• Title/Summary/Keyword: range detection

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Microbiological Quality and Potential Pathogen Monitoring for Powdered Infant Formulas from the Local Market (영유아용 분말 조제분유의 미생물 품질분석과 위해세균 모니터링)

  • Hwang, Ji-Yeon;Lee, Ji-Youn;Park, Jong-Hyun
    • Food Science of Animal Resources
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    • v.28 no.5
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    • pp.555-561
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    • 2008
  • Ninety-nine samples of powdered infant formula in a market were collected from the local market and their contaminations for total aerobic bacteria, coliform, FAO/WHO Category A, B, and C pathogens were analyzed. Total aerobic bacteria were detected in 92 of 99 samples (93%) at levels of $1.83{\pm}0.68\;Log\;MPN/g$. These levels were below legal levels specified for infant formulas except for one sample detected by 4.5 Log CFU/g. Coliform was detected in 12 of 99 samples (12%) at levels of $1.26{\pm}1.03\;Log\;MPN/g$ whereas non-detection was required according to the specification of coliform in infant formulas. Escherichia coli was detected in 1 of 99 samples by 0.48 Log MPN/g. Salmonella and Enterobacter sakazakii among Category A weren't detected in all the samples. Enterobacteriaceae, Category B group, were detected in 25 samples of total 99 samples (25%) by $0.83{\pm}1.37\;Log\;MPN/g$. Enterobacteriaceae identified by API 20E were Escherichia vulneris, Es. hermannii, Pantoea spp., Citrobacter koseri, Klebsiella pneumoniae, En. cloaceae. Bacillus cereus among Category C was highly detected in 29 of 99 samples (29%) at levels of $0.69{\pm}0.32\;Log\;MPN/g$ with the most probable number count method, which were below legal levels for the specification of B. cereus in infant formulas. Clostridium perfringens, E. coli O157, Staphyloccus aureus, Listeria monocytogenes, Yersinia enterocolitica, and Campylobacter jejuni/coli were not detected. Contamination level of major pathogens was low and falls within the range of specification of infant formulas. However, Enterobacteriaceae and B.cereus showed the high prevalence and some Enterobacteriaceae causing disease were detected. Therefore, it is necessary to monitor the potential pathogens continually and reduce them to improve the microbial quality of non-sterilized powdered infant formulas.

Optimization of HPLC Method and Clean-up Process for Simultaneous and Systematic Analysis of Synthetic Color Additives in Foods (식품 중 타르색소의 동시분석 및 계통분석을 위한 HPLC 분석조건 및 정제과정 확립)

  • Park, Sung-Kwan;Hong, Yeun;Jung, Yong-Hyun;Lee, Chang-Hee;Yoon, Hae-Jung;Kim, So-Hee;Lee, Jong-Ok
    • Korean Journal of Food Science and Technology
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    • v.33 no.1
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    • pp.33-39
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    • 2001
  • To develop a method for separation process using Sep-pak $C_18$, simultaneous and systematic analysis of 8 permitted and 11 non-permitted synthetic food colors in Korea, optimization of analysis conditions for reverse phase ion-pair high performance liquid chromatography was carried out. For the best result of Sep-pak $C_18$ separation the pH of color standard mixture solution was $5{\sim}6$ and 0.1% HCl-methanol solution were set as eluent. The colors eluated from Sep-pak $C_18$ cartridge were determined and confirmed by high performance liquid chromatography with a photodiode array detector at 420 nm for yellow colors type, at 520 nm for red colors type, at 600 nm for blue and green colors type and at 254 nm for mixed colors. Conditions for HPLC analysis were as follows: column, Symmetry $C_18$ (5 m, 3.9 mm $i.d.{\times}150\;mm$); mobile phase, 0.025 M ammonium acetate (containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (65 : 25 : 10) and 0.025 M ammonium acetate(containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (40 : 50 : 10); flow rate, 1 mL/min. It takes 35 minutes for simultaneaus analysis and 18 minutes for systematic analysis. The detection limits range of each colors were $0.01{\sim}0.05\;{\mu}g/g$.

