• Title/Summary/Keyword: Regression analysis E. coli

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Correlation between Ground Water Quality Parameters and Total Coliforms in Livestock Farm (가축이용 지하수에서의 대장균군 출현과 수질인자의 상관성)

  • Choi, Chul-Mann;Yun, Sun-Gang;Kim, Min-Kyeong;Kim, Jin-Ho;Ko, Byong-Gu;Park, Seong-Jin;Ryu, Hui-Yong
    • Korean Journal of Environmental Agriculture
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    • v.28 no.1
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    • pp.86-91
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    • 2009
  • The main purpose of this study is to search the water factor which influences to occurrence of the total coliforms. Occurrences rate of the total coliforms and the E coli were 57.9% 10.5% in 2007 and 47.4%, 23.7% in 2008 respectively. According to the result which examines the correlation analysis and a regression analysis, most the water factor which is effect was $Na^+$(0.497, p<0.05) in appearance of the total coliforms and was $Cl^-$(0.622, p<0.01) in appearance of the E coli. The water factor that simultaneously influences to the total coliforms and the E. coli appearances was the $Cl^-$. The predictable regression formula for appearance rate of the total coliforms was expressed as 0.462 + 0.028 [$Na^+$] - 0.644 [$COD_{Mn}$] - 8.889 [$PO_4-P$](R = 0.930, $R^2$ = 0.866, adjusted $R^2$ = 0.839, p<0.05), and that of the E coli was described as -0.012 + 0.004 [$Cl^-$](R = 0.622, $R^2$ = 0.387, adjusted $R^2$ = 0.351, p<0.05).

Influence of Climate Factors on the Occurrence of Pathogenic Escherichia coli Food Poisoning in Korea (우리나라에서 병원성 대장균 식중독 발생과 기후요소의 영향)

  • Kim, Jong-Gyu
    • Journal of Environmental Health Sciences
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    • v.46 no.3
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    • pp.353-358
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    • 2020
  • Objectives: An outbreak of pathogenic Escherichia coli food poisoning in Korea was first reported in 1998. They have continued to occure since then. This study was performed to describe the long-term trend in pathogenic E. coli food poisoning occurrences in Korea and examine the relation with climate factors. Methods: Official Korean statistics on food poisoning outbreaks and meteorological data for the period 2002-2017 were used. Pearson's correlation analysis was employed to establish the relationship between outbreaks of pathogenic E. coli food poisoning and meteorological factors. The influence of meteorological factors upon the outbreaks was analyzed by regression analysis. Results: During the study period, pathogenic E. coli food poisoning ranked second for the number of outbreaks (excluding unknowns) and first for the number of cases. Average temperature, the highest and lowest temperatures, precipitation, number of days with rainfall, and humidity all had a significant correlation with monthly number of outbreaks of pathogenic E. coli food poisoning (p<0.001). It was found that the lowest and highest temperatures and precipitation had a significant influence on the monthly number of outbreaks of food poisoning (p<0.001). These variables together explained 42.1% of the total variance, with the lowest temperature having the greatest explanatory power. Conclusion: These results show that food poisoning incidences may have been influenced by climate change, especially warming. The results also suggest that pathogenic E. coli infections are now an important public health issue in Korea since it is one of the countries where climate change is occurring rapidly.

Disinfection of E. coli Using Electro-UV Complex Process: Disinfection Characteristics and Optimization by the Design of Experiment Based on the Box-Behnken Technique (전기-UV 복합 공정을 이용한 E. coli 소독 : 실험계획법중 박스-벤켄법을 이용한 소독 특성 및 최적화)

