Gonzalo Zurera-Cosano

TL;DR: Monitoring the evolution of spoilage organisms in a mixed salad of red cabbage, lettuce and carrot stored at 4 degrees C, 10 degrees C and 15 degrees C established a theoretical shelf-life time as a function of temperature, and lactic acid bacteria at levels of 10(6) cfu/g appeared to be related to both spoilage and theoretically-predicted shelf- life values.

TL;DR: ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters and during external validation, a higher degree of accuracy and bias were observed for the ANN model compared with the RS model.

TL;DR: In this article, Artificial Neural Networks (ANN) were evaluated and compared with Response Surface Model (RSM) results using growth response data for E.coli O157:H7 as affected by five variables: pH, sodium chloride, and nitrite concentrations, temperature, and aerobic/anaerobic conditions.

TL;DR: In this paper, the combined effects of different temperatures (10.5-24.5 C), pH level (5.5 −7.5), sodium chloride levels (0.25 −6.25%) and sodium nitrite levels (200 ppm) on the predicted growth rate and lag-time of Leuconostoc mesenteroides under aerobic and anaerobic conditions was studied.

TL;DR: The results agree with previous reports that modified atmosphere does not greatly inhibit Listeria monocytogenes and reflect a lower specific growth rate in this food compared to media, and illustrates the difficulty of validating, in complex food systems, mathematic models based on culture media.