This study examined the glycemic indices (GIs) and glycemic loads of carbohydrate-rich snacks in Korea according to variety and cooking method. The most popular carbohydrate snacks (corn, potatoes, sweet potatoes, chestnuts, and red beans) from the Korean National Health and Nutrition Examination Survey nutrient database were cooked using a variety of conventional cooking methods (steaming, baking, porridge, puffing, and frying). The GIs of foods were measured in 60 healthy males after receiving permission from the University Hospital institutional review board (KMC IRB 1306-01). Blood glucose and insulin levels were then measured at 0, 15, 30, 60, 90, and 120 min after consuming glucose, and each test food contained 50 g of carbohydrates (corn: 170.0 g, potatoes: 359.7 g, sweet potatoes: 160.3 g, chestnuts: 134.8 g, red beans: 73.1 g). GI values for test foods were calculated based on the increase in the area under the blood glucose response curve for each subject. Steamed potatoes (93.6±11.6), corn porridge (91.8±19.5), baked sweet potatoes (90.9±9.6), baked potatoes (78.2±14.5), steamed corn (73.4±9.9), and steamed sweet potatoes (70.8±6.1) were shown to be considered high GI foods, whereas baked chestnuts (54.3±6.3), red bean porridge (33.1±5.5), steamed red beans (22.1±3.2), fried potatoes (41.5±7.8), and ground and pan-fried potatoes (28.0±5.1) were considered as low GI foods. The results suggest that the cooking method of carbohydrate-rich snacks is an important determinant of GI values.

This study was designed to investigate the in vitro antioxidant potentials and the antioxidant components of 80% ethanolic extracts prepared from five edible spring flowers. The contents of total phenols (14.1-18.9 mg gallic acid equivalents/g of dry weight; DW), flavonoids (0.3-4.9 mg catechin equivalents), total carotenoids (1.9-578.1 µg β-carotene equivalents), and proanthocyanidins (2.8-23.5 mg catechin equivalents), were determined by spectrophotometry. In addition, the content of ascorbic acid (0.71-1.31 mg) was determined using HPLC with UV detection. All varieties of the flower examined showed antioxidant potential (1.2-46.1 mM Trolox equivalents/g DW), which was measured by the radical scavenging activity and the reducing power. Of the five flowers analyzed, Prunus avium L. showed the highest antioxidant activity. A strong correlation was found between the ascorbic acid (r 2 =0.75), flavonoids (r 2 =0.71), and total phenolic content (r 2 =0.64) and the antioxidant parameters, suggesting that these components are likely significant contributors to the antioxidant capacity of the five spring flowers.

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Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.키워드 : 딥러닝, 신경망, 객체 분류, 컨볼루션신경망