Recent progresses in two-dimensional correlation spectroscopy (2D-COS)

Park, Yeonju; Jin, Sila; Noda, Isao; Jung, Young Mee

doi:10.1016/j.molstruc.2018.04.099

Cited by 85 publications

(30 citation statements)

References 270 publications

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“…[27] 2DCoS can be used as a sensitive tool for determining phase transitions in organic compounds as well as for identifying fine spectral features associated with specific molecular conformation. [24,25] However, we were not able to find any literature data on the application of this approach to minerals.…”

Section: Introductionmentioning

confidence: 85%

“…A number of other approaches to the statistical analysis of vibrational spectral arrays are presented in the literature. There are several works devoted to Two-Dimensional Correlation Spectroscopy (2DCoS) (see, e.g., several studies [23][24][25][26] ). This well-established approach has been successfully applied in Raman, IR spectroscopy, X-ray diffraction, and so forth, when changing the external parameter.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of temperature‐dependent Raman spectra of minerals: Statistical approaches

Pankrushina

Kobuzov

Shchapova

et al. 2020

J Raman Spectroscopy

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The application of statistical methods to Raman spectroscopy is due to the need to analyse arrays of poorly resolved mineral spectra having low symmetry, large unit cells, and so forth. For the diagnosis of spectral changes under the influence of an external factor, as well as for the determination of critical values, statistical methods for the treatment of the spectrum profile provide more accuracy than a peak‐fitting procedure. The following algorithms for calculating statistical ξ parameters are used to parameterise I(ν,T) arrays of temperature‐dependent Raman spectra: Pearson correlation coefficient characterising the similarity/difference of signals; autocorrelation function, proposed earlier for estimating the weighted average width of spectral lines; and skewness and kurtosis, the parameters of the distribution of signal intensity. The ξ(T) relations were analysed for the synthetic and experimental spectra of natural zircon, titanite, and synthetic quartz in the range of T = 80–870 K. For zircon, the differences in the anharmonic behaviour of modes having various properties and symmetry are shown through the use of ξ(T). For quartz, at the temperatures preceding the displacive α–β phase transition, statistical analysis reveals the spectral region of soft modes, allowing their T‐behaviour to be analysed. For titanite, the critical temperature range corresponding to the displacive α–βγ phase transition is determined by differentiating ξ(T). The results show the potential of using a statistical approach for the rapid detection of spectral and temperature regions responding differently to an external effect, as well as for evaluating the critical values of an external parameter.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Analysis of temperature‐dependent Raman spectra of minerals: Statistical approaches

Pankrushina

Kobuzov

Shchapova

et al. 2020

J Raman Spectroscopy

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“…Another very versatile spectral analytical technique called 2D-COS can be used for sorting out key information in a broad range of spectroscopic signals such as Raman spectrum, nuclear magnetism, and UV-vis-NIR spectrum, among others [40]. In 2D-COS analysis, the raw spectral data were generated into synchronous and asynchronous correlation orthogonal representations.…”

Section: Selection Of Cwsmentioning

confidence: 99%

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Wang

Zhu

Zhang

et al. 2020

Foods

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The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

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“…Two-dimensional correlation spectroscopy (2D-COS) provides correlation maps spread between two independent variable axes. [130][131][132][133] For a spectral data set obtained under a certain perturbation, such as temperature, concentration, time, etc., generalized 2D-COS was proposed by Noda. 130 A Python program for generalized 2D-COS of 2Dpy 134 was developed by Morita and is available in GitHub.…”

Section: Othersmentioning

confidence: 99%

Chemometrics and Related Fields in Python

Morita

2019

ANAL. SCI.

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The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

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Recent progresses in two-dimensional correlation spectroscopy (2D-COS)

Cited by 85 publications

References 270 publications

Analysis of temperature‐dependent Raman spectra of minerals: Statistical approaches

Analysis of temperature‐dependent Raman spectra of minerals: Statistical approaches

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Chemometrics and Related Fields in Python

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