scholarly journals An Evaluation of the Chemical Composition of Soft Drinks in Nigeria: A Principal Component Analysis Approach

2020 ◽  
Vol 57 (1-4) ◽  
pp. 14-21
Author(s):  
Samuel Olorunfemi Adams ◽  
Rafiu Olayinka Akano ◽  
Rauf Ibrahim Rauf
Keyword(s):  
Principal Component Analysis ◽  
Chemical Composition ◽  
Sugar Content ◽  
Principal Component ◽  
Component Analysis ◽  
Soft Drinks ◽  
Chemical Compositions ◽  
Chemical Structures ◽  
High Sugar ◽  
The Relationship

This study aims to determine the relationship between the chemical compositions of twenty-five (25) soft drinks sold in Nigeria. Sample concentration of twenty-five (25) soft drinks used in the study was collected from the National Agency for Food and Drug Administration and Control (NAFDAC). Principal Component Analysis (PCA) was employed to explain the relationship between the chemical compositions and determine the soft drinks' chemical composition distribution. The result has shown that all except acidity and antioxidant has a significantly strong positive relationship among the chemical structures. PCA suggested retaining three components that explained about 82.465 per cent of the data set's total variability. It was observed that carbonated water, fructose, sucrose, main concentration, stabiliser, E412, colouring and gelatin were the major compositions of the soft drinks in Nigeria, Base on the findings in this study, it is recommendations that; Consumers who are allergic to sugar or diabetic should avoid taking any of the soft drinks with high sugar concentration. Soft drinks companies producing drinks with high sugar content should consider their customers who are diabetic and allergic to high sugar levels.

scholarly journals Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao
Keyword(s):  
Principal Component Analysis ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Production ◽  
Index Model ◽  
Geophysical Logging ◽  
The Relationship ◽  
Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

scholarly journals Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis

2018 ◽  
Vol 10 (2) ◽  
pp. 312 ◽  
Author(s):  
Ana-Maria Săndică ◽  
Monica Dudian ◽  
Aurelia Ştefănescu
Keyword(s):  
Principal Component Analysis ◽  
Human Development ◽  
Quantitative Measure ◽  
Principal Component ◽  
Component Analysis ◽  
Life Expectancy At Birth ◽  
Real Gdp ◽  
Development Indicator ◽  
The Relationship ◽  
Pm2.5 Concentration

EU countries to measure human development incorporating the ambient PM2.5 concentration effect. Using a principal component analysis, we extract the information for 2010 and 2015 using the Real GDP/capita, the life expectancy at birth, tertiary educational attainment, ambient PM2.5 concentration, and the death rate due to exposure to ambient PM2.5 concentration for 29 European countries. This paper has two main results: it gives an overview about the relationship between human development and ambient PM2.5 concentration, and second, it provides a new quantitative measure, PHDI, which reshapes the concept of human development and the exposure to ambient PM2.5 concentration. Using rating classes, we defined thresholds for both HDI and PHDI values to group the countries in four categories. When comparing the migration matrix from 2010 to 2015 for HDI values, some countries improved the development indicator (Romania, Poland, Malta, Estonia, Cyprus), while no downgrades were observed. When comparing the transition matrix using the newly developed indicator, PHDI, the upgrades observed were for Denmark and Estonia, while some countries like Spain and Italy moved to a lower rating class due to ambient PM2.5 concentration.

scholarly journals Chemical Composition and Variability of Leaf and Berry oils from Corsican Juniperus macrocarpa

2018 ◽  
Vol 13 (2) ◽  
pp. 1934578X1801300
Author(s):  
Joséphine Ottavioli ◽  
Ange Bighelli ◽  
Joseph Casanova ◽  
Félix Tomi
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Chemical Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Germacrene D ◽  
Leaf Oil ◽  
Main Components ◽  
Main Component ◽  
C Nmr

The chemical composition of five leaf oil samples and eighteen berry oil samples from Corsican Juniperus macrocarpa have been investigated by GC(RI), GC-MS and 13C NMR. The composition of berry oils was dominated by monoterpene hydrocarbons with α-pinene (56.4-78.9%) as main component followed by myrcene (2.2-11.9%). Germacrene D (4.5-103%) was the major sesquiterpene. The contents of the main components of leaf oils varied drastically from sample to sample: α-pinene (28.7-76.4%), δ3-carene (up to 17.3%), β-phellandrene (up to 12.3%), manoyl oxide (up to 8.1%). The occurrence of the unusual ( Z)-pentadec-6-en-2-one (0.1-1.2%) should be pointed out. Statistical analysis (Principal Component Analysis and k- means partition) suggested a unique group with atypical samples.

Studies on lichen-dominated systems. X. The sedge meadows of the coastal raised beaches

1974 ◽  
Vol 52 (8) ◽  
pp. 1947-1972
Author(s):  
K. A. Kershaw
Keyword(s):  
Principal Component Analysis ◽  
Water Table ◽  
Principal Component ◽  
Component Analysis ◽  
Beach Ridges ◽  
Plant Associations ◽  
Sedge Meadows ◽  
Relationship Of ◽  
Raised Beaches ◽  
The Relationship

The relationship of the sedge meadows lying between raised-beach ridges at the Pen Island site in NW Ontario is described using principal-component analysis. Three major trends are detected following moss hummock formation, depth of water table, and pH. The data also show a progressive sequence from the young meadows with few hummocks and high pH to the older meadows where marked hummock formation has occurred and where the overall pH is lower. Six noda have subsequently been extracted as the central plant associations characteristic of the area.

Sign in / Sign up

Export Citation Format

Share Document