• Title/Summary/Keyword: Leaf Lettuce

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Microbiological Evaluation of Raw Vegetables (비가열 섭취 채소류의 미생물 오염도 조사)

  • Jung, Seung-Hye;Hur, Myung-Je;Ju, Jeong-Hwa;Kim, Kyung-Ae;Oh, Sung-Suck;Go, Jong-Myoung;Kim, Yong-Hee;Im, Jeong-Soo
    • Journal of Food Hygiene and Safety
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    • v.21 no.4
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    • pp.250-257
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    • 2006
  • The purpose of this study is to evaluate microbiological contamination of leafy vegetables. Total aerobic bacteria and coliforms were monitored to get the contamination levels and Staphylococcus aureus, Bacillus cereus, Clostridium perfringens, Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Vibrio parahaemolyticus, Listeria monocytogenes, Yersinia enterocolitica, Campylobacter jejuni to detect pathogens with risk of foodpoisoning from fresh vegetables. The colony count of total aerobes and coliforms was also performed to determine the efficacy of washing with tab water by common consumers. 124 samples which are divided into 8 kinds of vegetables - Sesame leaf, Dropwort, Chinese cabbage, Korean leek, Lettuce, Crown daisy, Pimpinella brachycarpa, Chicory were sampled in 2 wholesale markets in Incheon. Mean counts of total aerobic bacteria for individual vegetables ranged from $2.2\times10^6\;CFU/g\;to\;6.0\times10^7\;CFU/g$ and total coliforms were from $4.1\times10^5\;CFU/g\;to\;9.8\times10^6\;CFU/g$. Both show the peaks in summer on this study from March to September. Decrease rates after washing with tab water averaged 81.0% and 82.5% in total aerobic bacteria and coliform counts respectively. Staphylococcus aureus was isolated 8.1%, Bacillus cereus 14.5%, Clostridium perfringens 5.6%, Escherichia coli 18.5%. 11 samples showed overlapped bacterial contamination. For respective vegetables Staphylococcus aureus isolated from 0.0% to 22.2%, Bacillus cereus from 0.0% to 29.4%, Clostridium perfringens from 0.0% to 23.1 %, Escherichia. coli from 0.0% to 35.0%. Escherichia coli O157:H7, Salmonella spp., Vibrio parahaemolyticus, Listeria monocytogenes, Yersinia enterocolitica, Campylobacter jejuni were not isolated. This study is expected to be available as the reference for the basal data of pathogens in fresh vegetables.

Design of Cloud-Based Data Analysis System for Culture Medium Management in Smart Greenhouses (스마트온실 배양액 관리를 위한 클라우드 기반 데이터 분석시스템 설계)

  • Heo, Jeong-Wook;Park, Kyeong-Hun;Lee, Jae-Su;Hong, Seung-Gil;Lee, Gong-In;Baek, Jeong-Hyun
    • Korean Journal of Environmental Agriculture
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    • v.37 no.4
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    • pp.251-259
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    • 2018
  • BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.