• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.9
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• pp.1336-1343
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• 2005

In the present study, we screened candidates for enhancing insulin action and secretion from Cinnamomum camphora (CC) fractions, in 3T3-L1 adipocytes and Min6 cells by investigating insulin- stimulated glucose uptake and glucose-stimulated insulin secretion, respectively. CC were extracted by $70\%$ ethanol followed by XAD-4 column chromatography with serial mixture solvents of methanol and water, and the fractional extractions were utilized for determining insulin action and secretion, and $\alpha$-glucoamylase suppressing activity, A significant insulin-stimulated glucose uptake was observed in 3T3-L1 adipocytes, giving 0.5 or $5{\mu}g/mL$ of $40\%\;and\;60\%$ methanol fractions plus 0.2 nM insulin, compared to the treatment of DMSO plus 0.2 nM insulin. The treatments of $40\%\;and\;60\%$ methanol fractions plus 0.2 nM insulin reached the glucose uptake of 10 nM insulin treatment. The $40\%$ methanol fraction increased triglyceride accumulation by stimulating differentiation and triglyceride synthesis similar to pioglitazone, PPAR-$\gamma$ agonist. No inhibition of $\alpha$-glucoamylase activity of CC fractions was observed. They did not modulate the insulin secretion capacity In either low or high glucose media. These results suggest that $40\%$ methanol fraction contains a potential insulin sensitizer to have a similar function of PPAR-$\gamma$ agonist. Crude CC extract may improve glucose utilization by enhancing insulin-stimulated glucose uptake without elevating glucose stimulated insulin secretion.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.163-173
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• 2011

The objective of this study was to determine microbiological risk factors in hot pepper farms for the application of good agricultural practices (GAP). Samples were collected from cultivation environments and utensils, plants, workers, and air at 3 hot pepper farms located in Cheongsong, Korea and were tested to detect sanitary indications [aerobic plate bacteria (APC), coliform, and Escherichia coli], foodborne pathogens, and fungi. APC, coliform, and fungi were detected at the levels of 0.7~6.2, 0.2~4.7, and 0.4~4.3 log CFU, respectively, in the three farms. Four (4.4%; l leaf, l irrigation water, and 2 soil) of 90 samples collected were revealed to be E. coli positives. For foodborne pathogens, Staphylococcus aureus was only detected at $1.0log\;CFU/100cm^2$ in the worker's cloth of B farm, and Bacillus cereus was detected at the levels 1.0~2.5 log CFU in the cultivation environments and utensils and worker of B and C farms. However, other pathogens were not detected. The results demonstrated potential microbiological risks for hot pepper cultivated in the farms. Therefore, a management system to minimize the microbial risk such as GAP is required to ensure the safety of hot pepper.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.475-483
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• 2023

This study was conducted to determine the optimal irrigation starting point by analyzing tree growth, physiological responses, fruit quality, and productivity in peach orchards. Seven-year-old 'Kawanakajima Hakuto' peach trees were used in an experimental field (35°49'30.4"N, 127°01'33.2"E) located within the National Institute of Horticultural and Herbal Science located in Wanju-gun, Jeollabuk-do. The irrigation starting point was set with four levels of -20, -40, -60, and -80 kPa from June to September 2022. While there were no significant differences in increase of trunk cross-section area and leaf area among treatments, shoot length and diameter decreased in the -80 kPa and -20 kPa treatments. The photosynthetic rate measured in August was highest for -60 kPa (17.7 μmol·m^-2·s^-1), followed by -40 kPa (15.6 μmol·m^-2·s^-1), -20 kPa (14.5 μmol·m^-2·s^-1) and -80 kPa (14.0 μmol·m^-2·s^-1). SPAD value measured in May and August was lower in the -80 kPa and -20 kPa treatments than in the -60 kPa and -40 kPa treatments. The harvest date reached three days earlier in the -20 kPa treatment compared to other treatments. The fruit weight was highest in the -60 kPa (379.1 g), followed by -40 kPa (344.0 g), -80 kPa (321.0 g) and -20 kPa (274.9 g). Firmness was the lowest in the -20 kPa treatment. The soluble solid content was highest in the -60 kPa treatment (13.3°Bx).The ratio of marketable fruits was highest in the -60 kPa treatment (50.7%) and lowest in the -80 kPa treatment (23.4%). In conclusion, we suggest that setting the irrigation starting point at -60 kPa could improve the fruit quality and yield in peach orchards.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.