• Title/Summary/Keyword: RDA analysis

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Phylogenetic analysis and association of markers and traits related to starch contents in Korean potato cultivars using SSRs

  • Yi, Jung Yoon;Seo, Hyo Won;Huh, On Sook;Park, Young Eun;Cho, Ji Hong;Cho, Hyun Mook
    • Korean Journal of Breeding Science
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    • v.42 no.1
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    • pp.28-34
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    • 2010
  • Diversity of 30 Korean potato cultivars was evaluated using 14 microsatellite markers. Twelve microsatellite markers representing 12 loci in the potato genome detected 84 polymorphisms among 30 cultivars and revealed alleles with a mean of 7.00 alleles per primer. The polymorphism information content (PIC) value ranged from 0.57 to 0.93 with average of 0.82. Based on polymorphism, cluster analysis was conducted by the unweighted pair-group method with arithmetic average (UPGMA) methods. Thirty potato varieties were distinctly separated into 2 groups and similarity coefficient of cluster ranged from 0.58 to 0.95. Thirty tetraploid cultivars were evaluated for six important agronomic traits. One-way analysis of variance was done to look for the degree of relationships between individual markers and traits. K1 and K2 markers showed a significant association with amylose contents, starch contents, and specific gravity.

Flavonoids analysis about mulberry fruit of Korean mulberry cultivar, 'Daeshim'

  • Ju, Wan-Taek;Kwon, O-Chul;Kim, Yong-Soon;Kim, Hyun-Bok;Sung, Gyoo-Byung;Kim, Jong-gil
    • International Journal of Industrial Entomology and Biomaterials
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    • v.37 no.2
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    • pp.43-48
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    • 2018
  • Mulberry fruit is a new income product in Korea sericulture due to the increase of fruit consumption. However, flavonoids of Korean mulberry cultivar for fruit production did not reported yet. In this study, the typical mulberry cultivar, 'Daeshim' was analyzed using ultrahigh performance liquid chromatography coupled with diode array detection and quadrupole time-of-flight mass spectrometry (UPLC-DAD-QTOF/MS) technique for flavonoids analysis. Nine flavonoids were isolated and analyzed from Daeshim using UPLC-DAD-QTOF/MS chromatogram. According to quantitative analysis, rutin (66.1 mg/100g DW) and quercetin 3-O-(6"-O-malonyl) glucoside (26.7 mg/100g DW) were abundant in mulberry fruit. Our results might be used as basic information for mulberry consumption.

Comparison of Analytical Methods for Volatile Flavor Compounds in Leaf of Perilla frutescens

  • Kim, Kwan-Su;Ryu, Su-Noh;Song, Ji-Sook;Bang, Jin-Ki;Lee, Bong-Ho
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.44 no.2
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    • pp.154-158
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    • 1999
  • Volatile flavor compounds from perilla leaves were extracted and analyzed with different methods, head-space analysis (HS), simultaneous steam distillation and extraction (SDE) , and solvent extraction (SE), and to compare their efficiencies for quick analysis. Over 30 volatile compounds were isolated and 28 compounds were identified by GC/MSD. Major compound was perillaketone showing the compositions of which were 92% in SDE method, 86% in headspace analysis, and 62% in solvent extraction method. For quick evaluation of leaf flavor in perilla, it was desirable because the headspace analysis method had a shorter analyzing time and smaller sample amount than the other methods.

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Relational Discriminant Analysis Using Prototype Reduction Schemes and Mahalanobis Distances (Prototype Reduction Schemes와 Mahalanobis 거리를 이용한 Relational Discriminant Analysis)

  • Kim Sang-Woon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.1 s.307
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    • pp.9-16
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    • 2006
  • RDA(Relational Discriminant Analysis) is a way of finding classifiers based on the dissimilarity measures among the prototypes extracted from feature vectors instead of the feature vectors themselves. Therefore, the accuracy of the RDA classifier is dependent on the methods of selecting prototypes and measuring proximities. In this paper we propose to utilize PRS(Prototype Reduction Schemes) and Mahalanobis distances to devise a method of increasing classification accuracies. Our experimental results demonstrate that the proposed mechanism increases the classification accuracy compared with the conventional approaches for samples involving real-life data sets as well as artificial data sets.