Wood Structure of the Rosaceae in Relation to Ecology, Habit and Phenology

IAWA Journal ◽  
1992 ◽  
Vol 13 (3) ◽  
pp. 307-349 ◽  
Author(s):  
Shu-Yin Zhang ◽  
Pieter Baas ◽  
Marinus Zandee
Keyword(s):  
Principal Component Analysis ◽  
Phylogenetic Analysis ◽  
Correlation Analysis ◽  
Path Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Distribution Area ◽  
Wood Structure ◽  
Simple Correlation ◽  
Entire Distribution

Twelve wood anatornical characters, together with broad parameters from ecology, habit and phenology were subjected to simple correlation analysis, path analysis and principal component analysis, in a total sampie of over 470 specimens belonging to 271 species of the Rosaceae from the entire distribution area of the farnily. The functional, developmental and systematic implications of the resulting relations are discussed. Based on the present analysis of ecological trends and previous phylogenetic analysis, a tentative scenario for the evolution of the Rosaceae is offered.

Evaluation of the morphological diversity of tea roses (Rosa x odorata) based on phenotypic traits

2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Morphological Traits ◽  
Significant Positive Correlation ◽  
Flower Colour ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Germplasm Resources ◽  
Trait Correlation

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.

scholarly journals Correlation Analysis Between Antioxidant Activity and Phytochemicals in Korean Colored Corns Using Principal Component Analysis

2014 ◽  
Vol 6 (4) ◽  
pp. 1 ◽  
Author(s):  
Kang Mo Ku ◽  
Hye Suk Kim ◽  
Soon Kwon Kim ◽  
Young-Hwa Kang
Keyword(s):  
Principal Component Analysis ◽  
Antioxidant Activity ◽  
Correlation Analysis ◽  
Principal Component ◽  
Total Content ◽  
Component Analysis ◽  
Total Anthocyanin ◽  
Reducing Ability ◽  
Good Target ◽  
Diammonium Salt

The colored corns are used as food as well as for feed in Asian countries; however, the active component of antioxidant activity in Korean colored corns has not been investigated. Thus, we measured the total content of carotenoids, phenols, flavonoids, and anthocyanins from 40 Korean colored corn genotypes for correlation analysis between antioxidant activity and these phytochemicals. The ferric reducing ability power (FRAP) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) activity were measured in order to study this correlation. As a result, there was large variation in total anthocyanin (coefficient of variation, CV 85.0%) and total carotenoid contents (CV 87.8%), while CVs of total phenol, total flavonoid contents, ABTS and FRAP was relatively low (CV 15.0%, 22.8%, 15.5%, and 16.3% respectively). There were meaningful correlations between ABTS and anthocyanins, phenols, and flavonoids, as well as correlations between FRAP and phenols as well as FRAP and flavonoids. We also obtained a more informative and easily visualized result by using principal component analysis (PCA). Anthocyanins and carotenoids showed a large variation as compared to other compounds. Anthocyanins are a good target to increase antioxidant activity in colored corns.

An Affective Approach to Developing Marketing Strategies of Mineral Water

2012 ◽  
Vol 16 (4) ◽  
pp. 514-520 ◽  
Author(s):  
Junzo Watada ◽  
◽  
Le Yu ◽  
Munenori Shibata ◽  
Marzuki Khalid ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Mineral Water ◽  
Principal Component ◽  
Component Analysis ◽  
Marketing Strategies ◽  
Taste Preferences ◽  
Self Organizing Map ◽  
Water Based ◽  
Self Organizing

This study is concerned with the development of marketing strategies for mineral water based on consumers’ taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.

scholarly journals Analisis Partial Least Square Menghasilkan Output Alternatif Antarmuka Tambahan Dari Kansei Engineering

2020 ◽  
Vol 17 (2) ◽  
pp. 67
Author(s):  
Arief Ginanjar ◽  
Awan Setiawan
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Type I ◽  
Kansei Engineering ◽  
Coefficient Corrélation

Ketika menggunakan Kansei Engineering dalam mencari kandidat terbaik untuk menentukan model perancangan antarmuka website, peneliti menggunakan metode analisis Partial Least Square (PLS) yang dilakukan secara berulang hingga ditemukan elemen terbaik yang dapat diimplementasikan. PLS sebagai alat bantu untuk menentukan nilai terbaik antara elemen website. Output perbandingan yang dihasilkan akan dikelompokkan berdasarkan Kansei Word sebagaimana yang telah ditentukan dalam rencana awal implementasi Kansei Engineering, output perbandingan PLS iterasi pertama mempunyai kemungkinan mendapatkan nilai usulan terbaik jika digabung dengan melakukan iterasi kedua terhadap asimilasi dua atau tiga elemen yang mempunyai nilai tertinggi. Metodologi yang digunakan mengacu kepada Kansei Engineering Type I dengan melalui pengolahan data menggunakan Cronbach’s Alpha untuk menguji kelayakan responden, kemudian untuk mengetahui hubungan Kansei Words dapat menggunakan Coefficient Correlation Analysis (CCA), sedangkan hubungan antara Kansei Words dengan spesimen dapat menggunakan Principal Component Analysis (PCA), sedangkan mencari pengaruh Kansei Words paling kuat dapat menggunakan Factor Analysis (FA) dan analisis Partial Least Square (PLS) namun harus dilakukan iterasi proses PLS hingga variabel rekomendasi model perancangan antarmuka yang dihasilkan menjadi lebih bervariatif.

scholarly journals Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

2020 ◽  
Author(s):  
Christiane Scherer ◽  
James Grover ◽  
Darby Kammeraad ◽  
Gabe Rudy ◽  
Andreas Scherer
Keyword(s):  
Principal Component Analysis ◽  
Population Genetics ◽  
Phylogenetic Analysis ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Whole Genomes

AbstractSince the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year.SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

Statistical Weather Forecasting

1976 ◽  
Vol 98 (1) ◽  
pp. 49-55
Author(s):  
A. Grosjean ◽  
J. L. Kueny
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Correlation Analysis ◽  
Weather Forecasting ◽  
Selection Procedure ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Correlation ◽  
Multiple Correlation Analysis ◽  
Prediction Problems

This paper illustrates the use of statistical methods such as multiple correlation analysis, discriminant analysis and principal component analysis for weather forecasting. Two examples are presented: the first is a qualitative local prediction of daily precipitation for the next day, using pressure measures of the present day and of past days, by means of discriminant analysis; the second is an analysis of the 500-mbar geopotential heights over Western Europe by means of principal component analysis, followed by a quantitative synoptic prediction of the evolution of these geopotential heights for the next week to come, by means of multiple correlation analysis. For each of these two prediction problems, good predictors are chosen among a great number of candidate ones by a special stepwise selection procedure.

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