Classification of Vegetable Oils by Principal Component Analysis of FTIR Spectra

2003 ◽  
Vol 80 (5) ◽  
pp. 541 ◽  
Author(s):  
David A. Rusak ◽  
Leah M. Brown ◽  
Scott D. Martin
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis ◽  
Ftir Spectra

scholarly journals HPLC-UV Polyphenolic Profiles in the Classification of Olive Oils and Other Vegetable Oils via Principal Component Analysis

Separations ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. 33 ◽  
Author(s):  
Mireia Farrés-Cebrián ◽  
Raquel Seró ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis ◽  
Olive Oils

A new application of X-ray scattering using principal component analysis – classification of vegetable oils

2005 ◽  
Vol 539 (1-2) ◽  
pp. 283-287 ◽  
Author(s):  
Gisele G. Bortoleto ◽  
Luiz Carlos M. Pataca ◽  
Maria Izabel M.S. Bueno
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Principal component analysis of measured quantities during degradation of hydroperoxides in oxidized vegetable oils

Lipids ◽  
1999 ◽  
Vol 34 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Károly Héberger ◽  
Ágnes Keszler ◽  
Michael Gude
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis
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