scholarly journals Analysis on the Development of Digital Economy in Guangdong Province Based on Improved Entropy Method and Multivariate Statistical Analysis

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1441
Author(s):  
Xue Deng ◽  
Yuying Liu ◽  
Ye Xiong
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Evaluation System ◽  
Guangdong Province ◽  
Digital Economy ◽  
Entropy Method ◽  
Multivariate Statistical ◽  
Comparison Results ◽  
The Future ◽  
The Difference

The lack of adequate indicators in the research of digital economy may lead to the shortage of data support on decision making for governments. To solve this problem, first we establish a digital economy indicator evaluation system by dividing the digital economy into four types: “basic type”, “technology type”, “integration type” and “service type” and select 5 indicators for each type. On this basis, the weight of each indicator is calculated to find the deficiencies in the development of some digital economic fields by the improved entropy method. By drawing on the empowerment idea of Analytic Hierarchy Process, the improved entropy method firstly compares the difference coefficient of indicators in pairs and maps the comparison results to the scales 1–9. Then, the judgment matrix is constructed based on the information entropy, which can solve as much as possible the problem that the difference among the weight of each indicator is too large in traditional entropy method. The results indicate that: the development of digital economy in Guangdong Province was relatively balanced from 2015 to 2018 and will be better in the future while the development of rural e-commerce in Guangdong Province is relatively backward, and there is an obvious digital gap between urban and rural areas. Next we extract two new variables respectively to replace the 20 indicators we select through principal component analysis and factor analysis methods in multivariate statistical analysis, which can retain the original information to the greatest extent and provide convenience for further research in the future. Finally, we and provide constructive comments of digital economy in Guangdong Province from 2015 to 2018.

scholarly journals UPLC-Q-TOF/MS Coupled with Multivariate Statistical Analysis as a Powerful Technique for Rapidly Exploring Potential Chemical Markers to Differentiate between Radix Paeoniae Alba and Radix Paeoniae Rubra

2013 ◽  
Vol 8 (4) ◽  
pp. 1934578X1300800 ◽  
Author(s):  
Nian-cui Luo ◽  
Wen Ding ◽  
Jing Wu ◽  
Da-wei Qian ◽  
Zhen-hao Li ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Ultra Performance Liquid Chromatography ◽  
Uv Spectra ◽  
Chemical Markers ◽  
Multivariate Statistical ◽  
Flight Mass Spectrometry ◽  
The Difference ◽  
Radix Paeoniae Alba ◽  
Tof Ms

To explore rapidly the potential chemical markers for differentiating Radix Paeoniae Alba and Radix Paeoniae Rubra, a method is proposed based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) coupled with multivariate statistical analysis. Batches of commercial samples were analyzed by UPLC-Q-TOF/MS. The datasets of tR-m/z pair, ion intensities and sample codes were further processed with orthogonal partial least squared discriminant analysis (OPLS-DA) to compare holistically the difference between these two kinds of samples. Then statistics were used to generate an S-plot, in which the variables (tR-m/z pair) contributing most to the difference were clearly depicted as points at the two ends of “S”, and the components correlated to these ions should be regarded as the chemical markers. The identities of the most changed markers can be identified by comparing the mass/UV spectra and retention times with those of reference compounds and/or tentatively assigned by matching empirical molecular formulae with those of known compounds published in the literature. Using this proposed approach, albflorin, paeoniflorin, oxypaeoniflorin, benzoylpaeoniflorin, galloylalbiflorin and paeoniflorigenone were found to be the differentiating components for discrimination of Radix Paeoniae Alba and Radix Paeoniae Rubra. Moreover, paeoniflorin sulfonate and its isomer, isomaltopaeoniflorin sulfonate, were found to be the characteristic markers for all Radix Paeoniae Alba samples that were processed by sulfurdioxide gas fumigation. The results suggested that this newly established approach could be used to explore rapidly the potential chemical markers for herbs with similar chemical characteristics.

Multivariate statistical analysis of elements content in snow cover in Blagoveshchensk

2018 ◽  
Vol 52 (2) ◽  
pp. 15
Author(s):  
V. I. Radomskaya ◽  
D. V. Yusupov ◽  
L. М. Pavlova ◽  
А. G. Sеrgееvа ◽  
N. А. Bоrоdinа ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Snow Cover ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Multivariate Statistical Analysis Regarding the Formulation of Oxicam-Based Pharmaceutical Hydrogels

2017 ◽  
Vol 68 (4) ◽  
pp. 726-731
Author(s):  
Lenuta Maria Suta ◽  
Anca Tudor ◽  
Colette Roxana Sandulovici ◽  
Lavinia Stelea ◽  
Daniel Hadaruga ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Pharmaceutical Formulations ◽  
Analysis Data ◽  
Multivariate Statistical ◽  
Predictive Variables ◽  
Rapid Release ◽  
Computational Data ◽  
Experimental Protocols ◽  
Formulation Factors

In this paper, it was analysed the influence of formulation factors over obtaining oxicam hydrogels, using the statistical analysis. Data analysis and predictive modeling by multivariate regression offers a large number of possible explanatory/predictive variables. Therefore, variable selection and dimension reduction is a major task for multivariate statistical analysis, especially for multivariate regressions. The statistical analysis and computational data processing of responses obtained from different pharmaceutical formulations, via different experimental protocols, lead to the optimization of the formulation process. It was found that the most suitable pharmaceutical formulations based on oxicams with the possibility of rapid release contained cyclodextrin, in particular 2-hydroxypropyl-b-cyclodextrin.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

scholarly journals Metabolomic-Based Comparison of Traditional and Industrial Doenjang Samples with Antioxidative Activities

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1377
Author(s):  
Song-Hui Soung ◽  
Sunmin Lee ◽  
Seung-Hwa Lee ◽  
Hae-Jin Kim ◽  
Na-Rae Lee ◽  
...  
Keyword(s):  
Amino Acids ◽  
Statistical Analysis ◽  
Organic Acids ◽  
Biogenic Amines ◽  
Multivariate Statistical Analysis ◽  
Metabolite Profiling ◽  
New Products ◽  
Multivariate Statistical ◽  
Fermentation Processes

Numerous varieties of doenjang are manufactured by many food companies using different ingredients and fermentation processes, and thus, the qualities such as taste and flavor are very different. Therefore, in this study, we compared many products, specifically, 19 traditional doenjang (TD) and 17 industrial doenjang (ID). Subsequently, we performed non-targeted metabolite profiling, and multivariate statistical analysis to discover distinct metabolites in two types of doenjang. Amino acids, organic acids, isoflavone aglycones, non-DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4- one) soyasaponins, hydroxyisoflavones, and biogenic amines were relatively abundant in TD. On the contrary, contents of dipeptides, lysophospholipids, isoflavone glucosides and DDMP-conjugated soyasaponin, precursors of the above-mentioned metabolites, were comparatively higher in ID. We also observed relatively higher antioxidant, protease, and β-glucosidase activities in TD. Our results may provide valuable information on doenjang to consumers and manufacturers, which can be used while selecting and developing new products.

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