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The time-consuming nature of culturing methods has urged the exploration of rapid modern technologies. One promising alternative utilizes redox potential, which describes the oxidative changes within complex media, indicating oxygen and nutrient consumption, as well as the production of reduced substances in the investigated biological system. Redox potential measurement can detect microbial activity within 16 h, what is significantly faster than the minimum 24 h incubation time of the reference plate counting technique. The redox potential based method can be specific with selective media, but bacterial strains have unique kinetic pattern as well. The proposed method suggests evaluation of the curve shape for the differentiation of environmental contaminant and pathogenic microbial strains. Six bacterial species were used in validation (Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Listeria innocua, Listeria monocytogenes, and Listeria ivanovii). Descriptive parameters reached 98.2 % accuracy and Gompertz model achieved 91.6 % accuracy in classification of the selected 6 bacteria species.•Mathematical model (Gompertz function) and first order descriptive parameters are suggested to describe the specific shape of redox potential curves, while Support Vector Machine (SVM) is recommended for classification.•Due to the concentration dependent time to detection (TTD), pre-processing applies standardization according to the inflection point time.

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AIMS: The two-parameter (α and ß) Schiraldi's model reliably fits growth curves of psychrotrophic pathogens and suggests a different description of the latency phase. METHODS AND RESULTS: Data obtained at various temperatures and different starting cell densities for Aeromonas hydrophila, Listeria monocytogenes and Yersinia enterocolitica have been fitted with the Baranyi and Roberts' model and the new one. On average, the former showed higher standard error and R2 values (0.140 and 0.991) than the Schiraldi's one (0.079 and 0.983). Around 15℃, the increase of temperature showed a milder effect on the growth rate than that expected. Y. enterocolitica showed a practically null duration of the lag phase, no matter the value of the starting density, whereas A. hydrophila and L. monocytogenes revealed slower onset trends. CONCLUSIONS: Parameter ß defines the number of cell duplications and appears independent on temperature, while (ß/α)1/2 is proportional to the maximum specific growth rate. The α-1/2 versus temperature trend directly reflects the corresponding behaviour of the growth rate and does not require the use of Arrhenius plots. SIGNIFICANCE AND IMPACT OF THE STUDY: Values of the parameters α and ß, as well as the duration of the latency phase, allowed some considerations about the effect of storage temperature in terms of food safety, especially for psychrotrophic bacteria of concern.

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The development of timelapse videos for the investigation of growing microbial colonies has gained increasing interest due to its low cost and complexity implementation. In the present study, a simple experimental setup is proposed for periodic snapshot acquisition of a petri dish cultivating a fungus of the genus Candida SPP, thus creating a timelapse video. A computational algorithm, based on image processing techniques is proposed for estimating the microbial population and for extracting the experimental population curves, showing the time evolution of the population of microbes at any region of the dish. Likewise, a novel mathematical population evolution modeling approach is reported, which is based on the logistic function (LF). Parameter estimation of the aforementioned model is described and visually assessed, in comparison with the conventional and widely-used LF method. The effect of the image analysis parameterization is also highlighted. Our experiments take into account different area sizes, i.e., the number of pixels in the neighborhood, to generate population curves and calculate the model parameters. Our results reveal that, as the size of the area increases, the curve becomes smoother, the signal-to-noise-ratio increases and the estimation of model parameters becomes more accurate.

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