Color Quality of Pigments in Cochineals (Dactylopius coccusCosta). Geographical Origin Characterization Using Multivariate Statistical Analysis

2004 ◽  
Vol 52 (5) ◽  
pp. 1331-1337 ◽  
Author(s):  
Jesús Méndez ◽  
Mónica González ◽  
M. Gloria Lobo ◽  
Aurelio Carnero
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Geographical Origin ◽  
Multivariate Statistical ◽  
Color Quality

scholarly journals Assessing water quality of Nen River, the neighboring section of three provinces, using multivariate statistical analysis

2018 ◽  
Vol 67 (8) ◽  
pp. 779-789 ◽  
Author(s):  
Duoying Zhang ◽  
Guochen Zheng ◽  
Shufeng Zheng ◽  
Wenbo Guan ◽  
Wenjun Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index

2017 ◽  
Vol 189 (9) ◽  
Author(s):  
Shahla Soltani ◽  
Asghar Asghari Moghaddam ◽  
Rahim Barzegar ◽  
Naeimeh Kazemian ◽  
Evangelos Tziritis
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Nw Iran

scholarly journals Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mansoor A. Baluch ◽  
Hashim Nisar Hashmi
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quality Parameters ◽  
Anthropogenic Source ◽  
Indus River ◽  
Multivariate Statistical ◽  
Quality Of Water ◽  
The Impact

Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

scholarly journals Correlation between grain quality of foxtail millet ( Setaria italica [L.] P. Beauv.) and environmental factors on multivariate statistical analysis

2017 ◽  
Vol 77 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Na Ning ◽  
Yan-jun Yang ◽  
Jian-ping Hong ◽  
Xiang-yang Yuan ◽  
Xi-e Song ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Environmental Factors ◽  
Multivariate Statistical Analysis ◽  
Grain Quality ◽  
Foxtail Millet ◽  
Setaria Italica ◽  
Multivariate Statistical

scholarly journals Multivariate statistical analysis of the quality of apple juice to integrate and simplify juice industrial production technologies

2018 ◽  
Vol 16 (1) ◽  
pp. 190-198 ◽  
Author(s):  
You Tian ◽  
Lijun Sun ◽  
Nisar Tanzeela ◽  
Di Liang ◽  
Xiaoju Gou ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Industrial Production ◽  
Multivariate Statistical Analysis ◽  
Apple Juice ◽  
Multivariate Statistical ◽  
Production Technologies

Multivariate statistical analysis based on a chromatographic fingerprint for the evaluation of important environmental factors that affect the quality of Angelica sinensis

2014 ◽  
Vol 6 (20) ◽  
pp. 8268-8276 ◽  
Author(s):  
Xin-Yue Song ◽  
Ling Jin ◽  
Yan-Ping Shi ◽  
Ying-Dong Li ◽  
Juan Chen
Keyword(s):  
Statistical Analysis ◽  
Environmental Factors ◽  
Multivariate Statistical Analysis ◽  
Angelica Sinensis ◽  
Optimum Level ◽  
Chromatographic Fingerprint ◽  
Multivariate Statistical ◽  
Holistic Strategy

A holistic strategy combining chromatographic fingerprint and multivariable statistical analysis was developed to evaluate the effects of environmental factors on the quality of A. sinensis and choose the optimum level.

scholarly journals Discovery of Quality Markers of Nucleobases, Nucleosides, Nucleotides and Amino Acids for Chrysanthemi Flos From Different Geographical Origins Using UPLC–MS/MS Combined With Multivariate Statistical Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiangwei Chang ◽  
Zhenyu Zhang ◽  
Hui Yan ◽  
Shulan Su ◽  
Dandan Wei ◽  
...  
Keyword(s):  
Amino Acids ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Ion Trap ◽  
Ultra Performance Liquid Chromatography ◽  
Support Vector ◽  
Multivariate Statistical ◽  
Quality Markers ◽  
Chrysanthemi Flos

Nucleobases, nucleosides, nucleotides and amino acids, as crucial nutrient compositions, play essential roles in determining the flavor, function and quality of Chrysanthemi Flos. The quality of Chrysanthemi Flos from different geographical origins is uneven, but there have been no reports about the screening of their quality markers based on nutritional ingredients. Here, we developed a comprehensive strategy integrating ultra performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UPLC–MS/MS) and multivariate statistical analysis to explore quality markers of Chrysanthemi Flos from different geographical origins and conduct quality evaluation and discrimination of them. Firstly, a fast, sensitive, and reliable UPLC–MS/MS method was established for simultaneous quantification 28 nucleobases, nucleosides, nucleotides and amino acids of Chrysanthemi Flos from nine different regions in China. The results demonstrated that Chrysanthemi Flos from nine different cultivation regions were rich in the above 28 nutritional contents and their contents were obvious different; however, correlation analysis showed that altitude was not the main factor for these differences, which required further investigation. Subsequently, eight crucial quality markers for nine different geographical origins of Chrysanthemi Flos, namely, 2′-deoxyadenosine, guanosine, adenosine 3′,5′-cyclic phosphate (cAMP), guanosine 3′,5′-cyclic monophosphate (cGMP), arginine, proline, glutamate and tryptophan, were screened for the first time using partial least squares discriminant analysis (PLS-DA) and the plot of variable importance for projection (VIP). Moreover, a hierarchical clustering analysis heat map was employed to intuitively clarify the distribution of eight quality markers in the nine different regions of Chrysanthemi Flos. Finally, based on the contents of selected eight quality markers, support vector machines (SVM) model was established to predict the geographical origins of Chrysanthemi Flos, which yielded excellent prediction performance with an average prediction accuracy of 100%. Taken together, the proposed strategy was suitable to discover the quality markers of Chrysanthemi Flos and could be used to discriminate its geographical origin.

Sign in / Sign up

Export Citation Format

Share Document