scholarly journals Distinguishing Coconut Oil from Coconut Paring Oil using Principle Component Analysis of Fatty Acid Data

CORD ◽  
2012 ◽  
Vol 28 (1) ◽  
pp. 5
Author(s):  
J.M.N. Marikkar
Keyword(s):  
Principal Component Analysis ◽  
Fatty Acid ◽  
Iodine Value ◽  
Coconut Oil ◽  
Principal Component ◽  
Component Analysis ◽  
Pca Analysis ◽  
Fatty Acid Data ◽  
Influencing Parameters ◽  
Iodine Values

A study was carried out to distinguish coconut oil from coconut pairing oil by the application of principal component analysis (PCA) to fatty acid compositional and iodine value data. Five samples of ordinary coconut oil extracted from five different batches of copra and five samples of coconut pairing oil obtained from five batches of dried coconut pairings were employed. Fatty acid composition and iodine values of oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric and oleic acid contents and iodine value data are the most influencing parameters to discriminate coconut oil from coconut pairing oil. Hence, the application of PCA to fatty acid compositional and iodine value data was successful in distinguishing coconut oil from coconut pairing oil.

scholarly journals Principal Component Analysis of Fatty Acid Data to Detect Virgin Coconut Oil Adulteration by Palm Olein

CORD ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 9
Author(s):  
J.M.N. Marikkar
Keyword(s):  
Principal Component Analysis ◽  
Fatty Acid ◽  
Palm Olein ◽  
Compositional Data ◽  
Coconut Oil ◽  
Principal Component ◽  
Component Analysis ◽  
Coefficient Of Determination ◽  
Virgin Coconut Oil ◽  
Fatty Acid Compositions

Authentication of virgin coconut oil (VCO) is important to safeguard customers from adulteration practices. A study was carried out to distinguish VCO from VCO adulterated with palm olein (PO) using principal component analysis (PCA) of fatty acid (FA) compositional data. Six samples of VCO, and six samples of palm olein were obtained from oil producers’ companies in Malaysia. Six samples of adulterated VCO were prepared by mixing with palm olein in 5% increment of adulteration. Fatty acid compositions of all oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric, palmitic and oleic acids were the most influencing parameters to discriminate VCO from adulterated VCO. Out of the thirteen FA variables investigated, ten were found to display high correlation with increasing adulteration. Predictive models showing higher coefficient of determination (R2) and good confidence limits were useful for quantification purposes.

Principal component analysis applied to harbour porpoise fatty acid data

2007 ◽  
pp. 515-527 ◽  
Author(s):  
I. T. Jolliffe ◽  
J. A. Learmonth ◽  
G. J. Pierce ◽  
M. B. Santos ◽  
N. Trendafilov ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fatty Acid ◽  
Principal Component ◽  
Component Analysis ◽  
Harbour Porpoise ◽  
Fatty Acid Data

scholarly journals Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

2012 ◽  
Vol 17 (3) ◽  
pp. 184-191 ◽  
Author(s):  
Eui-Cheol Shin ◽  
Chung-Eun Hwang ◽  
Byong-Won Lee ◽  
Hyun-Tae Kim ◽  
Jong-Min Ko ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fatty Acid ◽  
Principal Component ◽  
Component Analysis ◽  
Fatty Acid Profiles ◽  
Soybean Cultivars ◽  
Chemometric Approach

scholarly journals Physicochemical and Techno-Functional Characterization of Native Corn Reintroduced in the Andean Zone of Jujuy, Argentina

Proceedings ◽  
2020 ◽  
Vol 53 (1) ◽  
pp. 7
Author(s):  
María Alejandra Giménez ◽  
Cristina Noemí Segundo ◽  
Manuel Oscar Lobo ◽  
Norma Cristina Sammán
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Food Industry ◽  
Functional Characterization ◽  
Principal Component ◽  
Component Analysis ◽  
High Variability ◽  
Pca Analysis

The chemical and techno-functional properties of nine maize races from the Andean zone of Jujuy, Argentina, in the process of reintroduction, were determined. Principal component analysis (PCA) was applied to establish the differences between them. The breeds studied showed high variability in their chemical and techno-functional properties, which would indicate that their applications in the food industry will also be differentiated. The PCA analysis allowed us to group them into four groups, and the Capia Marron and Culli races showed unique properties, mainly in the formation of gels.

Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data

2006 ◽  
Author(s):  
Kartik Ramanujachar ◽  
Satish Draksharam
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Pattern Generation ◽  
Automatic Test Pattern Generation ◽  
Pca Analysis ◽  
Wafer Level ◽  
Failure Data ◽  
Commonality Analysis

Abstract This article explores the use of principal component analysis (PCA) and hierarchical clustering in the analysis of wafer level automatic test pattern generation (ATPG) failure data. The principle of commonality is extended by utilizing hierarchical clustering to collect die that are more similar to one another in their manner of failure than to others. Similarity is established by PCA of the patterns that the die in a wafer fail. Results demonstrated that PCA analysis and clustering are useful tools for dimensionality reduction and commonality analysis of wafer level ATPG data. The utility of PCA analysis and clustering in the extraction of die for physical failure analysis is also illustrated.

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