• Horticultural Science & Technology
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• 제28권3호
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• pp.423-428
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• 2010

The quality change of musk melons, divided into ripened (90 days) and over-ripened (92 days) set by the formal day maturing melons, was investigated during marketing period at both 10 and $25^{\circ}C$. The rate of weight loss was increased in all samples as the storage period passed and greater in ripened melons than over-ripened melon. The hardness decreased in both well and over-ripened melon as the storage period passed. Furthermore, changes in hardness were prevented in fruit stored at $10^{\circ}C$ compared to fruit stored at $25^{\circ}C$. Immediately after harvest, the solid solubility of over-ripened melon was 14.6%, while that of ripened fruit was 12.8%. The respiration rate of both well and over-ripened melon increased temporarily when stored at $25^{\circ}C$, which is characteristic of climacteric fruits during the first day of storage; however, no change in respiration rate was observed in fruit stored at $10^{\circ}C$. When sensory evaluation was conducted, there were no differences observed in flavor and taste among samples. However, with the exception of over-ripened melon, the texture of all samples increased significantly with storage time when melon was stored at $25^{\circ}C$. The score of overall acceptability remained high for 12 days in both well and over-ripened melon, while that of ripened melon stored at $10^{\circ}C$ and over-ripened melon stored $25^{\circ}C$ remained high for 7 and 5 days, respectively (p<0.05).

• Korean Journal of Food Science and Technology
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• 제28권1호
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• pp.127-136
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• 1996

Mak-kimchi (shredded kimchi) which was prepared in a commercial factory was packed in bottle (200 g) under vacuum (560 mmHg) or atmosphere, and chemical characteristics and microbiological parameters were monitored during storage at 5, 15 and $25^{\circ}C$, respectively. Optimum ripening time of the kimchi at different temperature were 2 days at $25^{\circ}C$, 5 days at $15^{\circ}C$ and more than 60 days at $5^{\circ}C$. By vacuum treatment pH and acidity changes in kimchi were considerably retarded. The vacuum of each bottle released within 1 or 2 days at 25 or $15^{\circ}C$, respectively but the pack at $5^{\circ}C$ maintained more than 380 mmHg vacuum for 36 days and then the vacuum slowly released. The colour of kimchi (lightness, redness, yellowness) in bottle increased sharply at $25^{\circ}C$ and $15^{\circ}C$ but sustained a stable level with vacuum treatment at $5^{\circ}C$. The range of total viable count of kimchi in bottle was $10^7{\sim}10^{10}/ml$. The number decreased by storage temperature drop to $5^{\circ}C$ and even more vacuum treatment than atmosphere treatment at $5^{\circ}C$. Lactobacillus brevis, L. plantarum, L. acidophilus, Aerococcus viridans and Streptococcus faecium subsp. casseliflavus were identified in bottled kimchi and L. brevis and L. plantarum contributed to the main function during kimchi fermentation. Those main lactic acid bacteria decreased in numbers at $5^{\circ}C$ than 25 or $15^{\circ}C$ and even more declined in case of vacuum treatment.

• Korean Journal of Remote Sensing
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• 제39권5_3호
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.