Fungal growth on solid foods can make them unfit for human consumption, but certain specialty foods require fungi to produce their characteristic properties. In either case, a reliable way of measuring biomass is needed to study how various factors (e.g. water activity) affect fungal growth rates on these substrates. Biochemical markers such as chitin, glucosamine or ergosterol have been used to estimate fungal growth, but they cannot distinguish between individual species in mixed culture. In this study, a real-time polymerase chain reaction (rt-PCR) protocol specific for a target fungal species was used to quantify its DNA while growing on solid food. The measured amount of DNA was then related to the biomass present using an experimentally determined DNA-to-biomass ratio. The highly sensitive rt-PCR biomass assay was found to have a wide range, able to quantify the target DNA within a six orders-of-magnitude difference. The method was used to monitor germination and growth of Penicillium chrysogenum spores on a model porous food (cooked wheat flour) at 25°C and different water activities of 0.973, 0.936, and 0.843. No growth was observed at 0.843, but lag, exponential and stationary phases were identified in the growth curves for the higher water activities. The calculated specific growth rates (μ) during the exponential phase were almost identical, at 0.075/h and 0.076/h for aw=0.973 and 0.936, respectively. The specificity of the method was demonstrated by measuring the biomass of P. chrysogenum while growing together with Aspergillus niger on solid media at aw=0.973.

The aim of this study was to develop a model to predict growth of Listeria in complex food matrices as a function of pH, water activity and undissociated acetic and propionic acid concentration i.e. common food hurdles. Experimental growth curves of Listeria in food products and broth media were collected from ComBase, the literature and industry sources from which a bespoke secondary gamma model was constructed. Model performance was evaluated by comparing predictions to measured growth rates in growth media (BHI broth) and two adjusted food matrices (zucchini purée and béarnaise sauce). In general, observed growth rates were higher in broth than in the food matrices which resulted in the model over-estimating growth in the adjusted food matrices. In addition, model outputs were more accurate for conditions without acids, indicating that the organic acid component of the model was a source of inaccuracy. In summary, a new predictive growth model for innovating or renovating food products that rely on multi-hurdle technology was created. This study is the first to report on modelling of propionic acid as an inhibitor of Listeria in combination with other hurdles. Our findings provide valuable insights into predictive model design and performance and highlight the importance of experimental validation of models in real food matrices rather than laboratory media alone.

A cardinal model (CM) for the effects of temperature (range: 32–59 °C), pH (range: 5.0–8.5) and water activity (aw) (range: 0.980–0.995) on Bacillus coagulans DSM 1 growth rate was developed in brain heart infusion broth (BHI), using the Bioscreen C method and further validated in selected food products. The estimated values for the cardinal parameters Tmin, Topt, Tmax, pHmin, pHopt, pHmax, awmin and awopt were 23.77 ± 0.19 °C, 52.89 ± 0.01 °C, 59.37 ± 0.07 °C, 4.70 ± 0.02, 6.43 ± 0.02, 8.56 ± 0.01, 0.969 ± 0.0007 and 0.998 ± 0.0011, respectively. The growth behaviour of B. coagulans was studied in five commercial non-refrigerated ready-to-eat food products under static conditions at 53 °C in order to estimate the optimum specific growth rate for each tested food product. The developed models were validated in the five selected food products under four different dynamic temperature profiles by comparing predicted and observed growth behaviour of B. coagulans. The validation results indicated a good performance of the model for all tested products with the overall Bias factor (Bf) and Accuracy factor (Af) estimated at 1.00 and 1.12, respectively. The developed model can be considered an effective tool in predicting B. coagulans growth and spoilage risks of non-refrigerated ready-to-eat food products during distribution and storage.