Biomedical subjects

Philippe Dantigny

Publications and source records attributed to Philippe Dantigny.

6 recordsLinked to original sources

PubMed2006

Mould germination: data treatment and modelling.

The objectives of this study were i/ to examine germination data sets over a range of environmental conditions (water activity, temperature) for eight food spoilage moulds, ii/ to compare the ability of the Gompertz equation and logistic function to fit the experimental plots, iii/ to simulate germination by assessing various distributions of the latent period for germination amongst a population of spores. Data sets (percentage germination, P (%), versus time, t) of Aspergillus carbonarius, Aspergillus ochraceus, Fusarium verticillioides, Fusarium proliferatum, Gibberella zeae, Mucor racemosus, Penicillium chrysogenum and Penicillium verrucosum were analysed. No correlation, or relationship between the mean percentage [mean (P)] and the variance [var (P)] was found. Therefore no transformation of the germination data was required. Experimental data were fitted by using the Gompertz equation P = A exp (-exp [mu(m) e/A (delta - t) + 1]) and the logistic function P = Pmax/(1 + exp (k (tau - t))). Based on the residual mean square error (RMSE), no model performed better than the other one. However, model parameters were generally determined more precisely with the logistic model than with the Gompertz one. The time course of fungal spore germination curves was simulated assuming different distributions of the latent period for germination, lag, amongst a population of spores. The growth rate of germ tubes was calculated by means of the relationship: lag x rate = k. For normal Gaussian distributions, germination curves were symmetrical with respect to the inflection point and should be modelled with the logistic function. Skewed distributions were capable of simulating an asymmetric germination curve that was fitted by the Gompertz model. Future studies should be conducted for assessing whether the distributions assumed in this paper are in accordance with the experimental distributions that are still unknown.

Colony Count, Microbial↗

PubMed2006

Standardisation of methods for assessing mould germination: a workshop report.

The first workshop on predictive mycology was held in Marseille, France, 2--4 February 2005 under the auspices of the French Microbiological society. The purpose of the workshop was to list the different techniques and definitions used by scientists for assessing mould germination and to evaluate the influence of the different techniques on the experimental results. Recommendations were made when a large consensus was obtained. In order to facilitate the study of germination, alternative methods to microscopic examination were examined.

Food Microbiology↗

PubMed2005

Basis of predictive mycology.

For over 20 years, predictive microbiology focused on food-pathogenic bacteria. Few studies concerned modelling fungal development. On one hand, most of food mycologists are not familiar with modelling techniques; on the other hand, people involved in modelling are developing tools dedicated to bacteria. Therefore, there is a tendency to extend the use of models that were developed for bacteria to moulds. However, some mould specificities should be taken into account. The use of specific models for predicting germination and growth of fungi was advocated previously []. This paper provides a short review of fungal modelling studies.

Food Microbiology↗

PubMed2005

Modelling the effect of ethanol on growth rate of food spoilage moulds.

The effect of ethanol (E) on the radial growth rate (mu) of food spoilage moulds (Aspergillus candidus, Aspergillus flavus, Aspergillus niger, Cladosporium cladosporioides, Eurotium herbariorum, Mucor circinelloides, Mucor racemosus, Paecilomyces variotii, Penicillium chrysogenum, Penicillium digitatum, Rhizopus oryzae and Trichoderma harzianum) was assessed in Potato Dextrose Agar (PDA) medium at a(w) 0.99, 25 degrees C. In order to model this effect, the Monod type equation described previously by Houtsma et al. (Houtsma, P.C., Kusters, B.J.M., de Wit, J.C., Rombouts, F.M., Zwietering, M.H., 1994. Modelling growth rates of Listeria monocytogenes as a function of lactate concentration. Int. J. Food. Microbiol. 24, 113-123.) was re-parameterised: mu = mu(opt)[K(E(max)-E)/K E(max)-2KE+E(max)E]; E(max) (%, wt/wt): ethanol concentration at which no growth occurs, K (%, wt/wt): ethanol concentration at which mu = mu(opt)/2, mu(opt) (mm day(-1)): growth rate at 0% ethanol. The model was capable of describing curves, mu vs. E, with either a concave shape (K E(max)/2) with a good accuracy (root mean square error (RMSE) < or = 0.136) with the notable exception of R. oryzae and T. harzianum. After growth rate data were square-root transformed to stabilise the variance, E(max) was estimated in the range 3% to 5% for all moulds with the exception of T. harzianum (E(max) 2.14%) and P. variotii (E(max) 6.43%). Ethanol would appear an effective additional barrier to inhibit fungal growth in food products and would represent an interesting alternative to the use of preservatives.

Aspergillus↗

PubMed2005

Long-term survival of pathogenic and sanitation indicator bacteria in experimental biowaste composts.

For economic, agricultural, and environmental reasons, composting is frequently used for organic waste recycling. One approach to limiting the potential risk from bacterial food-borne illnesses is to ensure that soil amendments and organic fertilizers are disinfected. However, more knowledge concerning the microbiological safety of composted substrates other than sludge and manure is necessary. Experimental in-vessel biowaste composts were used to study the survival of seeded Listeria monocytogenes, Salmonella enterica subsp. enterica serotype Enteritidis, and Escherichia coli. Four organic waste mixtures, containing various proportions of paper and cardboard, fruits and vegetables, and green waste, were composted in laboratory reactors with forced aeration. The physicochemical and microbiological parameters were monitored for 12 weeks during composting. The survival of bacteria over a 3-month period at 25 degrees C was assessed with samples collected after different experimental composting times. Strain survival was also monitored in mature sterilized composts. Nonsterile composts did not support pathogen growth, but survival of seeded pathogens was observed. Salmonella serovar Enteritidis survived in all composts, and longer survival (3 months) was observed in mature composts (8 and 12 weeks of composting). Mature biowaste composts may support long-term survival of Salmonella serovar Enteritidis during storage at room temperature. E. coli and L. monocytogenes survival was observed only in 4-week-old composts and never in older composts. Proper composting may prevent long-term survival of E. coli and L. monocytogenes. These results suggest that like composted sewage sludge or manure, domestic waste composts may support pathogen survival. Survival was not related to the physicochemical characteristics of the composts.

Escherichia coli↗

PubMed2005

Modeling the effect of ethanol vapor on the germination time of Penicillium chrysogenum.

The influence of ethanol vapor on germination of Penicillium chrysogenum was determined on yeast nitrogen base plus glucose agar medium at 25 degrees C. Ethanol vapors were generated by 0 to 6% (wt/wt) ethanol solutions at the bottom of hermetically closed petri dishes. The logistic equation was used to describe the data as the percentage of germination versus time and to estimate the germination time. The effect of ethanol concentration on germination time was described by a new reparameterized equation, resulting in an estimated limiting ethanol concentration of 4.3%. Up to 3% ethanol, all spores germinated, and the germination time increased with increasing ethanol concentration. At 3.5 and 4%, some spores formed abnormal germ tubes and others were inhibited at the swelling stage. The inhibiting effect of ethanol was reversible under these experimental conditions.

Dose-Response Relationship, Drug↗