Double two-dimensional correlation analysis – 2D correlation of 2D spectra

Noda, Isao

doi:10.1016/j.molstruc.2009.11.048

Cited by 21 publications

(7 citation statements)

References 13 publications

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“…For this reason, it is a powerful tool to investigate and describe qualitative and structural characteristics of molecular systems such as polymers. A further development of 2DCORR analysis is D2DCORR, which can provide higher spectral resolution than 2DCORR (Noda 2010). In our study, we elaborated the asynchronous maps by applying D2DCORR: asynchronous spectra have the distinctive ability to describe differences in crystallinity and orientation of chemical bonds in polymeric structures (Noda and Ozaki 2004;Chen et al 2012), such as starchy foods.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Rheological Properties of Four Italian Rice Samples and Starch Thereof by RVA and FTIR Spectroscopy Supported by Double Two‐Dimensional Correlation Analysis: Evidence of Lipid–Carbohydrate Interactions

et al. 2016

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Cereal Chem. 93(5): [456][457][458][459][460][461][462][463][464] In this paper four rice samples and purified starch thereof characterized by different genetic background, amylose content, and gelatinization behavior were considered. They were examined with respect to chemical composition and pasting properties. To explain differences observed in the Rapid Visco Analyzer (RVA) curves, Fourier transform infrared spectroscopy (FTIR) was also used to characterize the rice and starch samples. Double two-dimensional correlation analysis (D2DCORR), a powerful chemometric tool not commonly used in food analysis, was †

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Section: Resultsmentioning

confidence: 99%

Evaluation of Rheological Properties of Four Italian Rice Samples and Starch Thereof by RVA and FTIR Spectroscopy Supported by Double Two‐Dimensional Correlation Analysis: Evidence of Lipid–Carbohydrate Interactions

et al. 2016

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“…For more information on the theory of 2D correlation spectroscopy, the calculations, scaling techniques and the influence of noise the reader is referred to the literature (Noda 1990(Noda , 2006(Noda , 2016aŠašić, Muszynski, and Ozaki 2001;Yu, Liu, and Noda 2003;Noda 2008). For techniques derived from the basic 2D correlation spectroscopy like sample-sample 2D correlation spectroscopy, moving window 2D correlation spectroscopy, multiple-perturbation 2D correlation spectroscopy, double 2D correlation spectroscopy, 2D codistribution spectroscopy and quadrature 2D correlation spectroscopy the reader is also referred to the literature (Šašić, Muszynski, and Ozaki 2000a,b;Thomas and Richardson 2000;Shinzawa et al 2009;Noda 2010Noda , 2014aNoda , 2016b.…”

Section: Corr2d: Implementation Of Two-dimensional Correlation Analysmentioning

confidence: 99%

corr2D: Implementation of Two-Dimensional Correlation Analysis in R

Geitner¹,

Fritzsch²,

Popp³

et al. 2019

J. Stat. Soft.

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In the package corr2D two-dimensional correlation analysis is implemented in R. This paper describes how two-dimensional correlation analysis is done in the package and how the mathematical equations are translated into R code. The paper features a simple tutorial with executable code for beginners, insight into the calculations done before the correlation analysis, a detailed look at the parallelization of the fast Fourier transformation based correlation analysis and a speed test of the calculation. The package corr2D offers the possibility to preprocess, correlate and postprocess spectroscopic data using exclusively the R language. Thus, corr2D is a welcome addition to the toolbox of spectroscopists and makes two-dimensional correlation analysis more accessible and transparent.

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“…For more information on the theory of 2D correlation spectroscopy, the calculations, scaling techniques and the influence of noise the reader is referred to the literature (Noda 1990(Noda , 2006(Noda , 2016aŠašić et al 2001;Yu et al 2003;Noda 2008). For techniques derived from the basic 2D correlation spectroscopy like sample-sample 2D correlation spectroscopy, moving window 2D correlation spectroscopy, multiple-perturbation 2D correlation spectroscopy, double 2D correlation spectroscopy, 2D codistribution spectroscopy and quadrature 2D correlation spectroscopy the reader is also referred to the literature ( Šašić et al 2000a,b;Thomas and Richardson 2000;Shinzawa et al 2009;Noda 2010Noda , 2014aNoda , 2016b.…”

Section: Theoretical Description Of 2d Correlation Spectroscopymentioning

confidence: 99%

corr2D - Implementation of Two-Dimensional Correlation Analysis in R

Geitner,

Fritzsch,

Popp

et al. 2018

Preprint

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In the package corr2D two-dimensional correlation analysis is implemented in R. This paper describes how two-dimensional correlation analysis is done in the package and how the mathematical equations are translated into R code. The paper features a simple tutorial with executable code for beginners, insight into at the calculations done before the correlation analysis, a detailed look at the parallelization of the fast Fourier transformation based correlation analysis and a speed test of the calculation. The package corr2D offers the possibility to preprocess, correlate and postprocess spectroscopic data using exclusively the R language. Thus, corr2D is a welcome addition to the toolbox of spectroscopists and makes two-dimensional correlation analysis more accessible and transparent.

show abstract

Double two-dimensional correlation analysis – 2D correlation of 2D spectra

Cited by 21 publications

References 13 publications

Evaluation of Rheological Properties of Four Italian Rice Samples and Starch Thereof by RVA and FTIR Spectroscopy Supported by Double Two‐Dimensional Correlation Analysis: Evidence of Lipid–Carbohydrate Interactions

Evaluation of Rheological Properties of Four Italian Rice Samples and Starch Thereof by RVA and FTIR Spectroscopy Supported by Double Two‐Dimensional Correlation Analysis: Evidence of Lipid–Carbohydrate Interactions

corr2D: Implementation of Two-Dimensional Correlation Analysis in R

corr2D - Implementation of Two-Dimensional Correlation Analysis in R

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