Monsoon-driven Dynamics of water quality by multivariate statistical methods in Daya Bay, South China Sea

2012 ◽  
Vol 41 (4) ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Cui-Ci Sun ◽  
Fu-Lin Sun ◽  
Hao Cheng ◽  
...  
Keyword(s):  
Sustainable Use ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Daya Bay ◽  
Grouped Data ◽  
Northeast Monsoon ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Monsoon Period ◽  
Natural Processes

AbstractEleven physicochemical parameters of data collected from 12 stations in Daya Bay in 2003 were analyzed by multivariate statistical analysis. Cluster analysis (CA) grouped data from 4 seasons into two groups, the northeast and southwest monsoon periods, representing different natural processes. During the northeast monsoon period, principal component analysis (PCA) and CA group the 12 monitoring sites into Cluster DA1 (S1, S2 and S6) and Cluster DA2 (S3-S5 and S7-S12). During the southwest monsoon period, PCA and CA group the 12 monitoring sites into Cluster WB1 (S1, S2, S7, S9 and S11) and Cluster WB2 (S3-S6, S8, S10, S11 and S12). The spatial heterogeneity within the bay was defined by different hydrodynamic conditions and human activities. These results may be valuable for achieving sustainable use of the coastal ecosystems in Daya Bay.

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

Multivariate Techniques of Use in Biological Research

1969 ◽  
Vol 5 (1) ◽  
pp. 67-77 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Matrix Algebra ◽  
Principal Component ◽  
Component Analysis ◽  
Canonical Analysis ◽  
Biological Research ◽  
Multivariate Techniques ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

scholarly journals DAKSHIN BHARTIYA PADHAR PAR HONE WALI MONSOON WARSHA KA PRADHAN AVYAV VISHLESHAN

MAUSAM ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 77-82
Author(s):  
O.P. SINGH
Keyword(s):  
Principal Components ◽  
Andhra Pradesh ◽  
Monsoon Rainfall ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Northeast Monsoon Rainfall ◽  
Total Variability ◽  
Southwest Monsoon Rainfall ◽  
The Impact

 The result of the Principal Component Analysis of southwest and northeast monsoon rainfall on the southern India plateau have been discussed. Monsoon rainfall data of five meteorological sub-divisions, i.e., Coastal Andhra Pradesh, Rayalseema, Tamilnadu, Interior parts of South Karnataka & Kerala, for a period of 33 years (1960-92), have been utilized. The results indicate that the rainfall of Coastal Andhra Pradesh and Rayalseema has maximum impact on first principal component of southwest monsoon rainfall of five meteorological sub-divisions. The study of only first principal component is sufficient in order to understand the 49% of total variability of southwest monsoon rainfall. Analysis of first three principal components is important to understand 85% of total variability of the rainfall of this season.   On the first principal component of northeast monsoon rainfall of aforesaid five meteorological sub-divisions the impact of the rainfall of Kerala and south interior Karnataka has been found maximum. In order to understand the 56% of total variability the analysis of first principal component is sufficient.   The special negative relation is found between northeast monsoon rainfall on the Coastal Andhra Pradesh and southwest monsoon rainfall of previous year on this very sub-division and Rayalseema. The principal components of southwest monsoon rainfall may prove useful for forecasting the northeast monsoon rainfall of southern Indian plateau.  

Evaluation of colon cancer elements contents in serum using statistical methods

2007 ◽  
Vol 61 (5) ◽  
Author(s):  
D. Milde ◽  
J. Macháček ◽  
V. Stužka
Keyword(s):  
Statistical Methods ◽  
Tnm Classification ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Acid Mixture ◽  
Multivariate Statistical Methods ◽  
Serum Samples ◽  
Multivariate Statistical ◽  
Colorectal Cancer Patients

