甲魚多元不飽和脂肪酸乙酯油之濃縮方法

一種甲魚多元不飽和脂肪酸乙酯油之濃縮方法,主要乃是藉由超臨界二氧化碳的親油脂之特性,提升甲魚油多元不飽和脂肪酸(EPA與DHA)的含量,達到濃縮甲魚油乙酯物中EPAEE與DHAEE脂肪酸乙酯的目的,為其特徵者

以高光譜影像和光達建置高速公路邊坡隆起和下陷監控

Hyper-spectral and LIDAR images provide a series of image data for geoscience applications. However, few applications of this image material have been proposed to establish a land creep prediction system. The spatial resolution of a LIDAR image is approximately 50 cm, while the typical creep displacement is about 20 cm. It is thus a difficult task to predict the land creep displacement by the use of LIDAR images. Nonetheless, the present study addresses the above issues by using Regional-based growing Object Classification (ROC). The ROC technique is a computational object model that is transformed into a ROI module by the connecting neighboring pixels of similar intensity. The creep displacement is predicted by analyzing the DEM model, which is generated by LIDAR images. The study region is the retaining wall and the slide of a hillslope along Formosa Freeway. The creep displacement is so small that a series of hyperspectral and LIDAR images taken at the same geological location but at different time periods are used for investigation. The hyperspectral data is used to generate the ROC model, and LIDAR data is used to create the DEM model for measuring heaving/settlement. This study proposed two different approaches to investigate the results: the average of difference and baseline correction methods. The average of displacement obtained by the average of difference method is 24 mm, and that obtained by the baseline-correction method is 19 mm. The result indicates the baseline-correction method is a better prediction model, and its regression formula can be used to estimate the amount of heaving/settlement in the early stage of analysis.本研究在使用光達和高光譜兩種影像材料，分析高速公路的高程變化，分別利用這兩種材料建置一 個評估系統， 建立一套可行的評估方法。高速公路的邊坡一年約有十公分到二十公分的隆起、沉陷變化，但 目前光達平面精度約為五十公分，裸地的高程精度約為二十公分，高光譜影像的精度約為二十公分，如何有 效監控邊坡之變動潛勢是重要的課題。本研究提出改善上述的缺點的方法，擬以區域成長法 (Regional-based Object Classification) 中區塊物件的概念，將連續圖素 (pixel) 轉製成 ROI (Region of Interest) 物件，此次研究 主要實證區以國道三號邊坡擋土牆因土壓而引起高程變化現象，擬以不同時期高光譜與光達材料，確認位置 精確後，先以高光譜紋理概念降低植生干擾資訊，後透過光達之高程資料計算不同時期地表變化造成之高程 變化量。本研究案鎖定不同時期的航拍高光譜影像 (用於建立 ROC 模型) 和光達 (用於建立地表垂直位移)， 並以平均差量法和基礎校正法兩種不同的計算方法，求得地表的隆起或下陷，分別分析 DEM 移動量。研究結 果顯示基礎校正法為較佳的預測模型並且可以提供一簡單回歸公式，可用於初期邊坡高程變化量的估算。結 果顯示，平均差量法計算獲得坡地整區移動量約 24 mm，而基礎校正法計算之移動量為 19 mm，顯示這兩種 不同估算方法的結果接近

超臨界二氧化碳製備乙醇代謝之薑黃保健原料

一種超臨界二氧化碳製備乙醇代謝之薑黃保健原料，新鮮薑黃粉經過超臨界二氧化碳(SC-CO2)脫除薑黃酮後簡稱為脫油薑黃粉，再以乙醇溶劑超音波萃取出乙醇代謝成分時，可以從脫油薑黃粉回收89%的薑黃素

Adaptation and Seed Production of Soybean Cultivar Tainan 4 for Green Manure

綠肥大豆新品種台南4號係由青皮豆地方種經單株選拔採純系育種育成，具有籽粒小、生長快速、鮮草量及肥分含量高、覆蓋期長之特性，適合雲嘉南地區水旱田綠肥栽培及秋作採種。為進一步評估該品種在台灣地區之適應性以供綠肥種植推廣，經89年試驗的結果，台南4號在全台9個地點一期作的表現，東部地區以宜蘭、花蓮，西部地區以雲林、嘉義的適應性最佳，生育80天每公頃的生草量25,000至46,000公斤，且田區達到95~100％的覆蓋率，其次為高雄、苗栗及台東地區的適應性良好，其生草量19,740至23,075公斤，至於桃園地區的覆蓋率不佳仍待評估。二期作水田試驗結果，台南4號的生草量及覆蓋率以雲林、嘉義、苗栗、彰化、宜蘭、花蓮、台東等7個地點表現較佳，生育80天每公頃生草量達20,000公斤及87.6%的覆蓋率，顯示該品種除了桃園地點外均適合一、二期作水旱田綠肥種植。 New soybean cultivar Tainan No. 4 was developed by pure line selection. It has many good characteristics such as small seed, growth rapidly, high biomass and fertility, therefore, it is suitable for green manure. To evaluate the adaptionat at different locations and the optimal plant densityfor seed production. We investigate the adaption and seed production of Tainan No. 4 in Taiwan. the results were summarized as fellow. (1) In the spring season, the biomass at han and Hualien areas were higher than other eastern area, Yunlin and Chiayi aera was higher than other western area. The biomass per hectare of Tainan No.4. Were 25,000 kg to 46,000 kg at 80 days after plant. (2) In the summer season, the adaption of seven locations including Yunlin, Chiayi, Miaoli, Changhua, Taitung, Ilan and Hualien showed well and the biomass was 20,000 kg. In the conclusoion, except Tauyang location, Tainan No. 4 which planted in wet field is suitable for green manure in the spring and summer