  • Kim, Dong-Seog;Park, Young-Seek
    • Journal of Environmental Science International
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    • v.19 no.7
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    • pp.889-900
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    • 2010
  • The experimental design and response surface methodology (RSM) have been applied to the investigation of the electro-UV complex process for the disinfection of E. coli in the water. The disinfection reactions of electro-UV process were mathematically described as a function of parameters power ($X_1$), NaCl dosage ($X_2$), initial pH ($X_3$) and disinfection time ($X_4$) being modeled by use of the Box-Behnken technique. The application of RSM using the Box-Behnken technique yielded the following regression equation, which is an empirical relationship between the residual E. coli number and test variables in actual variables: Ln (CFU) = 23.57 - 0.87 power - 1.87 NaCl dosage - 2.13 pH - 2.84 time - 0.09 power time - 0.07 NaCl dosage pH + 0.14 pH time + 0.03 $power^2$ + 0.47 NaCl $dosage^2$ + 0.20 $pH^2$+ 0.33 $time^2$. The model predictions agreed well with the experimentally observed result ($R^2$ = 0.9987). Graphical response surface and contour plots were used to locate the optimum point. The estimated ridge of maximum response and optimal conditions for the E. coli disinfection using canonical analysis was Ln 1.06 CFU (power, 15.40 W; NaCl dosage, 1.95 g/L, pH, 5.94 and time, 4.67 min). To confirm this optimum condition, the obtained number of the residual E. coli after three additional experiments were Ln 1.05, 1.10 and Ln 1.12. These values were within range of 0.62 (95% PI low)~1.50 (95% PI high), which indicated that conforming the reproducibility of the model.

Investigation of Microbial Safety and Correlations Between the Level of Sanitary Indicator Bacteria and the Detection Ratio of Pathogens in Agricultural Water (농업용수의 미생물학적 안전성 조사 및 위생지표세균 농도와 병원성미생물 검출률과의 상관관계 분석)

  • Hwang, Injun;Lee, Tae Kwon;Park, Daesoo;Kim, Eunsun;Choi, Song-Yi;Hyun, Jeong-Eun;Rajalingam, Nagendran;Kim, Se-Ri;Cho, Min
    • Korean Journal of Environmental Agriculture
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    • v.40 no.4
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    • pp.248-259
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    • 2021
  • BACKGROUND: Contaminated water was a major source of food-borne pathogens in various recent fresh produce-related outbreaks. This study was conducted to investigate the microbial contamination level and correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water by logistic regression analysis. METHODS AND RESULTS: Agricultural water was collected from 457 sites including surface water (n=300 sites) and groundwater (n=157 sites) in South Korea from 2018 to 2020. Sanitary indicator bacteria (total coliform, fecal coliform, and Escherichia coli) and food-borne pathogens (pathogenic E. coli, E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were analyzed. In surface water, the coliform, fecal coliform, and E. coli were 3.27±0.89 log CFU/100 mL, 1.90±1.19 log CFU/100 mL, and 1.39±1.26 log CFU/100 mL, respectively. For groundwater, three kinds of sanitary indicators ranged in the level from 0.09 - 0.57 log CFU/100 mL. Pathogenic E. coli, Salmonella and Listeria monocytogenes were detected from 3%-site, 1.5%- site, and 0.6%-site water samples, respectively. According to the results of correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens by logistic regression analysis, the probability of pathogen detection increased individually by 1.45 and 1.34 times as each total coliform and E. coli concentration increased by 1 log CFU/100mL. The accuracy of the model was 70.4%, and sensitivity and specificity were 81.5% and 51.7%, respectively. CONCLUSION(S): The results indicate the need to manage the microbial risk of agricultural water to enhance the safety of fresh produce. In addition, logistic regression analysis is useful to analyze the correlation between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water.

The Use of the Strain Containing Multiple Plasmids as Size Reference Plasmids (분자량측정을 위한 Reference Plasmid 보유균주의 이용)

  • Bang, Sung-Hyuk;Lee, Yoo-Chul;Seol, Sung-Yong;Cho, Dong-Taek
    • The Journal of the Korean Society for Microbiology
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    • v.22 no.3
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    • pp.267-273
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    • 1987
  • In the analysis of plasmid profile obtained by agarose gel electrophoresis, the strain harvoring multiple reference plasmids of known molecular weight were needed to estimate the size of unknown molecular weight plasmids. Six strains of E. coli isolated from clinical specimens carried multiple plasmids and these strains could be available as a reference plasmids harvoring strain. These E. coli strains showed 4 to 9 plasmids of various size ranging 2.5 to 94.3 megadalton (Mdal). The correlation coefficients of linear regression between the relative mobility and molecular weight were 0.99966 to 1.00000. Among them, E. coli KE327 from throat which contained 7 plasm ids as follows: 79 Mdal, 46 Mdal, 33 Mdal(pKY3027 C), 4.9Mdal, 3.8Mdal(pKY3027 E), 3.5Mdal, and 2.7 Mdal. Relative amount of the pKY3027 C was the smallest(7.09%) and that of the pKY3027 E was the largest (24.24%) among the plasmid fractions of E. coli KE327.

