• Food Engineering Progress
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• v.14 no.3
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• pp.208-216
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• 2010

Lab scale experiments were conducted in order to assess the applicability of $CaCO_{3}$-alginate beads as neutralizer for the high cell density cultivation and prepare the direct vat inoculation cultures of isolated sourdough lactic acid bacteria. With increasing the amount of bead and decreasing the diameter of bead in acidic solution, the neutralizing effect of $CaCO_{3}$-alginate bead became higher. In batch process with $CaCO_{3}$-alginate beads, Lactobacillus amylovorus DU-21 isolated from sourdough showed the highest viable cell counts and optical density in MRS broth. The values of viable cell counts and optical density were 9.996 log CFU/mL and 3.97, respectively. Experiments on the conditions which increase viability during lyophilization were carried out and the following results were obtained; 15% glycerol revealed the high cryoprotective effect on the concentrated cultures during lyophilization among the two cryoprotective agents. Consequently, $CaCO_{3}$-alginate beads and 15% glycerol were found to be useful not only to cultivate Lactobacillus amylovorus DU-21 but also to preserve strain.

• Journal of Food Hygiene and Safety
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• v.33 no.6
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• pp.466-473
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• 2018

An analytical method of sodium polyacrylate in processed food products was developed and monitored by using size-exclusion chromatography. GF-7M HQ column and UV/VIS detector were selected based on peak shape and linearity. Flow rate, column oven temperature, and mobile phase were selected as 0.6 mL/min, $45^{\circ}C$, and 50 mM sodium phosphate buffer of pH 9.0, respectively. Samples for analysis of sodium polyacrylate were extracted with 50 mM sodium phosphate buffer of pH 7.0 for 3 hr at $20^{\circ}C$ and 150 rpm. Analytical method validation revealed proper selectivity and calibration curve was selected in the range of 50-500 mg/L, and correlation coefficient of calibration curve was more than 0.9985. Limit of detection of sodium polyacrylate was 10.95 mg/kg and limit of quantification was 33.19 mg/kg. Accuracy and coefficient of variation for sodium polyacrylate analysis was 99.6-127.6%, 3.0-8.3% for intra-day and 94.3-121.9%, 1.3-2.6% for inter-day, respectively. Sodium polyacrylate was detected in 40 samples among monitored 125 processed food products. Detected contents were less than 0.2%, limited by the Food Additives Code. Results suggest the established size-exclusion chromatography method could be used to analyze sodium polyacrylate in processed food products.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Food Hygiene and Safety
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• v.34 no.3
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• pp.283-289
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• 2019

This study was performed to monitor the residual pesticides in agricultural products used in school foodservice in the Gwangju metropolitan area. Risk assessment was also carried out based on the amount of agricultural products consumed. A total of 320 agricultural products supplied to schools in Gwangju were analyzed from 2015 to 2017. The pre-treatment and residual pesticide analysis of these products was conducted in accordance with the second method for multi-residue analysis of pesticides in the Korean food code. The hazard index was calculated by dividing the estimated daily intake (EDI) of pesticides by the acceptable daily intake (ADI). The linearity correlation coefficient for the calibration curve was 0.9923 to 1.0000, LOD 0.004 to 0.019 mg/kg, LOQ 0.012 to 0.057 mg/kg, and recovery was 79.1 to 100.2%. Residual pesticides were detected in 18 (5.6%) of 320 agricultural products used for school foodservice, and one sample of sweet potato stem (0.3%) exceeded the maximum residual limit (MRL). The detection frequency for chili peppers and bell peppers was higher than that for other agricultural products. The frequently-detected pesticides were boscalid and acetamiprid. These results showed that residual pesticide management is needed for chili pepper, bell pepper and sweet potato stem among agricultural products supplied to schools. The hazard index of bifenthrin in sweet potato stem showed the highest (64.18%), and the other pesticides were 0.03-8.23%. These results indicated that agricultural products supplied to schools in Gwangju were safe for consumption. To minimize the intake of residual pesticides, it is necessary to not only thoroughly wash agricultural products but to also ensure the expanded supply of products that are pesticide-free.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.1
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• pp.69-75
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• 2019

