Prediction of Listeria monocytogenes ATCC 7644 growth on fresh-cut produce treated with bacteriophage and sucrose monolaurate by using artificial neural network
作者： Adebola O Oladunjoye , Stanley A Oyewole , S Singh , Oluwatosin A Ijabadeniyi , None
DOI: 10.1016/J.LWT.2016.10.042
关键词: Lysis 、 Artificial neural network 、 Bacteriophage 、 Inoculation 、 Food science 、 Listeria monocytogenes 、 Antimicrobial 、 Bacteria 、 Microbiology 、 Sigmoid function 、 Biology
摘要: Abstract Combination of bacteriophage and sucrose monolaurate (SML) against Listeria monocytogenes growth on fresh-cut produce prediction relationship among initial bacterial load, fresh-produce type, antimicrobial concentration residual bacteria using Artificial Neural Networks (ANNs) was investigated. Inoculated samples (tomato carrot) containing 108 log cfu mL−1 L. monocytogenes, treated with (108 pfu mL−1), SML (100, 250 400 ppm) chlorine control (200 ppm) were stored at 4, 10 25 °C for 6 days. Mathematical models developed a linear regression sigmoid (hyperbolic logistic) activation functions. Data sets (120) trained Back propagation ANN one hidden layer four neurons. Phage treatment tomato carrot showed (p 0.05) ineffective, but significantly (p
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