NMR spectrum utilizes the phase-corrected real component of the Fourier transform of the free induction decay (FID), discarding the imaginary component.…”

Section: Sensitivity and Dynamic Rangementioning

confidence: 99%

1H and 13C NMR for the Profiling of Natural Product Extracts: Theory and Applications

Dayrit¹,

Dios²

2017

Spectroscopic Analyses - Developments and Applications

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Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the principal methods of metabolomics, the branch of '-omics' that deals with small molecules. Although MS is gaining popularity in metabolomics, NMR enjoys a number of key advantages because it is nondestructive, unbiased, quantitative, does not require separation or derivatization, and is amenable to compounds that are difficult to analyze by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). There are two general approaches to the use of NMR for profiling studies: an untargeted approach, which uses chemometric analysis; and a targeted approach, which aims to quantify known compounds in the extract. These approaches, however, are not mutually exclusive and will likely converge in the future. This paper will describe the basic theoretical principles that should be considered to develop NMR into a standard quantitative method. Although 1H NMR is more sensitive, 13C NMR spectra are simpler with less overlapping signals and are less affected by different magnetic field strengths. Various applications of 1H and 13C NMR for the profiling of natural products are described. The use of two-dimensional 1H NMR has been used to overcome problems of spectral overlap. The standardization of the NMR protocol will make it a more useful tool for the profiling of natural products extracts.

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Graphical exploration of 600‐ and 60‐MHz proton NMR spectral datasets from ground roast coffee extracts

Kemsley

2023

Magnetic Reson in Chemistry

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This article uses a variety of graphical and mathematical approaches to analyse 600‐ and 60‐MHz (‘benchtop’) proton NMR spectra acquired from lipophilic and hydrophilic extracts of roasted coffee beans. The collection of 40 authenticated samples comprised various coffee species, cultivars and hybrids. The spectral datasets were analysed by a combination of metabolomics approaches, cross‐correlation and whole spectrum methods, assisted by visualisation and mathematical techniques not conventionally employed to treat NMR data. A large amount of information content was shared between the 600‐MHz and benchtop datasets, including in its magnitude spectral form, suggesting the potential for a lower cost, lower tech route to conducting informative metabolomics studies.

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Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

Cited by 11 publications

References 25 publications

Enhanced zippy restricted Boltzmann machine for feature expansion and improved classification of analytical data

Enhanced zippy restricted Boltzmann machine for feature expansion and improved classification of analytical data

1H and 13C NMR for the Profiling of Natural Product Extracts: Theory and Applications

Graphical exploration of 600‐ and 60‐MHz proton NMR spectral datasets from ground roast coffee extracts

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