Preservatives of cosmetics is managed by positive list in Korea. Positive list requires a proper quantitative analysis method, but the analysis method is still insufficient. In this study, gas chromatography with flame ionization detector was used to simultaneously analyze 14 preservatives in cosmetics. As a result of method validation, the specificity was confirmed by the calibration curves of 14 preservatives showing good linearity correlation coefficient of above 0.9997 except dehydroacetic acid (0.9891). The limits of detection (LOD) and quantification (LOQ) of 14 preservatives were 0.0001 mg/mL ~ 0.0039 mg/mL and 0.0003 mg/mL ~ 0.0118 mg/mL, respectively, but they were 0.0204 mg/mL, 0.0617 mg/mL for dehydroacetic acid, respectively. The precision (Repeatability) of the values was less than 1.0%, but 7.1% for dehydroacetic acid. The Accuracy (% recovery) of 14 preservatives in cosmetics showed 96.9% ~ 109.2%. Finally, this method was applied to 50 cosmetics available in market. Results showed that the commonly used preservatives were chlorophene, phenoxyethanol, benzyl alcohol and parabens. However, the amount of the detected preservatives was within maximum allowed limits established by KFDA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.24-33
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• 2021

For this study, we surveyed concentrations of 8 mycotoxins (aflatoxin B1, B2, G1, G2, ochratoxin A, fumonisin B1, B2 and zearalenone) in agricultural products used for food and medicine by liquid chromatography-tandem mass spectrometry and conducted a risk assessment. Samples were collected at the Yangnyeong Market in Seoul, Korea, between January and November 2019. Mycotoxins were extracted from these samples by adding 0.1% formic acid in 50% acetonitrile and cleaned up by using an ISOLUTE Myco cartridge. The method was validated by assessing its matrix effects, linearity, limit of detection (LOD), limit of quantification (LOQ), recovery and precision using four representative matrices. Matrix-matched standard calibration was used for quantification and the calibration curves of all analytes showed good linearity (r2>0.9999). LODs and LOQs were in the range of 0.02-0.11 ㎍/kg and 0.06-0.26 ㎍/kg, respectively. Sample recoveries were from 81.2 to 118.7% and relative standard deviations lower than 8.90%. The method developed in this study was applied to analyze a total of 187 samples, and aflatoxin B1 was detected at the range of 1.18-7.29 ㎍/kg (below the maximum allowable limit set by the Ministry of Food and Drug Safety, MFDS), whereas aflatoxin B2, G1 and G2 were not detected. Mycotoxins that are not regulated presently in Korea were also detected: fumonisin (0.84-14.25 ㎍/kg), ochratoxin A (0.76-17.42 ㎍/kg), and zearalenone (1.73-15.96 ㎍/kg). Risk assessment was evaluated by using estimated daily intake (EDI) and specific guideline values. These results indicate that the overall exposure level of Koreans to mycotoxins due to the intake of agricultural products used for food and medicine is unlikely to be a major risk factor for their health.

• Journal of Nutrition and Health
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• v.53 no.6
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• pp.629-647
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• 2020