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Robust Eye Localization using Multi-Scale Gabor Feature Vectors (다중 해상도 가버 특징 벡터를 이용한 강인한 눈 검출)

  • Kim, Sang-Hoon;Jung, Sou-Hwan;Cho, Seong-Won;Chung, Sun-Tae
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.1
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    • pp.25-36
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    • 2008
  • Eye localization means localization of the center of the pupils, and is necessary for face recognition and related applications. Most of eye localization methods reported so far still need to be improved about robustness as well as precision for successful applications. In this paper, we propose a robust eye localization method using multi-scale Gabor feature vectors without big computational burden. The eye localization method using Gabor feature vectors is already employed in fuck as EBGM, but the method employed in EBGM is known not to be robust with respect to initial values, illumination, and pose, and may need extensive search range for achieving the required performance, which may cause big computational burden. The proposed method utilizes multi-scale approach. The proposed method first tries to localize eyes in the lower resolution face image by utilizing Gabor Jet similarity between Gabor feature vector at an estimated initial eye coordinates and the Gabor feature vectors in the eye model of the corresponding scale. Then the method localizes eyes in the next scale resolution face image in the same way but with initial eye points estimated from the eye coordinates localized in the lower resolution images. After repeating this process in the same way recursively, the proposed method funally localizes eyes in the original resolution face image. Also, the proposed method provides an effective illumination normalization to make the proposed multi-scale approach more robust to illumination, and additionally applies the illumination normalization technique in the preprocessing stage of the multi-scale approach so that the proposed method enhances the eye detection success rate. Experiment results verify that the proposed eye localization method improves the precision rate without causing big computational overhead compared to other eye localization methods reported in the previous researches and is robust to the variation of post: and illumination.

Development and Validation of Analytical Method for Determination of Biphenyl Analysis in Foods (식품 중 비페닐 분석법 개발 및 유효성 검증)

  • Kim, Jung-Bok;Kim, Myung-Chul;Song, Sung-Woan;Shin, Jae-Wook
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.46 no.4
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    • pp.459-464
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    • 2017
  • Biphenyl is used as an intermediate in the production of crop protection products, a solvent in pharmaceutical production, and as a component in the preservation of citrus fruits in many countries. Biphenyl is not authorized for use and also does not have standards or specifications as a food additive in Korea. National and imported food products are likely to contain biphenyl. Therefore, control and management of these products is required. In this study, a simple analytical method was developed and validated using HPLC to determine biphenyl in food. These methods are validated by assessing certain performance parameters: linearity, accuracy, precision, recovery, limit of detection (LOD), and limit of quantitation (LOQ). The calibration curve was obtained from 1.0 to $100.0{\mu}g/mL$ with satisfactory relative standard deviations (RSD) of 0.999 in the representative sample (orange). In the measurement of quality control (QC) samples, accuracy was in the range of 95.8~104.0% within normal values. The inter-day and inter-day precision values were less than 2.4% RSD in the measurement of QC samples. Recoveries of biphenyl from spiked orange samples ranged from 92.7 to 99.4% with RSD between 0.7 and 1.7% at levels of 10, 50, and $100{\mu}g/mL$. The LOD and LOQ were determined to be 0.04 and $0.13{\mu}g/mL$, respectively. These results show that the developed method is appropriate for biphenyl identification and can be used to examine the safety of citrus fruits and surface treatments containing biphenyl residues.

Comparative Quantitative Study of Surfactant Protein C mRNA by Filter Hybridization and Solution Hybridization in Rats (Filter Hybridization과 Solution Hybridization 방법에 의한 백서 Surfactant Protein C mRNA 정량측정의 비교)

