Modeling the Soluble Solids and Storage Temperature Effects on Byssochlamys fulva Growth in Apple Juices

Characterisation of the surface growth of Mucor circinelloides in cheese agar media using predictive mathematical models

The main objective of this work was to characterise the mycelial growth of Mucor circinelloides, one of the fungal contaminants that appear frequently in the artisan cheese production environment. The study uses primary Baranyi and Huang models to compare their parameters and predict M. circinelloides on cheese-based medium (CBA) under diverse environmental conditions (temperature range from 6 to 37 °C and 0 and 1 % NaCl concentration). However, the Baranyi model consistently estimated longer lag phases and higher surface growth rates (sgr) than the Huang model; both models showed adequate best-fit performance (exactly with the mean coefficient of determination R2 = (0.993 ± 0.020 × 10-1). The groups of primary growth parameters were analysed against temperature using the cardinal model (CM) with the following main outputs. The optimal surface growth rates (sgropt) on CBA were 6.8 and 6.5 mm/d calculated with the Baranyi and Huang models, respectively. They were reduced by approximately 46 % on the surface of the agar medium when 1 % NaCl was added. Topt was estimated in a very narrow range of 32.1-32.5 °C from both primary sgr data sets (0 % and 1 % NaCl). Similarly, Tmax values of 37.2 °C and 37.3 °C were estimated for the Baranyi and Huang models, respectively; however, they decreased at 2 °C in CBA with 1 % NaCl (Tmax = 35.1 °C). The application of CM for sgr provided an estimation of the parameter Tmin with negative values that are considered only as a theoretical output. The results provide insight into the modelling and prediction of fungi growth as a function of time and salt concentration, including the times to detect visible mycelial growth of Mucor circinelloides. The mere quantification of this phenomenon can be useful for practice. Adjusting the frequency of the cheese surface washing step with a salt solution at the early stage of ripening properly can prevent the growth of not only fast fungal growers.

Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

Postharvest preservation and storage have a crucial impact on the technological quality and safety of grain. The important threat to stored grain quality and nutritional safety of cereal products is mould development and their toxic metabolites, mycotoxins. Models based on predictive microbiology, which are able to estimate the kinetics of fungal growth, and thus, the risks of mycotoxin accumulation in a mass of grain are promising prognostic tools that can be applied in postharvest management systems. The study developed a modelling approach to describe total fungal growth in barley ecosystems stored at different temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). As the pattern of fungal growth curves was sigmoidal, the experimental data were modelled using the modified Gompertz equation, in which constant coefficients reflecting biological parameters of mould development (i.e. lag phase duration (τlag), maximum growth rate (μmax) and the maximum increase in fungal population level (Δmaxlog(CFU)) were expressed as functions of storage conditions, i.e. aw and T. The criteria used to evaluate the overall model performance indicated its good precision (R2 = 0.95; RMSE = 0.23) and high prediction accuracy (bias factor and accuracy factor Bf = 1.004, Af = 1.035). The formulated model is able to estimate the extension of fungal contamination in a bulk of grain versus time by monitoring temperature and intergranular relative humidity that are readily measurable in practice parameters; therefore, it may be used as a prognostic support tool in modern postharvest management systems.

Using extended Bigelow meta-regressions for modelling the effects of temperature, pH, °Brix on the inactivation of heat resistant moulds

The management of Heat Resistant Moulds (HRMs) is considered a great challenge for the juice fruit industry. Neosartorya, Byssochlamys and Talaromyces are three out of the main genera isolated from fruit juices that show great resistance to heat treatments. Several inactivation parameters can be found in the literature, however all of them were carried out in specific food matrices and using diverse inactivation methods. Thus, this meta-analysis study synthesizes the thermal resistance parameters of the three HRMs by adjusting extended Bigelow-based meta-regression models to data on inactivation experiments conducted in different liquid media. The meta-analytical data, extracted from publications between 1969 and 2017, was composed of decimal reduction time (D), inactivation method, temperature of inactivation, pH, °Brix, age of spores, and type of medium (model, juice, concentrates). Pooled D* values (D at 90 °C, pH 3.5 and 12° Brix) were estimated for B. fulva (1.95 min; 95% CI: 1.21-3.11 min), Talaromyces (4.03 min; 95% CI: 3.43-4.74 min), Neosartorya (0.5.35 min; 95% CI: 4.10-7.08 min), and B. nivea (10.32 min; 95% CI: 5.81-18.4 min). It was found that increasing the soluble solids in concentrates tends to cause a lower decrease in the heat resistance of Neosartorya and Talaromyces than increasing the soluble solids in model liquid or juices (p = 0.001; 0.012). In general, the screw-capped tubes and three neck round inactivation methods render higher D* values (p < 0.05) than the thermal death tubes, the polyethylene bag and the capillary methods. Spores of Talaromyces (overall z_pH = 7.56; 95% CI: 5.13-13.5) and Neosartorya (overall z_pH = 7.07; 95% CI: 5.04-10.8) appear to be more thermal sensitive to a decrease in medium pH than spores of Byssochlamys (overall z_pH = 4.34; 95% CI: 3.20-6.73). The meta-regression models presented in this study can be valuable for estimating pooled inactivation kinetic parameters to be used by the fruit juice industry in the management of thermal processes and in the determination of shelf-life.