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Analysis of prevalence and risk factors of diarrhea in dairy calf using a rapid diagnostic kit in Gyeonggi province (신속진단키트를 활용한 경기지역 젖소 송아지 설사병 유병률과 위험요인 분석)

  • Taemook Park;Gil Jae Cho;Young Jin Yang;Il-Sun Ryu
    • Korean Journal of Veterinary Service
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    • v.46 no.2
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    • pp.147-156
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    • 2023
  • Between February 2020 and September 2021, a total of 452 dairy calves with diarrhea were investigated across 17 dairy farms in Gyeonggi province, Korea, using a rapid diagnostic kit. The study aimed to examine the infection rates of major pathogens causing diarrhea in dairy calves, categorizing them by season, age, and birth month. Additionally, logistic regression analysis was conducted to investigate the factors affecting the infection rate. The infection rates of the major pathogens causing infectious diarrhea in dairy calves, including bovine rotavirus, bovine coronavirus, Cryptosporidium, and E. coli, are influenced by season, age, and birth month. Bovine coronavirus and Cryptosporidium showed variations in infection rates according to season, age, and birth month, while bovine coronavirus was influenced by age and birth month, and E. coli showed variations in infection rates based on age. Furthermore, in the analysis of risk factors influencing the infection rates of these pathogens, age and birth month were identified as risk factors for bovine rotavirus, bovine coronavirus, and Cryptosporidium.

Chemometric Analysis of 2D Fluorescence Spectra for Monitoring and Modeling of Fermentation Processes (생물공정 모니터링 및 모델링을 위한 2차원 형광스펙트럼의 다변량 분석)

  • Kang Tae-Hyoung;Sohn Ok-Jae;Kim Chun-Kwang;Chung Sang-Wook;Rhee Jong-Il
    • KSBB Journal
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    • v.21 no.1 s.96
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    • pp.59-67
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    • 2006
  • 2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

Optimization of coagulant dosage using response surface methodology with central composite design (반응표면분석법-중심합성계획을 이용한 최적 응집제 주입량 산정 연구)

  • Kim, Yeseul;Oh, Jeill
    • Journal of Korean Society of Water and Wastewater
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    • v.29 no.2
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    • pp.193-202
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    • 2015
  • The determining the appropriate dosage of coagulant is very important, because dosage of coagulant in the coagulation process for wastewater affects removing the amount of pollutants, cost, and producing sludge amount. Accordingly, in this study, in order to determine the optimal PAC dosage in the coagulation process, CCD (Central composite design) was used to proceed experimental design, and the quadratic regression models were constructed between independent variables (pH, influent turbidity, PAC dosage) and each response variable (Total coliform, E.coli, PSD (Particle size distribution) (< $10{\mu}m$), TP, $PO_4$-P, and $COD_{cr}$) by the RSM (Response surface methodology). Also, Considering the various response variables, the optimum PAC dosage and range were derived. As a result, in order to maximize the removal rate of total coliform and E.coli, the values of independent variables are the pH 6-7, the influent turbidity 100-200 NTU, and the PAC dosage 0.07-0.09 ml/L. For maximizing the removal rate of TP, $PO_4$-P, $COD_{cr}$, and PSD(< $10{\mu}m$), it is required for the pH 9, the influent turbidity 200-250 NTU, and the PAC dosage 0.05-0.065 ml/L. In the case of multiple independent variables, when the desirable removal rate for total coliform, E.coli, TP, and $PO_4$-P is 90-100 % and that for $COD_{cr}$ and PSD(< $10{\mu}m$) is 50-100 %, the required PAC dosage is 0.05-0.07 ml/L in the pH 9 and influent turbidity 200-250 NTU. Thus, if the influent turbidity is high, adjusting pH is more effective way in terms of cost since a small amount of PAC dosage is required.

