In this study, we investigated the quality characteristics and the sensory evaluation for madeleine added with peach (Prunus persica L. Batsch) juice. The pH and specific volume of madeleine were decreased with increase of peach juice, whereas the moisture and loss rate were increased. In the color of madeleine crust, L and b value were decreased with increase of peach juice, and these results showed significant differences compared to control(p<0.05). The other hand, the colors of madeleine crumb showed less significant changes compared to the control. On texture of madeleine with increase of peach juice, the hardness, chewiness, gumminess and cohesiveness were increased, whereas the adhesiveness was decreased. In the sensory evaluation with taste, color, flavor and overall preference, the madeleine with 20%(w/w) peach juice showed the highest value. Consequently, these results should provide the possibile use of peach processing in bakery industry because the addition of peach juice enhanced the quality and sensory characteristics of madeleine.

Demand for organic analysis increase as industries are growing and many products are spreaded in the daily life. One of many products is oil spill dispersant. It was used for oil accident in the ocean. When oil spill dispersant spread at the ocean, the petroleum in the ocean is dispersed. The oil spill dispersant is made of non ionic surfactant and petroleum oil. The non ionic surfactant disperse petroleum from oil accident. The other part is petroleum oil which has aromatic hydrocarbon. Because the aromatic hydrocarbon is cancerogenic material, it directly injure animals in the ocean. This cause the second pollution in the human body. Many oil accidents still are controlled by oil spill dispersant. Therefore quality control of the oil spill dispersant become important and this also demand for the exact quantitative analysis of aromatic hydrocarbon. Hereupon the first we develop separate petroleum oil from surfactant. The second standardize analytical method of aromatic hydrocarbon in the separated petroleum oil.

정보 검색 분야의 문서 분류에 기계 학습 기법을 적용할 때 발생하는 가장 큰 문제는 문서를 패턴으로 표현할 때, 하나의 패턴이 가지는 특징의 수가 기계 학습 기법에서 처리할 수 있는 범위를 넘어서는 것이다. 이러한 문제를 해결하기 위하여 특징 선택 기법은 패턴을 구성하고 있는 특징 중에서 실제 문서 분류에 많은 영향을 주는 특징만을 선택하여, 기계 학습 기법에서 쉽게 처리할 수 있을 정도의 패턴을 구성하게 한다. 본 논문에서는 이러한 특징 선택 기법 중에서 IG(Information Gain), Gini index, Relief-F, DF(Document Frequency)를 비교하였다. 실험 결과 문서들에 포함된 모든 고유 단어를 특징의 길이로 하여 패턴을 구성했을 때보다 특징 선택 기법을 적용하여 고유 단어 중 일부를 특징으로 패턴을 구성할 때 기계학습에서 더 향상된 분류 성능을 보였다