Purpose: This study was undertaken to develop a nutrition quotient for elementary school children (NQ-C) for evaluating the overall dietary quality and eating behaviors. Methods: The NQ-C was developed by implementing 3 stages: item generation, item reduction, and validation. Candidate food behavior checklist (FBC) items of the NQ-C were derived from systematic literature reviews, expert in-depth interviews, statistical analyses of the fifth Korean National Health and Nutrition Examination Survey data, and national nutrition policies and recommendations. For the pilot survey, 260 elementary school students (128 second graders and 132 fifth graders) completed self-administered questionnaires as well as 24-hour dietary intakes, with the help of their parents and survey team staff, if required. Based on the pilot survey results, expert reviews, and priorities of national nutrition policy and recommendations, checklist items were reduced from 41 to 24. A total of 20 items for NQ-C were finally selected from results generated from 1,144 nationwide samples surveyed. Construct validity of the NQ-C was assessed using the confirmatory factor analysis, LInear Structural RELations. Results: Analyses of the exploratory factors of NQ-C identified that 5 dimensions of diet (balance, diversity, moderation, practice and environment) accounted for 46.2% of the total variance. Standardized path coefficients were used as weights of the items. The NQ-C and 5-factor scores of the subjects were calculated using the obtained weights of the FBC items. Conclusion: Our data indicates that NQ-C is a useful and suitable instrument for assessing nutrition adequacy, dietary quality, and eating behaviors of Korean elementary school children.

• Journal of Science Education
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• v.44 no.3
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• pp.273-288
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• 2020

This study aims to investigate the presentation of the Nature of Science (NOS) in integrated science textbooks of the 2015 revised curriculum. The five integrated science textbooks published by the revised 2015 curriculum were analyzed with the conceptual framework of the four themes of the Nature of Science (NOS) (Lee, 2013) based on scientific literacy. The four themes of the NOS are 1. nature of scientific knowledge (theme I), 2. nature of scientific inquiry (theme II), 3. nature of scientific thinking (theme III), and 4. nature of interactions among science, technology, and society. The reliability of the textbooks analysis was measured between two coders by the Cohen's kappa and resulted in between 0,83 and 0,96, which means the results of analysis was consistent and reliable. The findings were as follows. First, overall theme II, nature of scientific inquiry emphasized on the integrated science textbooks of the 2015 revised curriculum by devoting the contents over 40 % in the all five publishing companies' textbooks. Second, while the theme II, nature of scientific inquiry was emphasized on the textbooks regardless of the publishing companies, other themes of the NOS were emphasized in different portions by the publishing companies. Thus, the focus among other three themes of the NOS was presented differently by the publishing companies except that in theme II, nature of scientific inquiry was most emphasized on integrated science textbooks. Third, the presentation of the NOS was identified similarly across the topics of integrated science textbooks except on topic 4. Environment and Energy. The theme IV, nature of interactions among science, technology, and society was emphasized reasonably only in the topic of Environment and Energy of the textbooks. Finally, the presentation of the NOS in the integrated science textbooks of the 2015 revised curriculum were more balanced among the four themes of the NOS with focus on the scientific inquiry compared to the previous curriculum textbooks.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.226-231
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• 2011

Ulmus davidiana var. japonica is a deciduous tree species used for traditional medicine. This study was conducted to investigate the variation of samara, seed, germination and growth characteristics among populations and among individuals within five natural populations of U. davidiana var. japonica distributed in Korea. The ten characteristics of samara and seed, the three germination behaviors as well as the two growth traits were studied in samaras collected from total 32 trees. Statistical analysis of all characteristics showed that there were significant differences among populations as well as among individuals within populations. In this study, the mean characteristics of this species were 13.0 mm in samara length, 9.7 mm in samara width, 1.37 in samara index, 0.015 g in samara weight, 3.07 mm in samara stalk length, 3.85 seed length, 2.66 mm in seed width, 1.46 in seed index, 1.29 mm seed thickness, 0.0062 g in seed weigh, 34.8% in germination percentage, 8.6 days in mean germination time, 3.5 ea./day in gemination rate, 37.7 cm in height and 4.90 mm in root collar diameter. Especially, coefficients of variations in samara weight, germination percentage, germination rate, height and root collar diameter were relatively high (${\geq}30.0%$) compared to other traits. There was no significant relationship between population association and geographical distribution. The results of principal component analysis for 15 characteristics showed that primary four principal components (PC's) explained 100% of the total variation. The first PC accounted for 41.8% of the variability which correlated with morphological traits, the second PC accounted for 32.9% of the variability which correlated with germination behaviors and the third PC accounted for 16.3% of the variability which correlated with growth traits.