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.

본 연구에서는 전국 59개 지점의 3개월 SPI 자료를 가지고 EOF를 유도하고 아울러 그 공간적 특성을 분석하였다. 또한 EOF 해석에 의해 나타난 Coefficient Time Series를 다변량 시계열 모형에 적용하여 SPI 시계열을 자료기간 10,000년으로 확장하였고 전국적인 가뭄심도를 판단하기 위해 전국 평균 지수를 이용하여 재현기간별 최대심도를 결정하였다. 마지막으로 각 대권역의 댐 유효저수량과 농경지 면적을 이용하여 농업가뭄 대비능력을