  • Kim, Jin-Ho;Sohn, Jang-Won;Yang, Seok-Chul;Yoon, Ho-Joo;Shin, Dong-Ho;Park, Sung-Soo
    • Tuberculosis and Respiratory Diseases
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    • v.51 no.6
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    • pp.517-529
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    • 2001
  • Background : Surfactant protein C(SP-C) is a hydrophobic 5,000 dalton molecule. SP-C has the primary roles in accelerating surface spreading of a surfactant phospholipid. The filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. Methods : The authors measured the SP-C mRNA levels quantitatively using solution hybridization and filter hybridization assays to obtain a standard curve equation to quantify the mRNA of unknown samples comparatively. Results : 1. The minimum level of the specimens by solution hybridization was 3 pg for SP-C mRNA. 2. The standard curve equation of the solution hybridization assay between the counts per minute(Y) and the SP-C mRNA transcript input(X) was Y=6.46 X+244. The correlation coefficient was 0.99. 3. The minimum detection level of specimens by filter hybridization was 0.1 ng for SP-C mRNA. 4. The standard curve equation of the filter hybridization assay between the counts per minute(Y) and SP-C mRNA transcript input(X) is Y=2541.6 X+252.7. The correlation coefficient was 0.99. Conclusions : A comparison of CPM/filter in the linear range allowed an accurate and reproducible estimation of the SP-C mRNA copy number. Filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. It is ideally suited to situations where accurate quantitation of multiple samples is required.

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Development of Analysis Method of Caffeine and Content Survey in Commercial Foods by HPLC (HPLC를 이용한 카페인의 분석법 개발 및 시판 식품중 함유량 조사)

  • Kim, Hee-Yun;Lee, Young-Ja;Hong, Ki-Hyoung;Lee, Chul-Won;Kim, Kil-Saeng;Ha, Sang-Chul
    • Korean Journal of Food Science and Technology
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    • v.31 no.6
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    • pp.1471-1476
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    • 1999
  • A simple and practical method for determination of caffeine in foods was developed. The analysis of caffeine was performed by reverse phase high performance liquid chromatography using a ${\mu}-Bondapak\;C_{18}$ column at isocratic condition with methanol-acetic acid-water(20 : 1 : 79) on UV detector at 280 nm. The clean-up and extraction of caffeine in samples were based on a simple pretreatment using a Sep-Pak $C_{18}$ cartridge. Recovery rates obtained with this method for cider, candy, cookie, milk, ice cream and persimmon leaf tea were 99.23%, 99.50%, 99.17%, 99.37%, 98.93% and 99.10% respectively. And the detection limit of caffeine was $0.1\;{\mu}g/mL$. With this method, the range of caffeine contents extracted from coffee, green tea, black tea, Oolong tea(tea bag), soft drinks, ice cream, milk and commercial confectionery were $3.38{\sim}37.50\;mg/g,\;16.30{\sim}26.10\;mg/g,\;10.80{\sim}16.65\;mg/g,\;11.25\;mg/g,\;0.06{\sim}0.11\;mg/g,\;0.04{\sim}0.44\;mg/g,\;0.04{\sim}0.39\;mg/g\;and\;0.10{\sim}1.80\;mg/g$, respectively. But caffeine was not detected in the other tea such as Acanthopanax sessiliflorum tea, Angelica gigas tea, Angelica tea, Arrow root tea, Duchu'ng tea, Dunggulle tea, Ganoerma lucidum tea, Ginger tea powder, Persimmon leaf tea, Ssanghwa tea and Cocoa mix powder.

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Analysis and Uncertainty Estimation of Zearalenone in Cereal-Based Products by LC-MS/MS (LC-MS/MS를 이용한 곡류가공품의 제랄레논 분석과 측정불확도 추정)

  • Choi, Eun Jung;Kang, Sung Tae;Jung, So Young;Shin, Jae Min;Jang, Min Su;Lee, Sang Me;Kim, Jung Hun;Chae, Young Zoo
    • Korean Journal of Food Science and Technology
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    • v.44 no.6
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    • pp.658-665
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    • 2012
  • A survey of zearalenone contamination was conducted on cereal-based products by using an immunoaffinity column with LC-MS/MS. The calibration curve showed good lineality, with correlation coefficients ($R^2$) of 0.999 in the concentration range from 1 to 250 ng/mL. The limits of detection and quantification were approximately $0.3{\mu}g/kg$ and $1.0{\mu}g/kg$, respectively. The recoveries in the barley tea, Misutgaru and snack ranged from 73.6-107.8%. Zearalenone was detected in 10 samples (11.2% incidence). The highest zearalenone contamination level was $29.7{\mu}g/kg$ in the Misutgaru. This survey was conducted with uncertainty of measurement. The expanded uncertainty for zearalenone was estimated to be $44.9{\pm}5.0{\mu}g/kg$ (k=2, 95% confidence level) and $128.7{\pm}7.9{\mu}g/kg$ (k=2, 95% confidence level) for barley tea, $30.7{\pm}5.8{\mu}g/kg$ (k=2, 95% confidence level) and $173.7{\pm}14.9{\mu}g/kg$ (k=2.26, 95% confidence level) for Misutgaru, and $37.2{\pm}7.4{\mu}g/kg$ (k=2.31, 95% confidence level) and $151.0{\pm}10.4{\mu}g/kg$ (k=2, 95% confidence level) snack at the level of $41.7{\mu}g/kg$ and $166.7{\mu}g/kg$, respectively.

