Modelling the growth of pear postharvest fungal isolates at different temperatures

Modeling aflatoxin B1 production by Aspergillus flavus during wheat malting for craft beer as a function of grains steeping degree, temperature and time of germination

This study aimed to model the aflatoxin B₁ (AFB₁) production by A. flavus in wheat grains during malting for craft beer. A total of sixty-four different combinations of grains steeping degree (ST; 41, 43, 45 and 47%), temperature (13, 15, 17 and 19 °C) and time of germination (48, 72, 96 and 120 h), comprising the range of malting conditions that allow the production of quality malt, were assayed. AFB₁ was produced in a range of 15.78 ± 3.54 μg/kg (41% ST, 13 °C for 48 h) to 284.66 ± 44.34 μg/kg (47% ST, 19 °C for 120 h). The regression model showing an acceptable fit to the experimental data (adjusted R² 0.84) for AFB₁ as a function of grains steeping degree, temperature and time of germination. Results showed that AFB₁ levels in wheat malt increase with increase of the temperature or time of germination. Within the range of tested malting conditions, no significant effects were observed for steeping degree on AFB₁ levels in wheat malt. The generated model is useful to estimate the AFB₁ levels in wheat malt. Findings highlight overall that if wheat grains are contaminated with A. flavus, AFB₁ might be produced in malt in levels above the limits set by regulatory agencies, regardless the steeping conditions used.

Predictive Model for the Effect of Environmental Conditions on the Postharvest Development of Colletotrichum gloeosporioides Strains Isolated from Papaya (Carica papaya L.)

ABSTRACT

Colletotrichum species are the most important postharvest spoilage fungi of papaya fruit. The objective of this research was to evaluate the effect of temperature and relative humidity on growth rate and time for growth to become visible of five strains of Colletotrichum gloeosporioides isolated from papaya fruit in a complex medium. As a primary model, the radial growth rates were estimated using the Baranyi and Roberts model in papaya agar. The Solver MS Excel function was used to obtain the time to visible mycelium (tv). Secondary models obtained with the Rosso et al. cardinal model of inflection were applied to describe the effect of temperature on the growth rate (μ). The Arrhenius-Davey model was used to model tv. The obtained models seem to be satisfactory for describing both μ and tv. The relative humidity had an effect on μ and tv for all tested C. gloeosporioides isolates, but no model accurately described the behavior of the fungus. External validation of models was performed with papaya fruit. Growth models were developed with the same models used in vitro. The bias and the accuracy factors as indices for performance evaluation of predictive models in food microbiology as a function of temperature and RH were 1.22 and 1.33, respectively, for μ and 1.18 and 1.62, respectively, for tv, indicating accurate predictions. The supply chain of papaya is complex and requires constant conditions, and poor conditions can result in damage to the fruit. Knowledge of the behavior of C. gloeosporioides on papaya fruit and application of the developed models in the supply chain will help to establish transport control strategies to combat these fungi. This research has contributed to development of the first models of growth for C. gloeosporioides in Mexico.

HIGHLIGHTS

Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions

Baranyi model was fitted to experimental growth data of Pseudomonas spp. on the button mushrooms (Agaricus bisporus) stored at different isothermal conditions (4, 12, 20 and 28 °C), and the kinetic growth parameters of Pseudomonas spp. on the button mushrooms were obtained. The goodness of fit of the Baranyi model was evaluated by considering the root mean squared error (RMSE) and the adjusted coefficient of determination (adjusted-R²). The Baranyi model gave RMSE values lower than 0.193 and adjusted-R² values higher than 0.975 for all isothermal storage temperatures. The maximum specific growth rate (µ_max) was described as a function of temperature using secondary models namely, Ratkowsky and Arrhenius models. The Ratkowsky model described the temperature dependence of µ_max better than the Arrhenius model. Therefore, the differential form of the Baranyi model was merged with the Ratkowsky model, and solved numerically using the fourth-order Runge-Kutta method to predict the concentration of Pseudomonas spp. populations on button mushrooms under non-isothermal conditions in which they are frequently subjected to during storage, delivery and retail marketing. The validation performance of the dynamic model used was assessed by considering bias (B_f) and accuracy (A_f) factors which were found to be 0.998 and 1.016, respectively. The dynamic model developed also exhibited quite small mean deviation (MD) and mean absolute deviation (MAD) values being -0.013 and 0.126 log CFU/g, respectively. The modelling approach used in this work could be an alternative to traditional enumeration techniques to determine the number of Pseudomonas spp. on mushrooms as a function of temperature and time.