Evaluation of Dry Rehydratable Film Method for Enumeration of Microorganisms in Korean Traditional Foods (한국 전통식품 중 미생물 분석을 위한 건조필름법 평가)

  • Kim Kwan-Sik;Bae Eun-Kyung;Ha Sang Do;Park Young Seo;Mok Chul Kyoon;Hong Kwan Pyo;Kim Sang Phil;Park Jiyong
    • Journal of Food Hygiene and Safety
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    • v.19 no.4
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    • pp.209-216
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    • 2004
  • Dry rehydratable film methods were compared to conventional methods for the enumeration of microorganisms in Korean traditional foods. Kimchi, doenjang, kochujang, kanjang, takju, sujeongkwa and sikhe were used as Korean traditional foods. $Petrifilm^{TM}$ aerobic count plate, $Petrifilm^{TM}$ coliform count plate, $Petrifilm^{TM}$ E. coli/coliform count plate, $Petrifilm^{TM}$ yeast and mold count plate and $Petrifilm^{TM}$ staph express count plate were compared to plate count agar, most probable number (MPN) for coliform, MPN for E. coli, potato dextrose agar and coagulase test, respectively. Regression analysis indicated that correlation coefficient values were 0.974-0.998, 0.913-0.995, 0.955-0.978, 0.968-0.986 and 0.998-0.999 for total aerobic bacteria, yeast and mold, coliform, E. coli and S. aureus, respectively. There were no significant differences between two methods, suggesting that $Petrifilm^{TM}$ plates can be used as an alternative to conventional method for the determination of microorganisms in Korean traditional foods.

A Study on Seasonal Pollutant Distribution Characteristics of Contaminated Tributaries in Nakdong River Basin (낙동강 중점관리지류·지천의 계절적 오염발생특성 분석)

  • Na, Seungmin;Kwon, Heongak;Shin, Sang Min;Son, YoungGyu;Shin, Dongseok;Im, Tae Hyo
    • Journal of Wetlands Research
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    • v.18 no.3
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    • pp.301-312
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    • 2016
  • This study has performed comparative analysis on characteristics of contaminated 35 tributaries on seasonal variation/point discharge load/pollutant distribution of water quality factors(8) in order to understand the effect of the watershed in Nakdong River Basin. As a results, the water quality of $BOD_5$(Biochemical Oxygen Demand), Chl-a(Chlorophyll a) and Fecal E. Coli shows II grade at tributaries of more than 50% without COD(Chemical Oxygen Demand), TP(Total Phosphate), TOC(Total Oxygen Carbon) and TN(Total Nitrogen) factors. The specific discharge(Q) were occupied about 54.4% (19 sites) as $0.05m^3/sec/km^2$ value. Among these results, the contaminant level of Dalseocheon, Hyeonjicheon, Seokkyocheon 1, Uriyeongcheon and Dasancheon was also high, which has to consider a discharged pollutant load(kg/day). The 35 major tributaries of Nakdong River were included in 7 mid-watershed, such as Nakdong Waegwan, Geumho River, Nakdong Goryung, Nakdong Changnyung, Nam River, Nakdong Milyang, Nakdong River Hagueon. Especially, the discharged pollutant load of Nam River and Geumho River also was high according to the amount of discharge such as Kachang dam, Gongsan dam and Nam river dam. Seasonal difference of the water quality factors such as $BOD_5$, TN, SS and Q was observed largely, on the other hand the TP and Chl-a was not. This is guessed due to the precipitation effect of site, biological and physicochemical degradation properties of pollutant and etc. The co-relationship between the seasonal difference and water quality factors was observed using a Pearson correlation coefficients. Besides, the Multiple Regression analysis using a Stepwise Regression method was conducted to understand the effect between seasonal difference and water quality factors/regression equations. As a result, the Multiple Regression analysis was adapted in the spring, summer and autumn without the winter, which was observed high at spring, summer and autumn in the order COD/TP, Chl-a/TOC, TOC/COD/$BOD_5$ water quality factors, respectively.