Chemometric Analysis of NMR Spectroscopy Data: A Review

Alam, Todd M.; Alam, Mary Kathleen

doi:10.1016/s0066-4103(04)54002-4

Cited by 46 publications

(35 citation statements)

References 111 publications

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“…6b). An alternative approach exists, known mostly from infrared and ultraviolet spectroscopy but also from NMR spectroscopy (Alam & Alam, 2005), which is classical least-squares (CLS) regression. CLS is not bound to a particular time-dependent functional form for contributions to the CPMG decay.…”

Section: Resultsmentioning

confidence: 99%

Fat and moisture content determination with unilateral NMR

Petrov

Hay

Mastikhin

et al. 2008

Food Research International

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Section: Resultsmentioning

confidence: 99%

Fat and moisture content determination with unilateral NMR

Petrov

Hay

Mastikhin

et al. 2008

Food Research International

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“…The precision of the algorithms, regarding the cross-validation error, was defined by the difference between the values of root mean square error of cross-validation (RMSECV) obtained from each assay, according to Equation (6), The comparison of cross-validation results between algorithms i and j was quantified by…”

Section: Real Data Setsmentioning

confidence: 99%

“…Data matrices can be very large in distinct applications of multivariate calibration, e.g. in QSAR/QSPR (Quantitative Structure Activity/Property Relationship) studies [3], data mining [4], near infrared spectroscopy (NIR) [5], nuclear magnetic resonance (NMR) [6], chromatography [7] and studies dealing with unfolded matrices from multiway data [8], among others [9]. In such cases, the response variables are by their nature highly correlated to each other, leading to ill-conditioned X matrices.…”

Section: Introductionmentioning

confidence: 99%

Computational performance and cross‐validation error precision of five PLS algorithms using designed and real data sets

Martins

Teófilo

Ferreira

2010

Journal of Chemometrics

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An evaluation of computational performance and precision regarding the cross-validation error of five partial least squares (PLS) algorithms (NIPALS, modified NIPALS, Kernel, SIMPLS and bidiagonal PLS), available and widely used in the literature, is presented. When dealing with large data sets, computational time is an important issue, mainly in cross-validation and variable selection. In the present paper, the PLS algorithms are compared in terms of the run time and the relative error in the precision obtained when performing leave-one-out cross-validation using simulated and real data sets. The simulated data sets were investigated through factorial and Latin square experimental designs. The evaluations were based on the number of rows, the number of columns and the number of latent variables. With respect to their performance, the results for both simulated and real data sets have shown that the differences in run time are statistically different. PLS bidiagonal is the fastest algorithm, followed by Kernel and SIMPLS. Regarding cross-validation error, all algorithms showed similar results. However, in some situations as, for example, when many latent variables were in question, discrepancies were observed, especially with respect to SIMPLS.

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“…The different supervised methods have been utilised in the chemometric analysis of NMR data for classification and/or quantization [68], such as principal components regression, partial least squares (PLS) [69], soft independent modelling of class analogies (SIMCA) [70], linear discriminant analysis and K nearest neighbours. Recent examples include the utilisation of Bayesian methods for class recognition, wavelet transforms, artificial neural networks (ANN) in biomedical NMR and PR [71] plus genetic and evolutionary computing [72].…”

Section: Multivariable Data Analysismentioning

confidence: 99%

“…Cavill et al developed a GA approach that simultaneously selects sets of samples and spectral regions from the COMET database to build robust, predictive classifiers of liver and kidney toxicity [80]. The results indicate that using simultaneous selection of samples and variables improved performance by more than 9% [68] compared with either method alone. Simultaneous selection also halved computation time.…”

Section: Multivariable Data Analysismentioning

confidence: 99%

NMR-based metabonomics: a useful platform of oncology research

2010

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Cancer threatens human health, thus research focusing on oncology has great significance. Metabonomics is the global quantitative assessment of the dynamic metabolic response of a biological system to some exogenous or genetic pathophysiological perturbation. The metabolites are detected in tissues or fluids by various analytical methods, such as nuclear magnetic resonance (NMR) and mass spectroscopy. Metabonomics, as a tool, can provide a link between the laboratory and clinic. NMRbased metabonomics offers a useful tool to understand tumour biochemistry and may also has some potentials for tumour diagnosis and prognosis, even when some other disease processes are present. Here, we review NMR-based metabonomics principles and their applications in oncology research.

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Chemometric Analysis of NMR Spectroscopy Data: A Review

Cited by 46 publications

References 111 publications

Fat and moisture content determination with unilateral NMR

Fat and moisture content determination with unilateral NMR

Computational performance and cross‐validation error precision of five PLS algorithms using designed and real data sets

NMR-based metabonomics: a useful platform of oncology research

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