Method Validation and Quantification of Lutein and Zeaxanthin from Green Leafy Vegetables using the UPLC System (UPLC를 이용한 lutein과 zeaxanthin의 분석법 검증 및 엽채류에서의 정량적 평가)

  • Kim, Suna;Kim, Ji-Sun
    • Korean Journal of Food Science and Technology
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    • v.44 no.6
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    • pp.686-691
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    • 2012
  • The objective of this research is to present method development and validation for the simultaneous determination of lutein and zeaxanthin using ultra performance liquid chromatography (UPLC). Also, rapid quantification was performed on six green leafy vegetables (Allium tuberosum, Aster scaber, Hemerocallis fulva, Pimpinella brachycarpa, Sedum sarmentosum and Spinacia oleracea) that are commonly consumed in Korea. Separation and quantification were successfully achieved with a Waters Acquity BEH C18 ($50{\times}2.1mm$, $1.7{\mu}m$) column by 85% methanol within 5 min. Two compounds showed good linearity ($r^2$ > 0.9968) in $1-150{\mu}g/mL$. Limit of detection (LOD) and quantification (LOQ) for lutein and zeaxanthin were 1.7 and 5.1 g/mL and 2.1 and 6.3 g/mL, respectively. The RSD for intra- and inter-day precision of each compound was less than 10.69%. The recovery of each compound was in the range of 91.75-105.13%. Aster scaber and Spinacia oleracea contained significantly higher amounts of lutein ($4.06{\pm}0.24$ and $3.97{\pm}0.10mg$/100 g of fresh weight), respectively.

Monitoring and Exposure Assessment of Pesticide Residues in Domestic Agricultural Products (국내 유통 다소비 농산물의 잔류농약 모니터링 및 노출평가)

  • Kang, Namsuk;Kim, Seongcheol;Kang, Yoonjung;Kim, Dohyeong;Jang, Jinwook;Won, Sera;Hyun, Jaehee;Kim, Dongeon;Jeong, Il-Yong;Rhee, Gyuseek;Shin, Yeongmin;Joung, Dong Yun;Kim, Sang Yub;Park, Juyoung;Kwon, Kisung;Ji, Youngae
    • The Korean Journal of Pesticide Science
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    • v.19 no.1
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    • pp.32-40
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    • 2015
  • This study was implemented to evaluate food safety on residual pesticides in agricultural products of Korea and to use as a data base for the establishment of food policy. A total of 196 pesticide upon these products were analyzed using multi class pesticide multiresidue methods of Korean Food Code, and 232 samples of 15 agricultural products collected from 9 regions were supplied for this study. In the results, 64 kinds of pesticides were detected in 53 samples, chlorpyrifos and procymidone of them were shown a high frequency of detection in the analyzed pesticides. Among them, two samples (chlorpyrifos in perilla leaves and picoxystrobin in peach) were detected over Maximum Residue Limits (MRLs). The levels of the detected pesticide residues were within safe levels. Also, the intake assessment for pesticide residues including chlorpyrifos at multi pesticide residue monitoring were carried out. The result showed that the ratio of EDI (estimated daily intake) to ADI (acceptable daily intake) was 0.001~0.902% which means that the detected pesticide residues were in a safe range so that residual pesticides in the agricultural products in Korea are properly controlled.

Predictive Clustering-based Collaborative Filtering Technique for Performance-Stability of Recommendation System (추천 시스템의 성능 안정성을 위한 예측적 군집화 기반 협업 필터링 기법)

  • Lee, O-Joun;You, Eun-Soon
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.119-142
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    • 2015
  • With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.