Investigation on the Behavior of Noise in Asynchronous Spectra in Generalized Two-Dimensional (2D) Correlation Spectroscopy and Application of Butterworth Filter in the Improvement of Signal-to-Noise Ratio of 2D Asynchronous Spectra

He, Aiyong; Zeng, Xianzhe; Xu, Yizhuang; Noda, Isao; Ozaki, Yukihiro; Wu, Jinguang

doi:10.1021/acs.jpca.7b06621

Cited by 21 publications

(19 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, n = 201. Two-dimensional correlation spectroscopy developed by Noda is a powerful spectral analysis method to analyze complex spectral intensity variation obtained successively under certain form of perturbation, such as temperature, pressure, or even concentration, and so on [12,33]. In two-dimensional correlation spectroscopy, a group of spectral data is transformed into a correlation intensity map defined by two independent spectral axes.…”

Section: Spectral Angle and Two-dimensional Correlation Spectroscopymentioning

confidence: 99%

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Hong

Chen

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM-based cluster method with known SM (classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R 2 ), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R 2 cv = 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM (RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

show abstract

Section: Spectral Angle and Two-dimensional Correlation Spectroscopymentioning

confidence: 99%

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Hong

Chen

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In our previous work, we demonstrated that the noise fluctuation could be reflected by the standard deviation of a 2D asynchronous spectrum at a given position in statistical analysis. 35 Herein, we adopt the following procedure to retrieve the value of standard deviation.…”

Section: Resultsmentioning

confidence: 99%

“…41 In our previous work, we used a Butterworth filter to suppress noise in 2D spectra. 35 In noise reduction approaches, an unavoidable problem is signal distortion, which might mislead the analysis of 2D spectra, which is why we emphasized that the balance should be maintained between noise reduction and suppression of signal distortion. In the second aspect, enhancement of the signal part can be realized with a modification of the approach to generate 2D spectra.…”

Section: Introductionmentioning

confidence: 98%

“…34 As demonstrated in our previous work, noise in 1D spectra is magnified in the generation of a 2D asynchronous spectrum. 35 The interference of noise is even exacerbated when an n D spectrum is constructed in the analysis of bilinear data from mixture containing multiple components. To improve the quality of 2D/ n D correlation spectra, a variety of approaches have been reported.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intensity Enhancement of a Two-Dimensional Asynchronous Spectrum Without Noise Level Fluctuation Escalation Using a One-Dimensional Spectra Sequence Change

Zhou

et al. 2020

Appl Spectrosc

Self Cite

View full text Add to dashboard Cite

Previously, we demonstrated that the intensities of cross peaks in a two-dimensional asynchronous spectrum could be enhanced via a change of the sequence of the corresponding 1D spectra. This unusual approach becomes useful when the determination of the sequential order of physicochemical events is not essential. However, we have not known whether the level of noise in the 2D asynchronous spectrum is also escalated as the sequence of 1D spectra is changed. Herein, we firstly investigate the noise behavior in a two-dimensional asynchronous spectrum upon changing the sequence of the corresponding 1D spectra on a model system. In the model system, bilinear data from a chromatographic-spectroscopic experiment on a mixture containing two components are analyzed via 2D asynchronous spectrum. The computer simulation results confirm that the cross peaks intensities in the resultant 2D asynchronous spectrum are indeed enhanced by more than100 times as the sequence of 1D spectra is changed, whereasthe fluctuation level of noise, reflected by the standard deviation of the value of a 2D asynchronous spectrum at a given point, is almost invariant. Further analysis on the model systems demonstrates that the special mathematical property of the Hilbert-Noda matrix (the modules of all the column vectors of the Hilbert-Noda matrix is near a constant) accounts for the moderate variation of the noise level during the changes of the sequence of 1D spectra. Secondly, a realistic example from a thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) experiment with added artificial noise in seven 1D spectra was studied. As we alter the sequence of the seven FTIR spectra, the variation of the intensities of cross peaks covers four orders of magnitudes in the 2D asynchronous spectra. In contrast, the fluctuation of noise in 2D asynchronous spectra is within two times. The above results clearly demonstrate that a change in the sequence of 1D spectra is an effective way to improve the signal-to-noise level of the 2D asynchronous spectra.

show abstract

“…In recent decades, many applications including studies on intermolecular interactions using 2D-COS have been reported. 20–36 We propose an orthogonal sample design scheme (OSD) 37 and relevant techniques. 38–41 In these approaches, interference portion in cross peaks, which has no relationship with intermolecular interaction, can be thoroughly eliminated via the product of orthogonal vectors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Two-Dimensional Correlation Spectroscopy to Determine the Stoichiometric Ratio of Two Substances Involved in Intermolecular Interactions

Zeng

Deng

et al. 2019

Appl Spectrosc

Self Cite

View full text Add to dashboard Cite

A novel technique called AOSD@Job’s, combining asynchronous orthogonal sample design scheme (AOSD) with Job’s method, is proposed to estimate the stoichiometric ratio of two substances that form a supramolecular aggregate under intermolecular interactions. First, a mathematical analysis was performed along with the procedure of the AOSD@Job’s method. Then, the validity of the AOSD@Job’s method was manifested by computer simulation on two model systems. Finally, the AOSD@Job’s method was applied on two real chemical systems. The stoichiometric ratio between the coordination complex of Ni2+ and ethylenediaminetetraacetic acid disodium salt (EDTA) was estimated to be 1.0. Benzyl alcohol (BA) and β-cyclodextrin (β-CD) were determined to form a 1 : 1 host–guest complex. These values were consistent with the values reported in the literature. Compared with the traditional Job’s method, the AOSD@Job’s method has an evident advantage since it is still valid even if all the peaks of the supramolecular aggregate severely overlap with the peaks of the substances that form the aggregate.

show abstract

Investigation on the Behavior of Noise in Asynchronous Spectra in Generalized Two-Dimensional (2D) Correlation Spectroscopy and Application of Butterworth Filter in the Improvement of Signal-to-Noise Ratio of 2D Asynchronous Spectra

Cited by 21 publications

References 49 publications

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Intensity Enhancement of a Two-Dimensional Asynchronous Spectrum Without Noise Level Fluctuation Escalation Using a One-Dimensional Spectra Sequence Change

A Novel Approach Based on Two-Dimensional Correlation Spectroscopy to Determine the Stoichiometric Ratio of Two Substances Involved in Intermolecular Interactions

Contact Info

Product

Resources

About