• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.2
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• pp.151-158
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• 2010

Camelina sativa L., known as popular names "gold-of-pleasure" or "false flax" is an alternative oilseed crop that can be grown under different climatic and soil conditions. Up to date, however, the genomic information of Camelina has not been studied in detail. Therefore, a cDNA library was constructed and characterized from young leaves. The constructed cDNA library incorporated of 1334 cDNA clones and the size of the insertion fragments average was 736 base pair. We generated a total of 1269 high-quality expressed sequence tags (ESTs) sequences. The result of cluster analysis of EST sequences showed that the number of unigene was 851. According to subsequent analysis, the 476 (55.9%) unigenes were highly homologous to known function genes and the other 375 (44.1%) unigenes were unknown. Remaining 63 (7.4%) unigenes had no homology with any other peptide in NCBI database, indicating that these seemed to be novel genes expressed in leaves of Camelina. The database-matched ESTs were further classified into 17 categories according to their functional annotation. The most abundant of categories were "protein with binding function or cofactor requirement (27%)", "metabolism (11%)", "subcellular localization (11%)", "cellular transport, transport facilities and transport routes (7%)", "energy (6%)", "regulation of metabolism and protein function (6%)". Our result in this study provides an overview of mRNA expression profile and a basal genetic information of Camelina as an oilseed crop.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1440-1453
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• 2011

Proper consumer segmentation is receiving more attention from industry professionals as markets become more diverse and consumer-centered. Researchers have recognized the limitations of the traditional cluster analysis technique and this research study analyzes market segmentation using Mixture-model or latent-class segmentation. This study used a questionnaire to determine the characteristics of clothing shoppers using a new technique that proved its superiority over traditional techniques. Questions included items measuring fashion shopping behavior, store choice criteria, apparel consumption styles, price perception by product type, and demographic characteristics. Data were collected from 1074 males and females in their 20s and 30s through an online survey. SPSS 16.0 and Latent GOLD 4.0 were used to analyze the data. The ideal typology of clothing shoppers using the Mixture-model were: 'brand loyalty orientated group', 'group of conservative late 30s', 'group of pleasure-emotion early 20s', 'value oriented consumer product with high-income group', 'group of eco/symbol oriented consumer', and 'group of utility/goal oriented male consumer'. This study showed differences in fashion product purchasing behavior by conducting market segmentation for clothing shoppers using the Mixture-model.