Quality Assessment and Classification of Goji Berry by an HPLC-based Analytical Platform Coupled with Multivariate Statistical Analysis

2020 ◽  
Vol 13 (12) ◽  
pp. 2222-2237
Author(s):  
Xuxia Liu ◽  
Han Wang ◽  
Xinyi Huang ◽  
Mei Guo ◽  
Zhigang Yang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Quality Assessment ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Goji Berry

Rapid Quality Assessment of Wheat Cultivars Registered in Poland Using the 2-g Mixograph and Multivariate Statistical Analysis

2005 ◽  
Vol 82 (2) ◽  
pp. 182-186 ◽  
Author(s):  
B. J. Dobraszczyk ◽  
B. P. Salmanowicz ◽  
B. Ługowska ◽  
J. Chełkowski
Keyword(s):  
Statistical Analysis ◽  
Quality Assessment ◽  
Multivariate Statistical Analysis ◽  
Wheat Cultivars ◽  
Multivariate Statistical

A GIS-Based Spatial-Multivariate Statistical Analysis of Water Quality Data in the Porsuk River, Turkey

2009 ◽  
Vol 44 (3) ◽  
pp. 279-293 ◽  
Author(s):  
Ozan Arslan
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Multivariate Statistical Analysis ◽  
Water Quality Assessment ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Porsuk River

Abstract The study offers a GIS-based multivariate statistical analysis strategy to assess river water quality. Multivariate statistical methods and Geographic Information System (GIS) technology have effectively been used for water quality management. Recognizing the fact that the use of standard statistical methods can be restrictive due to the complexity of water quality datasets, geospatial statistical methods have been recommended for the water quality assessment. The objective of the study was to explore the potential capabilities of GIS-based joint multivariate statistical analysis for water quality assessment of Porsuk River in Turkey. A well-known multivariate statistical technique, principal component analysis (PCA), is incorporated into a geographic database for interpretation of water quality data. To characterize spatial variability of water quality data, spatial PCA was performed on the basis of spatial autocorrelation. Application of the joint spatio-multivariate statistical analysis for interpretation of the water quality database offered a better understanding of the hydrochemistry in the study region.

Multivariate statistical analysis for the classification of oak-aged wines based on their chromatic characteristics

2003 ◽  
Vol 217 (6) ◽  
pp. 512-516 ◽  
Author(s):  
Luis Javier Perez-Prieto ◽  
Jose Mar�a L�pez-Roca ◽  
Encarna G�mez-Plaza
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical
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