Classification of chili powders by thin-layer chromatography and principal component analysis

2000 ◽  
Vol 45 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Tibor Cserháti ◽  
Esther Forgács ◽  
Helena Morais ◽  
Teresa Mota
Keyword(s):  
Principal Component Analysis ◽  
Thin Layer ◽  
Thin Layer Chromatography ◽  
Principal Component ◽  
Component Analysis ◽  
Layer Chromatography

scholarly journals Deteksi Adulteran Pada Bahan Baku Sediaan Temulawak (Curcuma xanthorrhiza ROXB) Instan Secara Tlc Fingerprint Analysis

2018 ◽  
Vol 15 (1) ◽  
pp. 38
Author(s):  
Fauzan Zein Muttaqin ◽  
Nurul Aida ◽  
Aiyi Asnawi
Keyword(s):  
Principal Component Analysis ◽  
Thin Layer ◽  
Thin Layer Chromatography ◽  
Curcuma Longa ◽  
Principal Component ◽  
Component Analysis ◽  
Fingerprint Analysis ◽  
Curcuma Xanthorrhiza ◽  
Layer Chromatography

Temulawak (Curcuma xanthorrhiza Roxb) merupakan salah satu jenis tanaman unggulan yang banyak dimanfaatkan masyarakat. Pencampuran adulteran pada bahan baku sediaan temulawak dapat membahayakan kesehatan. Penelitian ini bertujuan untuk mendeteksi adulteran pada bahan baku sediaan temulawak instan. Metode yang digunakan adalah Thin Layer Chromatography (TLC) fingerprint analysis. Sidik jari KLT temulawak dibuat menggunakan rimpang temulawak yang berasal dari Cianjur, Semarang, dan Nusa Tenggara Timur. Sementara sidik jadi kunyit (Curcuma longa)sebagai adulteran utama dibuat menggunakan rimpang kunyi dari Cianjur. Ekstraksi dilakukan dengan metode maserasi menggunakan pelarut etanol 96%. Analisis kromatogram secara kemometrik menggunakan metode Principal Component Analysis (PCA). Nilai loadings Principal Component 1 (PC1) menunjukkan kurva yang linier dan data hasil scores PC1 tersebut dapat membedakan dengan baik sidik jari temulawak dari kunyit dengan nilai scores temulawak dan kunyit berada pada kuadran yang berbeda. Hasil menunjukkan bahwa nilai scores ketiga sampel temulawak instan berada di antara kuadran temulawak dan kunyit (Curcuma Longa L). Dapat disimpulkan bahwa semua sampel positif mengandung adulteran pada temulawak instan.

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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