• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.158-169
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• 2019

Ergot alkaloids are mycotoxin produced by fungi of the Claviceps genus, mainly by Claviceps purpurea in EU. Recently obtained informations indicates necessity for control the ergot in imported grains. Recent occurrence data of ergot alkaloids from EU countries indicate the necessities of management and control these toxins from the imported grains like rye, wheat, oat etc. The aim of this study is to optimize the liquid chromatography-tandem mass spectrometry method for determination of ergot alkaloids (ergometrine, ergosine, ergotamine, ergocornine, ergocryptine, ergocristine and their epimers (-inines) from grain and grain-based food. The test method was optimized by extracting the sample with acetonitrile containing 2 mM ammonium carbonate, purification with Mycosep cartridge, and instrumental analysis by LC-MS/MS using Syncronis C18 column. The standard calibration curves showed linearity with correlation coefficents; $R^2$ >0.99. Mean recoveries ranged from 72.0 to 111.3% at three different fortified levels (20, 50, and $100{\mu}g/kg$). The correlation coefficient expressed as precision was within the range of 1.9-12.9%. The limit or quantifications (LOQ) ranged from 0.012 to $0.058{\mu}g/kg$. The developed analytical method met the criteria of AOAC Int. and CAC validation parameters like accuracy and sensitivity. As a result, it was confirmed that the test method developed in this study is suitable for the simultaneous analysis of six species of ergot alkaloid from grains and grain products.

• Journal of Food Hygiene and Safety
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• v.36 no.4
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• pp.298-309
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• 2021

In this study we sought to develop a simultaneous analysis method for cis-bixin and cis-norbixin, the main components, to detect annatto pigment in food. To establish the optimal test method, the HPLC analysis methods of the European Food Safety Authority (EFSA), Japan's Ministry of Health, Labor and Welfare (MHLW), and National Institute of Food and Drug Safety Evaluation (NIFDS) were compared and reviewed. In addition, a new pretreatment method applicable to various foods was developed after selecting conditions for simultaneous high-performance liquid chromatography (HPLC) analysis in consideration of linearity, limit of detection (LOD), limit of quantification (LOQ), and analysis time. The HPLC analysis method of NIFDS showed the best linearity (R² ≥ 0.999), exhibiting low detection and quantification limits for cis-norbixin and cis-bixin as 0.03, 0.05 ㎍/mL, and 0.097, 0.16 ㎍/mL, respectively. All previously reported pretreatment methods had limitations in various food applications. However, the new pretreatment method showed a high recovery rate for all three main food groups of fish meat and meat products, processed cheese and beverages. This method showed an excellent simultaneous recovery rate of 98% or more for cis-bixin and cis-norbixin. The HPLC analysis method with a new pretreatment method showed high linearity with a coefficient of determination (R²) of 1 for both substances, and the accuracy (recovery rate) and precision (%RSD) were 98% and between 0.4-7.9, respectively. From this result, the optimized analytical method was considered to be very suitable for the simultaneous analysis of cis-bixin and cis-norbixin, two main components of annatto pigment in food.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.2
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• pp.185-193
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• 2020

Objectives : The aim of this study was to develop and to verify the Korean version of the Eating Disorder Diagnosis Scale DSM-5 (K-EDDS) as a web-based diagnostic system, which enables rapid diagnosis of patients for early intervention. Methods : A total of 119 persons participated in the study, including patients with eating disorders (n=38) and college students (n=81). Along with the paper-and-pencil SCOFF, all participants completed the web-based K-EDDS, the Eating Disorder Examination-Questionaire (EDE-Q), and the Clinical Impairment Assessment Questionnaire (CIA). The semi-structured interview using the Eating Disorder Examination Interview (EDE) was conducted for participants with two or more SCOFF scores. Within two weeks, the web-based K-EDDS, the EDE-Q, and the CIA were re-tested. Results : In the exploratory factor analysis, four factors were extracted : body dissatisfaction, binge behaviors, binge frequency and compensatory behaviors. The four subscales of the web-based K-EDDS had significant correlation with each of the four subscales of the EDE-Q. The internal consistency of the web-based K-EDDS was highly satisfactory (Cronbach's alpha=0.93). The diagnostic agreement between the web-based K-EDDS and the EDE was excellent (96.83%), and the web-based K-EDDS's test-retest diagnostic agreement was fairly good (92.86%). The web-based K-EDDS and the CIA also showed significant differences between patients and general population, supporting discriminant validity. Conclusions : This study suggested that the web-based K-EDDS is a valid tool for assisting diagnosis of eating disorders based on DSM-5 in clinical and research fields.

