Classification of Wines Based on Combination of 1H NMR Spectroscopy and Principal Component Analysis

2007 ◽  
Vol 25 (7) ◽  
pp. 930-936 ◽  
Author(s):  
Yuan-Yuan Du ◽  
Guo-Yun Bai ◽  
Xu Zhang ◽  
Mai-Li Liu
Keyword(s):  
Principal Component Analysis ◽  
Nmr Spectroscopy ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Principal Component ◽  
Component Analysis

scholarly journals Metabolic Fingerprinting of Ephedra Species Using 1H-NMR Spectroscopy and Principal Component Analysis

2005 ◽  
Vol 53 (1) ◽  
pp. 105-109 ◽  
Author(s):  
Hye Kyong Kim ◽  
Young Hae Choi ◽  
Cornelis Erkelens ◽  
Alfons W. M. Lefeber ◽  
Robert Verpoorte
Keyword(s):  
Principal Component Analysis ◽  
Nmr Spectroscopy ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Metabolic Fingerprinting

scholarly journals Quantification of Anethole in Fennel and Anise Essential Oils using Gas Chromatography and 1H-NMR-Spectroscopy

2019 ◽  
Vol 76 (2) ◽  
pp. 105
Author(s):  
Denisa Eglantina DUȚĂ ◽  
Alina CULEȚU ◽  
Mioara NEGOIȚĂ ◽  
Valentin Ionescu
Keyword(s):  
Principal Component Analysis ◽  
Antimicrobial Activity ◽  
Nmr Spectroscopy ◽  
Essential Oils ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Principal Component ◽  
Main Compound ◽  
Different Origins ◽  
Different Levels

Nine essential oils from fennel (seeds and herbs) and anise (seeds) from different origins were analysed for density, refractive index and for a complete composition through GC-MS and 1H-NMR-spectroscopy. Anethole was the main compound identified in fennel and anise essential oils. Anethole content varied between 30 – 90% in fennel oils and between 80 – 99% in anise oils; anethole is often used as flavouring substance in foods with a good antimicrobial activity also. A positive correlation was found between anethole content determined by GC-MS and 1H-NMR (r = 0.8567 for fennel oils and r = 0.6986 for anise oils). The results showed different levels of anethole in oils (values ranged between 30.66 % and 99.24 %). Electronic nose was a very good and rapid method for discrimination of essential oils based on PCA (Principal Component Analysis) with discrimination index above 90 for both essential oils.

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.

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