Multivariate statistical approach to a data set of dioxin and furan contaminations in human milk

1988 ◽  
Vol 40 (5) ◽  
pp. 641-646 ◽  
Author(s):  
Gunilla U. M. Lindström ◽  
Michael Sjöström ◽  
Stephen E. Swanson ◽  
Peter Fürst ◽  
Christiane Krüger ◽  
...  
Keyword(s):  
Human Milk ◽  
Statistical Approach ◽  
Multivariate Statistical ◽  
Data Set ◽  
Multivariate Statistical Approach

scholarly journals The 3Rs: Reduction and refinement through a multivariate statistical analysis approach in a behavioural study to unveil anxiolytic effects of natural extracts of Tilia tomentosa

2020 ◽  
Author(s):  
Guendalina Olivero ◽  
Federica Turrini ◽  
Matteo Vergassola ◽  
Raffaella Boggia ◽  
Paola Zunin ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Principal Component ◽  
Experimental Protocol ◽  
Multivariate Statistical ◽  
Data Set ◽  
Natural Extracts ◽  
Multivariate Statistical Approach ◽  
Behavioural Tests ◽  
Tilia Tomentosa

We propose a multivariate statistical approach based on Principal Component Analysis (PCA) as an useful instrument to improve the Rules of Refinement and Reduction in in vivo animal experimentation. We analysed with PCA the preliminary data from a study on the effects of the oral administration of Tilia tomentosa bud extracts (TTBEs) on the behavioural skills of adult and aged male and female mice. PCA allows to rationalize the data set information and to dissect the results, showing connections among variables under study (behavioural parameters) and different trends in the experimental groups (control and TTBEs-administered animals). Our results show that PCA can give some important information that can be useful for the refinement of the experimental protocol, in order to reduce the number of the animals used in the experiments and/or the behavioural tests to get reliable information.

scholarly journals Assessment of Near-Bottom Water Quality of Southwestern Coast of Sarawak, Borneo, Malaysia: A Multivariate Statistical Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Chen-Lin Soo ◽  
Cheng-Ann Chen ◽  
Shabdin Mohd-Long
Keyword(s):  
Water Quality ◽  
Spatial Variation ◽  
Coastal Water ◽  
Statistical Approach ◽  
Land Use Patterns ◽  
Multivariate Statistical ◽  
Data Set ◽  
Coastal Water Quality ◽  
Guideline Value ◽  
Multivariate Statistical Approach

The study on Sarawak coastal water quality is scarce, not to mention the application of the multivariate statistical approach to investigate the spatial variation of water quality and to identify the pollution source in Sarawak coastal water. Hence, the present study aimed to evaluate the spatial variation of water quality along the coastline of the southwestern region of Sarawak using multivariate statistical techniques. Seventeen physicochemical parameters were measured at 11 stations along the coastline with approximately 225 km length. The coastal water quality showed spatial heterogeneity where the cluster analysis grouped the 11 stations into four different clusters. Deterioration in coastal water quality has been observed in different regions of Sarawak corresponding to land use patterns in the region. Nevertheless, nitrate-nitrogen exceeded the guideline value at all sampling stations along the coastline. The principal component analysis (PCA) has determined a reduced number of five principal components that explained 89.0% of the data set variance. The first PC indicated that the nutrients were the dominant polluting factors, which is attributed to the domestic, agricultural, and aquaculture activities, followed by the suspended solids in the second PC which are related to the logging activities.

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