• Journal of Nutrition and Health
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• v.54 no.1
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• pp.76-94
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• 2021

Purpose: This study was implemented to develop and validate a semi-quantitative food frequency questionnaire (SQ-FFQ) to assess the dietary intake of married migrant women in multicultural families. Methods: The SQ-FFQ consisting of 41 food items was selected based on the information of frequently consumed foods from the preliminary studies on dietary intake survey for married migrant women or a Korean Health and Nutrition Examination survey. Thirty-five subjects who visited the Multicultural Family Support Center in Seoul and Daejeon area completed their three-day diet records and SQ-FFQ 2 times. The reproducibility and relative validity of SQ-FFQ were assessed by comparing the 1st and 2nd SQ-FFQ and by comparison with the three-day diet records, respectively. Results: The reliability of SQ-FFQ, which was examined 1-2 weeks apart, showed no significant difference in the energy and macronutrient intake (carbohydrate, fat, protein), while the average intakes of vitamin A, riboflavin, calcium, and potassium were slightly higher in the 2nd than 1st SQ-FFQ. All correlation coefficients for the reliability for nutrients were statistically significant, ranging from 0.453 to 0.885. The mean intakes of energy, carbohydrate, vitamin C, thiamin, folate, vitamin B12, calcium, and sodium obtained from the SQ-FFQ were greater than those of the 3-day dietary records. The Spearman's correlation coefficient between the 2 methods was the highest for plant fat (r = 0.602; p < 0.001) and relatively higher for energy, carbohydrate, fat, and thiamin ranging from 0.512 to 0.599; and protein, riboflavin, niacin, folate ranging from 0.335 to 0.499, while no significant correlation was observed for vitamin A, C, calcium, iron, and sodium. Conclusion: The developed SQ-FFQ for married migrant women in multicultural families in this study appears to be useful for estimating the nutritional status, particularly energy, carbohydrates, fat, and vitamin B group.

• 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 Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of fish pathology
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• v.34 no.1
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• pp.71-80
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• 2021

Cephalexin, a semi-synthetic cephalosporin antibiotic, has long been used in fish aquaculture in various countries under legal authorization. The drug is thus widely available for use in other aquatic species except fishes like the crustacean whiteleg shrimp. This study aims to develop a sensitive method for laboratory residue studies to adopt in withdrawal period determinations. Through repeated trials from the existing methods developed for other food animal tissues, it was possible to achieve a sensitive high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method. The results showed that at a concentration of 0.1 mg/kg, the recovery rate was 81.79%, and C.V. value was 8.2%, which meet the recovery rate and C.V. recommended by Codex guideline. After satisfactory validation of analytical procedures, applicability to the shrimp tissue was confirmed in experimentally cephalexin-treated whiteleg shrimp. As a result, most muscle samples were detected below the limit of quantification (0.05 mg/kg) after day 3, and most hepatopancreas samples were detected below the limit of quantification after day 14. In particular, the limit of quantification 0.05 ppm with the presently developed method suggests sufficient sensitive over the current legal maximum residue limit of 0.2 mg/kg set for fishes.

• Korean Journal of Food Science and Technology
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• v.52 no.6
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• pp.565-572
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• 2020

The 3-monochloropropane-1,2-diol (3-MCPD) is a contaminant that occurs in foodstuffs in its free form as well as in its bound form. The objective of this study was to evaluate the effects of emulsifier, frying temperature, and the amounts of salt and oil on the formation of bound 3-MCPD in a fried snack model. Emulsifier affected the formation of bound 3-MCPD; furthermore, it was observed that the largest amount of bound 3-MCPD was detected in the fried snack model when glycerin esters of fatty acids were used as emulsifiers. Frying temperature also affected the formation of bound 3-MCPD, which increased significantly as the frying temperature increased from 145 to 190℃. In addition, salt affected the formation of bound 3-MCPD. As the amount of salt increased, the amount of bound 3-MCPD also increased significantly. Moreover, it was observed that the amount of oil did not affect the formation of bound 3-MCPD. These results will aid in the reduction of bound 3-MCPD in fried snacks.

• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.228-238
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• 2021

The aim of this research was to develop a rapid and easy multi-residue method for determining dimethipin, omethoate, dimethipin, chlorfenvinphos and azinphos-methyl in agricultural products (hulled rice, potato, soybean, mandarin and green pepper). Samples were prepared using QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) and analyzed using gas chromatography-tandem mass spectrometry (GC-MS/MS). Residual pesticides were extracted with 1% acetic acid in acetonitrile followed by addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium acetate. The extracts were cleaned up using MgSO₄, primary secondary amine (PSA) and octadecyl (C₁₈). The linearity of the calibration curves, which waas excellent by matrix-matched standards, ranged from 0.005 mg/kg to 0.3 mg/kg and yielded the coefficients of determination (R²) ≥ 0.9934 for all analytes. Average recoveries spiked at three levels (0.01, 0.1, 0.5 mg/kg) and were in the range of 74.2-119.3%, while standard deviation values were less than 14.6%, which is below the Codex guideline (CODEX CAC/GL 40).

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.