Both carcass yield and meat quality are important indices for beef grade, and consequently they are crucial factors for determining beef consumption and price. However, an optimal slaughter time for satisfying high carcass yield and meat quality has not been systemically evaluated, and identifying the time requires their relationship. Therefore, the objective of this study is 1) to examine the distribution of carcass yield by quality index, and 2) to investigate correlation between factors for judging grades of carcass yield and meat quality. We applied statistical analysis (ANOVA and correlation) for the data of Hanwoo (892 data points: bulls=311, cows=163, steers=418) slaughtered from 1995 to 2006. As a result of ANOVA, the mean difference of carcass yield in quality grade 3 was different with grade 1+, 1, and 2(p<0.05). In the correlation analysis, the meat quality grade showed a positive weak correlation with carcass yield (r=0.259, p<0.05), but was negatively correlated to carcass weight and back-fat thickness which were factors for carcass yield index calculation (-0.225 and -0.154, respectively, p<0.05). The results provide an initial idea for relating carcass yield and quality so that we can use it for determining the optimal time for slaughting.

In South Korea, increase in kimchi import from China has emphasized the importance of noticing the origin of production area, largely because of the price and safety concerns. Nevertheless, identification of it depends on a complex physicochemical method. Hence, the objective of this study is to develop a statistical algorithm applicable for analyzing volatile compounds measured by electronic nose so that the device can be used for simple classification of kimchi by its production origin. Discriminant function analysis (DFA), one of multi-variate analysis, was mainly used for analyzing big-size data of volatile compounds detected from kimchi produced either in South Korea or China. Result showed that DFA could completely separate 69 varieties of kimchi by its origin of production (39 from South Korea and 30 from China). This result suggests that volatile compounds can be an index for identifying origin of kimchi and consequently, electronic nose is an optimal option for identifying origin of kimchi production when combined with multi-variate statistics.

Beneficial effects of glucosinolate in Chinese cabbage on human health have propelled researches on biochemical and genetic characteristics of glucosinolate and Chinese cabbage. However, growth conditions which are practically important in producing functional Chinese cabbage and in optimizing glucosinolate have been little focused. The objective of this study, hence, was to identify growth conditions affecting glucosinolate contents so that the result could be used for further optimizing glucosinolate in Chinese cabbage. We used principal component analysis (PCA) for analyzing glucosinolate contents in Chinese cabbage cultivated under various growth conditions in the plant factory. As a result, PCA showed that two principal components were able to explain more than 76% of variations in glucosinolate contents caused by different growth conditions. The first principal component (PC1) was mainly represented by humidity and temperature, while growth duration was the main component of PC2. From these results, it was conclusive that glucosinolate contents in Chinese cabbage were largely affected by humidity, temperature and growth duration.

Chinese cabbage, a traditional food source in Asia, has been highlighted due to its functional aspects. In particular, breakdown products of glucosinolates (GSLs) that contribute to the unique flavor and fragrance of Brassicaceae vegetables and act in plant defense mechanisms has been known as an anti-cancer agent. The objective of this study was to investigate optimal condition to grow Chinese cabbage with focusing on enhancing glucosinolates contents. The main tools for our analysis were correlation analysis and principal component analysis (PCA), and they were applied into a data obtained from Chinese cabbage grown in the plant factory. The data included glucosinolate contents and growth conditions (day, temperature, humidity, and EC). As a result of correlation analysis, day showed the largest significance with glucosinolates, followed by EC and temperature. The results of PCA showed that three principal components were able to explain more than 75% of variations in glucosinolate contents caused by different growth conditions. The first principal component (PC1) was mainly represented by day, EC, and temperature, while the main factors in the second (PC2) and the third (PC3) components were represent humidity, and , respectively. In conclusion, glucosinolate contents in Chinese cabbage were largely affected by day, EC, and temperature during the growth.