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Journal Abstract Search

389 related items for PubMed ID: 19217180

  • 1. Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.
    Ferrer J, Prats C, López D, Vives-Rego J.
    Int J Food Microbiol; 2009 Aug 31; 134(1-2):2-8. PubMed ID: 19217180
    [Abstract] [Full Text] [Related]

  • 2. The future of predictive microbiology: strategic research, innovative applications and great expectations.
    McMeekin T, Bowman J, McQuestin O, Mellefont L, Ross T, Tamplin M.
    Int J Food Microbiol; 2008 Nov 30; 128(1):2-9. PubMed ID: 18703250
    [Abstract] [Full Text] [Related]

  • 3. Predictive modelling of the microbial lag phase: a review.
    Swinnen IA, Bernaerts K, Dens EJ, Geeraerd AH, Van Impe JF.
    Int J Food Microbiol; 2004 Jul 15; 94(2):137-59. PubMed ID: 15193801
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  • 4. Current trends in predictive modelling of microbial lag phenomena.
    Swinnen IA, Bernaerts K, Dens EJ, Geeraerd AH, Van Impe JF.
    Meded Rijksuniv Gent Fak Landbouwkd Toegep Biol Wet; 2001 Jul 15; 66(3b):495-502. PubMed ID: 15954644
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  • 5. Estimation of microbial growth using population measurements subject to a detection limit.
    Shorten PR, Pleasants AB, Soboleva TK.
    Int J Food Microbiol; 2006 May 01; 108(3):369-75. PubMed ID: 16497400
    [Abstract] [Full Text] [Related]

  • 6. Cell division theory and individual-based modeling of microbial lag: part I. The theory of cell division.
    Dens EJ, Bernaerts K, Standaert AR, Van Impe JF.
    Int J Food Microbiol; 2005 Jun 15; 101(3):303-18. PubMed ID: 15925713
    [Abstract] [Full Text] [Related]

  • 7. A prototype model structure for mixed microbial populations in homogeneous food products.
    Dens EJ, Vereecken KM, Van Impe JF.
    J Theor Biol; 1999 Dec 07; 201(3):159-70. PubMed ID: 10600360
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  • 14. Predictive modeling of mixed microbial populations in food products: evaluation of two-species models.
    Vereecken KM, Dens EJ, Van Impe JF.
    J Theor Biol; 2000 Jul 07; 205(1):53-72. PubMed ID: 10860700
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  • 15. Obtaining single cells: analysis and evaluation of an experimental protocol by means of a simulation model.
    Standaert AR, Geeraerd AH, Bernaerts K, Francois K, Devlieghere F, Debevere J, Van Impe JF.
    Int J Food Microbiol; 2005 Apr 15; 100(1-3):55-66. PubMed ID: 15854692
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  • 16. Semantic annotation of Web data applied to risk in food.
    Hignette G, Buche P, Couvert O, Dibie-Barthélemy J, Doussot D, Haemmerlé O, Mettler E, Soler L.
    Int J Food Microbiol; 2008 Nov 30; 128(1):174-80. PubMed ID: 18678427
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  • 17. Modelling the combined effects of structured food model system and lactic acid on Listeria innocua and Lactococcus lactis growth in mono- and coculture.
    Antwi M, Bernaerts K, Van Impe JF, Geeraerd AH.
    Int J Food Microbiol; 2007 Nov 30; 120(1-2):71-84. PubMed ID: 17629978
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  • 18. Accounting for inherent variability of growth in microbial risk assessment.
    Marks HM, Coleman ME.
    Int J Food Microbiol; 2005 Apr 15; 100(1-3):275-87. PubMed ID: 15854712
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  • 19. Predictive model for growth of Clostridium perfringens during cooling of cooked uncured beef.
    Juneja VK, Marks H, Thippareddi H.
    Food Microbiol; 2008 Feb 15; 25(1):42-55. PubMed ID: 17993376
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  • 20. SysBioMed report: advancing systems biology for medical applications.
    Wolkenhauer O, Fell D, De Meyts P, Blüthgen N, Herzel H, Le Novère N, Höfer T, Schürrle K, van Leeuwen I.
    IET Syst Biol; 2009 May 15; 3(3):131-6. PubMed ID: 19449974
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