Principal component and multivariate statistical approach for evaluation of hydrochemical characterization of fluoride-rich groundwater of Shaslar Vagu watershed, Nalgonda District, India

2017 ◽  
Vol 10 (4) ◽  
Author(s):  
M. Sudheer Kumar ◽  
Ratnakar Dhakate ◽  
G. Yadagiri ◽  
K. Srinivasa Reddy
Keyword(s):  
Statistical Approach ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Multivariate Statistical Approach ◽  
Nalgonda District ◽  
Hydrochemical Characterization

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.

Geochemical characterization of ophiolitic soils in a temperate climate: A multivariate statistical approach

Geoderma ◽  
1997 ◽  
Vol 75 (1-2) ◽  
pp. 117-133 ◽  
Author(s):  
O. Vaselli ◽  
A. Buccianti ◽  
C. De Siena ◽  
C. Bini ◽  
N. Coradossi ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Temperate Climate ◽  
Multivariate Statistical ◽  
Geochemical Characterization ◽  
Multivariate Statistical Approach

scholarly journals NMR, RP-HPLC-PDA-ESI-MSn, and RP-HPLC-FD Characterization of Green and Oolong Teas (Camellia sinensis L.)

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5125
Author(s):  
Anatoly P. Sobolev ◽  
Arianna Di Lorenzo ◽  
Simone Circi ◽  
Cristina Santarcangelo ◽  
Cinzia Ingallina ◽  
...  
Keyword(s):  
Metabolic Profiling ◽  
Statistical Approach ◽  
Gaba Level ◽  
Bioactive Components ◽  
Multivariate Statistical ◽  
Nmr Data ◽  
Rp Hplc ◽  
Multivariate Statistical Approach ◽  
Gaba Content

Untargeted (NMR) and targeted (RP-HPLC-PDA-ESI-MSn, RP-HPLC-FD) analytical methodologies were used to determine the bioactive components of 19 tea samples, characterized by different production processes (common tea and GABA tea), degrees of fermentation (green and oolong teas), and harvesting season (autumn and spring). The combination of NMR data and a multivariate statistical approach led to a statistical model able to discriminate between GABA and non-GABA teas and green and oolong teas. Targeted analyses showed that green and GABA green teas had similar polyphenol and caffeine contents, but the GABA level was higher in GABA green teas than in regular green tea samples. GABA oolong teas showed lower contents of polyphenols, caffeine, and amino acids, and a higher content of GABA, in comparison with non-GABA oolong teas. In conclusion, the results of this study suggest that the healthy properties of teas, especially GABA teas, have to be evaluated via comprehensive metabolic profiling rather than only the GABA content.

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