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.

The Principal Component Analysis of Coastal Areas

2014 ◽  
Vol 513-517 ◽  
pp. 3703-3706
Author(s):  
Qiu Ju Wang ◽  
Da Shen Xue
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coastal Areas ◽  
Principal Component Analysis Analysis ◽  
Consumer Prices ◽  
The Government ◽  
Main Component ◽  
The Impact

In order to development the economic of China's coastal areas better, the paper mainly discusses the coastal areas of China's consumer price factors, the main use of software SPSS, using statistical analysis, principal component analysis, analysis that the impact is the main component of consumer prices and reached the level of consumer prices in coastal areas, which is conducive to the Government to take appropriate measures to faster and better development of the region's economy.

scholarly journals The Main Component of Smart Tourism: A Principal Component Analysis Approach

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 295-307
Author(s):  
Ruci Meiyanti ◽  
Dana Indra Sensuse ◽  
Yudho Giri Sucahyo
Keyword(s):  
Principal Component Analysis ◽  
Information And Communication Technologies ◽  
Principal Component ◽  
Component Analysis ◽  
Successful Implementation ◽  
Information And Communication ◽  
Main Components ◽  
Main Component ◽  
Development And Management ◽  
Smart Tourism

Introduction. The establishment of a smart tourism model is indispensable for the effectiveness of tourism development and management supported by advances in information and communication technology. The successful implementation of smart tourism is determined by the smart tourism model. Determination of the main components has a significant role in building an effective model. The purpose of this study, to determine the main components forming a smart tourism model using Principal Component Analysis (PCA). PCA is used to compress variables by reducing the number of dimensions, without losing much information. Method This research method is a quantitative method using SPSS software version 22. Result. The result obtained 9 main components, namely stakeholders, infrastructure, goals, resources, activities, challenges, innovation in various applications, strategies, and use of information and communication technologies. Conclusion. Those main components are expected to construct a smart tourism model with components that are comprehensive, interrelated, and adaptable.

scholarly journals Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama

2020 ◽  
Vol 1 (1) ◽  
pp. 40
Author(s):  
Samsul Arifin ◽  
Anna Islamiyati ◽  
Raupong Raupong
Keyword(s):  
Principal Component Analysis ◽  
Predictor Variable ◽  
Principal Component ◽  
Component Analysis ◽  
Research Data ◽  
Predictor Variables ◽  
Analysis Method ◽  
Main Components ◽  
Main Component ◽  
New Variables

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.

scholarly journals Principal component analysis implementation for brainwave signal reduction based on cognitive activity

2017 ◽  
Vol 3 (3) ◽  
pp. 125 ◽  
Author(s):  
Ahmad Azhari ◽  
Murein Miksa Mardhia
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Task ◽  
Cognitive Activity ◽  
Electrical Potential ◽  
Principal Component ◽  
Component Analysis ◽  
Wave Form ◽  
Brain Response ◽  
Cumulative Rate ◽  
Main Component

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.

scholarly journals Statistical Analysis of Mitochondrial Pathologies in Childhood: Identification of Deficiencies Using Principal Component Analysis

2000 ◽  
Vol 80 (7) ◽  
pp. 1019-1030 ◽  
Author(s):  
Thierry Letellier ◽  
Gilles Durrieu ◽  
Monique Malgat ◽  
Rodrigue Rossignol ◽  
Jaromir Antoch ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis

scholarly journals Composition and Chemical Variability of Ivoirian Xylopia staudtii Leaf Oil

2015 ◽  
Vol 10 (6) ◽  
pp. 1934578X1501000
Author(s):  
Thierry Acafou Yapi ◽  
Jean Brice Boti ◽  
Antoine Coffy Ahibo ◽  
Sylvain Sutour ◽  
Ange Bighelli ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Nmr Spectroscopy ◽  
Essential Oils ◽  
Oil Composition ◽  
Chemical Variability ◽  
Germacrene D ◽  
Leaf Oil ◽  
Nmr Techniques ◽  
C Nmr

The chemical composition of a leaf oil sample from Ivoirian Xylopia staudtii Engler & Diels (Annonaceae) has been investigated by a combination of chromatographic [GC(RI)] and spectroscopic (GC-MS, 13C NMR) techniques. Thirty-five components that accounted for 91.8% of the whole composition have been identified. The oil composition was dominated by the furanoguaiadienes furanoguaia-1,4-diene (39.0%) and furanoguaia-1,3-diene (7.5%), and by germacrene D (17.5%). The composition of twelve other leaf oil samples demonstrated qualitative homogeneity, but quantitative variability. Indeed, the contents of the major components varied substantially: furanoguaia-1,4-diene (24.7–51.7%) and germacrene D (5.9–24.8%). The composition of X. staudtii leaf oil is close to that of X. rubescens leaf oil but varied drastically from those of the essential oils isolated from other Xylopia species. 13C NMR spectroscopy appeared as a powerful and complementary tool for analysis of sesquiterpene-rich essential oils.

scholarly journals Principal Component Analysis of Multiwavelength Properties of Seyfert Galaxies

1994 ◽  
Vol 159 ◽  
pp. 502-502
Author(s):  
Deborah Dultzin–Hacyan ◽  
Carlos Ruano
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Seyfert Galaxies ◽  
Component Analysis ◽  
X Ray ◽  
Radio Data

A multidimensional statistical analysis of observed properties of Seyfert galaxies has been carried out using Principal Component Analysis (PCA) applied to X-ray, optical, near and far IR and radio data for all the Seyfert galaxies types 1 and 2 for the catalog by Lipovtsky et al. (1987).

scholarly journals Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Principal Component Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Principal Component ◽  
Component Analysis ◽  
Retinal Images ◽  
Image Area ◽  
Main Components ◽  
Vessel Tree ◽  
Optic Discs

A retinal image has blood vessels, optic disc, fovea, and so forth as the main components of an image. Segmentation of these components has been investigated extensively. Principal component analysis (PCA) is one of the techniques that have been applied to segment the optic disc, but only a limited work has been reported. To our knowledge, fovea segmentation problem has not been reported in the literature using PCA. In this paper, we are presenting the segmentation of optic disc and fovea using PCA. The PCA was trained on optic discs and foveae using ten retinal images and then applied on seventy retinal images with a success rate of 97% in case of optic discs and 94.3% in case of fovea. Conventional algorithms feed one patch at a time from a test retinal image, and the next patch separated by one pixel part is fed. This process is continued till the full image area is covered. This is time consuming. We are suggesting techniques to cut down the processing time with the help of binary vessel tree of a given test image. Results are presented to validate our idea.

Sign in / Sign up

Export Citation Format

Share Document