• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.10
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• pp.1638-1648
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• 2013

To promote the utilization of wild edible plants, this study examined blanching, drying, and fermentation as methods for enhancing the functionality of Hemerocallis coreana Nakai. Specimens fermented for 24 hours at a fermentation temperature of $50^{\circ}C$, with a relative humidity of 65%, contained the highest amount of organic acid (18,109.82 mg/100 g). For the blanched; specimens, total organic acid content decreased about 30% compared with the freeze-dried specimens. The main organic acid of Hemerocallis coreana Nakai was confirmed as succinic acid. After fermentation, free sugars decreased; in particular, specimens fermented at a relative humidity of 80% showed a 32~75% reduction in free sugar compared with the freeze-dried specimens. In terms of amino acid content, Hemerocallis coreana Nakai was mainly composed of valine, isoleucine, and phenylalanine. In fermented specimens the total amino acid content was highest in a moderately fermented (17 hr) specimen, (1,010.71 mg/100 g fresh wt.), but decreased in the maximally fermented (24 hr) specimen. The longer the fermentation, the higher the decrease in non-essential amino acids content, while the content of more essential amino acids consistently increased. In conclusion, since seasoned Hemerocallis coreana Nakai contains a considerable amount of glutamine and asparagine, it has a fresh sour and sweet taste; thus, it will likely be a highly preferred wild edible plant. Also, with an increase of essential amino acids after fermentation, Hemerocallis coreana Nakai is excellent in terms of nutrition. Thus, it may be possible to utilize fermented Hemerocallis coreana Nakai in the development of diverse products.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.