• 한국식품영양과학회지
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• 제37권2호
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• pp.190-194
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• 2008

팽이버섯 배지에 녹차 분말 또는 녹차 조카테킨 추출물을 농도별로 첨가하여 팽이버섯 자실체를 형성시킨 후 자실체의 이화학적 특성을 조사하였다. 버섯의 수확량은 무첨가구에서 최고의 수량을 보였으며 녹차분말 10% 첨가구에서 가장 낮은 값을 나타낸 반면, 경도는 무첨가구에서 가장 낮았고 녹차 분말 5% 및 10% 첨가구와 녹차 조카테킨 첨가구에서 높게 나타났다. 카테킨 및 카페인 함량 분석은 팽이버섯 추출물을 용매분획한 후 HPLC로 정량한 결과, 모든 시험구에서 카테킨은 검출되지 않았으며 총 폴리페놀 함량 또한 각 처리구간에 유의적 차이가 없는 것으로 나타나 카테킨 등 폴리페놀성 물질의 팽이버섯으로의 이행은 거의 없는 것으로 판단된다. 반면 카페인은 팽이버섯으로 이행되어 녹차 분말 무첨가구는 $2.12\;{\mu}g/g$, 녹차분말 10% 첨가구는 $196.23\;{\mu}g/g$이 검출되었다. 전자코를 이용한 향기패턴 분석 결과는 녹차 첨가에 의해 머무름 시간 3초와 6초 사이에 새로운 peak가 생성되었음을 알 수 있었으며, 녹차 10% 첨가구에서는 뚜렷한 향기의 변화가 있는 것을 확인할 수 있었다. 즉, 팽이버섯 배지에 녹차분말 또는 녹차 조카테킨 추출물 첨가에 의해 버섯의 경도가 증가되었으며 녹차 성분 중 카페인이 이행되어 새로운 기능성 버섯을 생산할 수 있을 것으로 사료되었다.

• 농촌계획
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• 제30권1호
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.