AbstractClassification of normal and different cancer groups (TNM classification) with univariate and multivariate statistical methods according to the contents of Cu, Fe, Mn, Se, and Zn in blood serum is discussed. All serum samples were digested by acid mixture in a microwave mineralization unit prior to the analysis by atomic absorption spectrometry. Results show that univariate methods can distinguish normal and cancer groups. Level of selenium evaluated as arithmetic mean with its standard deviation in colorectal cancer patients was (42.61 ± 23.76) µg L−1. Retransformed mean was used to evaluate levels of managanese (11.99 ± 1.71) µg L−1, copper (1.05 ± 0.06) mg L−1, zinc (2.14 ± 0.21) mg L−1, and iron (1.82 ± 0.22) mg L−1. Conclusions of multivariate statistical procedures (principal component analysis, hierarchical, and k-means clustering) do not correlate very well with the division of serum samples according to the TNM classification.

scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

scholarly journals Visualization of Chemical Space using Kernel Based Principal Component Research

2019 ◽  
Vol 8 (11S) ◽  
pp. 590-593
Keyword(s):  
Principal Component Analysis ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Non Linear ◽  
Hidden Patterns

Principal Component analysis (PCA) is one of the important and popular multivariate statistical methods applied over various data modeling applications. Traditional PCA handles linear variance in molecular descriptors or features. Handling complicated data by standard PCA will not be very helpful. This drawback can be handled by introducing kernel matrix over PCA. Kernel Principal Component Analysis (KPCA) is an extension of conventional PCA which handles non-linear hidden patterns exists in variables. It results in computational efficiency for data analysis and data visualization. In this paper, KPCA has been applied over dug-likeness dataset for visualization of non-linear relations exists in variables.

scholarly journals Rainfall Dynamics of Terengganu Malaysia and Its Recent Trends Analysis Using the Mann-Kendall Test.

2014 ◽  
Vol 4 (2) ◽  
pp. 372-381 ◽  
Author(s):  
Musa Garba Abdullahi ◽  
Mohd Ekhwan Toriman ◽  
Mohd Barzani Gasim ◽  
Hafizan Juahir
Keyword(s):  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Southwest Monsoon ◽  
The State ◽  
Northeast Monsoon ◽  
Monsoon Period ◽  
Mann Kendall Test ◽  
Recent Trends ◽  
Increasing Trends

This study investigated the pattern and trends of the daily rainfall data in Terengganu Malaysia based on seasonal rainfall indices. The statistics of rainfall indices were calculated in terms of their means for seven stations in Terengganu Malaysia for the period 2000 to 2012. The findings indicate that the trend in the study area has no significant changes in stations (1, 4 and 6) while station (2, 3, 5 and 7) shows significant changes and southwest monsoon had the greatest impact on the whole stations, particularly in characterizing the rainfall pattern of the area. During this season, the study area could be considered as the wettest region since all rainfall indices tested are higher than in other neighboring state of the Peninsula. Otherwise, the northwest of the area is denoted as the driest part of the state during the northeast monsoon period. The northwest of the state is less influenced by the northeast monsoon because of the existence of the Titiwangsa Range, which blocks some part of the region from receiving heavy rainfall. On the other hand, it is found that the areas with lowlands are strongly characterized by the northeast monsoonal flow.The results of the Mann-Kendall test, shows that, trends of the total amount of rainfall during the southwest monsoon decrease at some of the stations. The rainfall intensity increases in contrast, increasing trends in the total amount of rainfall were observed at three stations during the northeast monsoon, which give rise to the increasing trend of rainfall intensity. The results for the combined stations in both seasons indicate that there are no significant changes in trends during the extreme events for the Terengganu Malaysia. However, a smaller number of significant trends were found for extreme intensity. 

scholarly journals Use of multivariate statistical methods for classification of olive oil

2021 ◽  
Vol 6 (1) ◽  
pp. 035-043
Author(s):  
Moacyr Cunha Filho ◽  
Renisson Neponuceno Araujo Filho ◽  
Ana Luiza Xavier Cunha ◽  
Victor Casimiro Piscoya ◽  
Guilherme Rocha Moreira ◽  
...  
Keyword(s):  
Olive Oil ◽  
Statistical Methods ◽  
Virgin Olive Oil ◽  
Principal Component ◽  
Extra Virgin Olive Oil ◽  
Fuzzy Cluster ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fuzzy Clustering Method ◽  
Silhouette Index

