• Food Science and Preservation
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• v.24 no.8
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• pp.1103-1112
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• 2017

This study was performed to investigate the effects of different cooking methods (boiling, roasting, stir-frying, and deep-frying) on folate retention in 6 kinds of mushrooms (Beech-, button-, Juda's ear-, oak-, oyster-, and winter-mushrooms) frequently consumed in Korea. In order to assure reliability of analytical data, trienzyme extraction-L casei method was verified and analytical quality control was also evaluated. Folate contents of mushrooms varied by 6.04-64.82 g/100 g depending on the type of mushrooms. and were significantly affected by cooking methods. Depending on cooking methods, folate contents of mushrooms decreased by 22-48%, 2-31%, and 17-56% for Juda's ear-, oak- and oyster-mushrooms, respectively, while 17-90% of folate was increased in Beech mushroom. Overall, the largest weight loss was found in boiled mushrooms, but the lowest one in deep-fried samples. True folate retention rates considering processing factor were less than 100% for all cooked mushrooms except for Beech samples. Overall, folate loss was the largest by boiling with water but the smallest by deep-frying. Both accuracy and precision of trienzyme extraction-L-casei method were excellent based on a recovery close to 100% and coefficient variations less than 3%. Quality control chart of folate analysis (n=26) obtained during the entire study and an international proficiency test (z-score=-0.5) showed that trienzyme extraction-L casei method is reliable enough for production of national folate database.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.11
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• pp.1386-1396
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• 2017

The purpose of this study was to determine the optimum Platycodon grandiflorum root concentrate (PGRC, $65^{\circ}Brix$), fermented P. grandiflorum root extract by Lactobacillus plantarum (FPGRE, $2^{\circ}Brix$), and cactus Chounnyouncho extract (Cactus-E, $2^{\circ}Brix$) for preparation of PGRC stick product with FPGRE using response surface methodology (RSM). The experimental conditions were designed according to a central composite design with 20 experimental points, including three replicates for three independent variables such as amount of PGRC (8~12 g), FPGRE (0~20 g), and Cactus-E (0~20 g). The experimental data for the sensory evaluation and functional properties based on antioxidant activity and antimicrobial activity were fitted with the quadratic model, and accuracy of equations was analyzed by ANOVA. For the responses, sensory and functional properties showed significant correlation with contents of three independent variables. The results indicate that addition of PGRC contributed to increased bitterness and acridity based on the sensory test and antimicrobial activity, addition of FPGRE contributed to increased antioxidant activity and antimicrobial activity, and addition of Cactus-E contributed to increased fluidity based on the sensory test, antioxidant activity, and antimicrobial activity. Based on the results of RSM, the optimum formulation of PGRC stick product was calculated as PGRC 8.456 g, FPGRE 20.00 g, and Cactus-Ex 20.00 g with minimal bitterness and acridity, as well as optimized fluidity, antioxidant activity, and antimicrobial activity.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Food Hygiene and Safety
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• v.38 no.2
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• pp.69-78
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• 2023

This study aimed to investigate the protective effect of enzymatically modified stevia (EMS) on C2C12 cell-based model of dexamethasone (DEX)-induced muscle atrophy to provide baseline data for utilizing EMS in functional health products. C2C12 cells with DEX-induced muscle atrophy were treated with EMS (10, 50, and 100 ㎍/mL) for 24 h. C2C12 cells were treated with EMS and DEX to test their effects on cell viability and myotube formation (myotube diameter and fusion index), and analyze the expression of muscle strengthening or degrading protein markers. Schisandra chinensis Extract, a common functional ingredient, was used as a positive control. EMS did not show any cytotoxic effect at all treatment concentrations. Moreover, it exerted protective effects on C2C12 cell-based model of DEX-induced muscle atrophy at all concentrations. In addition, the positive effect of EMS on myotube formation was confirmed based on the measurement and comparison of the fusion index and myotube diameter when compared with myotubes treated with DEX alone. EMS treatment reduced the expression of muscle cell degradation-related proteins Fbx32 and MuRF1, and increased the expression of muscle strengthening and synthesis related proteins SIRT1 and pAkt/Akt. Thus, EMS is a potential ingredient for developing functional health foods and should be further evaluated in preclinical models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.134-146
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• 2023

Phytophthora root rot (PRR) is a major soybean disease caused by an oomycete, Phytophthora sojae. PRR can be severe in poorly drained fields or wet soils. The disease management primarily relies on resistance genes called Rps (resistance to P. sojae). This study aimed to identify resistance loci associated with resistance to P. sojae isolate 40468 in Daepung × CheonAl recombinant inbred line (RIL) population. CheonAl is resistant to the isolate, while Daepung is generally susceptible. We genotyped the parents and RIL population via high-throughput single nucleotide polymorphism genotyping and constructed a set of genetic maps. The presence or absence of resistance to P. sojae was evaluated via hypocotyl inoculation technique, and phenotypic distribution fit to a ratio of 1:1 (R:S) (χ² = 0.57, p = 0.75), indicating single gene mediated inheritance. Single-marker association and the linkage analysis identified a highly significant genomic region of 55.9~56.4 megabase pairs on chromosome 18 that explained ~98% of phenotypic variance. Many previous studies have reported several Rps genes in this region, and also it contains nine genes that are annotated to code leucine-rich repeat or serine/threonine kinase within the approximate 500 kilobase pairs interval based on the reference genome database. CheonAl is the first domestic soybean genotype characterized for resistance against P. sojae isolate 40468. Therefore, CheonAl could be a valuable genetic source for breeding resistance to P. sojae.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.