Multivariate statistical methods can contribute significantly to classification studies of extra virgin and common olive oil groups. Therefore, nuclear magnetic resonance (NMR) was used to discriminate olive oil samples, multivariate statistical techniques Principal Component Analysis - PCA, Fuzzy Cluster, Silhouette Validation Method to describe and classify. The groups' distinction into organic and common was observed by applying the non-hierarchical Fuzzy grouping with a distinction between the two groups with a 65% confidence interval. The validation was performed by the silhouette index that presented S (i) of 0.73, which showed that the adopted grouping presented adequate strength and distinction criterion. However, PCA only analyzed the behaviors of data from extra virgin olive oil. Thus, the Fuzzy clustering method was the most suitable for classifying extra virgin olive oil.

scholarly journals Human resources as the precondition of the regional development

2007 ◽  
Vol 55 (6) ◽  
pp. 157-162 ◽  
Author(s):  
Libuše Svatošová
Keyword(s):  
Regional Development ◽  
Regional Policy ◽  
Principal Component ◽  
Human Potential ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Potential Development ◽  
Specific Factors ◽  
Development Evaluation ◽  
Regional Condition

The paper provides information of the possibilities of regional condition and development evaluation with use of multivariate statistical methods. Human potential is regional development´s cruicial factor. Analysis of the human potential development is of fundamental significance in decision-making the field of regional policy. Principal component analysis as principal metod is able to appreciate both gene­ral development trends common to all regions and specific factors´development in particular regions too.

scholarly journals Annual variations of carbonaceous PM<sub>2.5</sub> in Malaysia: influence by Indonesian peatland fires

2015 ◽  
Vol 15 (16) ◽  
pp. 22419-22449 ◽  
Author(s):  
Y. Fujii ◽  
S. Tohno ◽  
N. Amil ◽  
M. T. Latif ◽  
M. Oda ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Biomass Burning ◽  
Vanillic Acid ◽  
Monsoon Season ◽  
Chemical Species ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Mass Ratio ◽  
Southwest Monsoon Season

Abstract. In this study, we quantified carbonaceous PM2.5 in Malaysia through annual observations of PM2.5, focusing on organic compounds derived from biomass burning. We determined organic carbon (OC), elemental carbon (EC) and concentrations of solvent-extractable organic compounds (biomarkers derived from biomass burning sources and n-alkanes). We observed seasonal variations in the concentrations of pyrolyzed OC (OP), levoglucosan (LG), mannosan (MN), galactosan, syringaldehyde, vanillic acid (VA) and cholesterol. The average concentrations of OP, LG, MN, galactosan, VA and cholesterol were higher during the southwest monsoon season (June–September) than during the northeast monsoon season (December–March), and these differences were statistically significant. Conversely, the syringaldehyde concentration during the southwest monsoon season was lower. The PM2.5 OP/OC4 mass ratio allowed distinguishing the seven samples, which have been affected by the Indonesian peatland fires (IPFs). In addition, we observed significant differences in the concentrations between the IPF and other samples of many chemical species. Thus, the chemical characteristics of PM2.5 in Malaysia appeared to be significantly influenced by IPFs during the southwest monsoon season. Furthermore, we evaluated two indicators, the vanillic acid/syringic acid (VA/SA) and LG/MN mass ratios, which have been suggested as indicators of IPFs. The LG/MN mass ratio ranged from 14 to 22 in the IPF samples and from 11 to 31 in the other samples. Thus, the respective variation ranges partially overlapped. Consequently, this ratio did not satisfactorily reflect the effects of IPFs in Malaysia. In contrast, the VA/SA mass ratio may serve as a good indicator, since it significantly differed between the IPF and other samples. However, the OP/OC4 mass ratio provided more remarkable differences than the VA/SA mass ratio, offering an even better indicator. Finally, we extracted biomass burning emissions' sources such as IPF, softwood/hardwood burning and meat cooking through varimax-rotated principal component analysis.

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