Quantitative microbial spoilage risk assessment of plant-based milk alternatives by Geobacillus stearothermophilus in Europe

A secondary model for the effect of pH on the variability in growth fitness of Listeria innocua strains

Variability in microbial growth is a keystone of modern Quantitative Microbiological Risk Assessment (QMRA). However, there are still significant knowledge gaps on how to model variability, with the most common assumption being that variability is constant. This is implemented by an error term (with constant variance) added on top of the secondary growth model (for the square root of the growth rate). However, this may go against microbial ecology principles, where differences in growth fitness among bacterial strains would be more prominent in the vicinity of the growth limits than at optimal growth conditions. This study coins the term "secondary models for variability", evaluating whether they should be considered in QMRA instead of the constant strain variability hypothesis. For this, 21 strains of Listeria innocua were used as case study, estimating their growth rate by the two-fold dilution method at pH between 5 and 10. Estimates of between-strain variability and experimental uncertainty were obtained for each pH using mixed-effects models, showing the lowest variability at optimal growth conditions, increasing towards the growth limits. Nonetheless, the experimental uncertainty also increased towards the extremes, evidencing the need to analyze both sources of variance independently. A secondary model was thus proposed, relating strain variability and pH conditions. Although the modelling approach certainly has some limitations that would need further experimental validation, it is an important step towards improving the description of variability in QMRA, being the first model of this type in the field.

Quantitative microbial spoilage risk assessment of Aspergillus niger in white bread reveal that retail storage temperature and mold contamination during factory cooling are the main factors to influence spoilage

The present study developed a model for effectively assessing the risk of spoilage caused by Aspergillus niger to identify key control measures employed in bakery supply chains. A white bread supply chain comprising a processing plant and two retail stores in Taiwan was selected in this study. Time-temperature profiles were collected at each processing step in summer and winter. Visual mycelium diameter predictions were validated using a time-lapse camera. Six what-if scenarios were proposed. The mean risk of A. niger contamination per package sold by retailer A was 0.052 in summer and 0.036 in winter, and that for retailer B was 0.037 in summer and 0.022 in winter. Sensitivity analysis revealed that retail storage time, retail temperature, and mold prevalence during factory cooling were the main influencing factors. The what-if scenarios revealed that reducing the retail environmental temperature by 1 °C in summer (from 23.97 °C to 22.97 °C) and winter (from 23.28 °C to 22.28 °C) resulted in a reduction in spoilage risk of 47.0% and 34.7%, respectively. These results indicate that food companies should establish a quantitative microbial risk assessment model that uses real data to evaluate microbial spoilage in food products that can support decision-making processes.

Levels and types of microbial contaminants in different plant-based ingredients used in dairy alternatives

In this study levels and types of microbial contaminants were investigated in 88 different plant-based ingredients including many that are used to manufacture dairy alternatives. Studied ingredients encompassed samples of pulses (pea, faba bean, chickpea, and mung bean), cereals/pseudocereals (oat, rice, amaranth and quinoa) and drupes (coconut, almond and cashew). The microbial analysis included: i) total viable count (TVC), ii) total aerobic mesophilic spore count (TMS), iii) heat resistant aerobic thermophilic spore count (HRTS), iv) anaerobic sulfite reducing Clostridium spore count (SRCS), and v) Bacillus cereus spore count (BCES). Microorganisms isolated from the counting plates with the highest sample dilutions were identified using 16S rRNA and MALDI-TOF MS analyses. Many of the investigated ingredients showed a high proportion of spores as part of their total aerobic mesophilic counts. In 63 % of the samples, the difference between TVC and TMS counts was 1 Log10 unit or less. This was particularly the case for the majority of pea isolates and concentrates, faba bean isolates, oat kernels and flakes, and for single samples of chickpea isolate, almond, amaranth, rice, quinoa, and coconut flours. Concentrations of TVC ranged between <1.0 and 5.3 Log10 CFU/g in different samples, and TMS varied between <1.0 and 4.1 Log10 CFU/g. Levels of HTRS, BCES and SRCS were generally low, typically around or below the LOD of 1.0 Log10 CFU/g. In total, 845 individual bacterial colonies were isolated belonging to 33 different genera. Bacillus licheniformis and B. cereus group strains were most frequently detected among Bacillus isolates, and these species originated primarily from pea and oat samples. Geobacillus stearothermophilus was the main species encountered as part of the HRTS. Among the Clostridium isolates, Clostridum sporogenes/tepidum were predominant species, which were mostly found in pea and almond samples. Strains with potential to cause foodborne infection or intoxication were typed using the PCR-based method for toxin genes detection. In the B. cereus group, 9 % of isolates contained the ces gene, 28 % contained hbl, 42 % cytK, and 69 % were positive for the nhe gene. Absence of the boNT-A and -B genes was confirmed for all isolated C. sporogenes/tepidum strains. Nearly all (98 %) B. licheniformis isolates were positive for the lchAA gene. Insight into the occurrence of microbial contaminants in plant-based ingredients, combined with knowledge of their key inactivation and growth characteristics, can be used for the microbial risk assessment and effective design of plant-based food processing conditions and formulations to ensure food safety and prevent spoilage.

Quantitative microbial spoilage risk assessment caused by fungi in sports drinks through multilevel modelling

The risk of fungal spoilage of sports drinks produced in the beverage industry was assessed using quantitative microbial spoilage risk assessment (QMSRA). The most relevant pathway was the contamination of the bottles during packaging by mould spores in the air. Mould spores' concentration was estimated by longitudinal sampling for 6 years (936 samples) in different production areas and seasons. This data was analysed using a multilevel model that separates the natural variability in spore concentration (as a function of sampling year, season, and area) and the uncertainty of the sampling method. Then, the expected fungal contamination per bottle was estimated by Monte Carlo simulation, considering their settling velocity and the time and exposure area. The product's shelf life was estimated through the inoculation of bottles with mould spores, following the determination of the probability of visual spoilage as a function of storage time at 20 and 30 °C using logistic regression. The Monte Carlo model estimated low expected spore contamination in the product (1.7 × 10-6 CFU/bottle). Nonetheless, the risk of spoilage is still relevant due to the large production volume and because, as observed experimentally, even a single spore has a high spoilage potential. The applicability of the QMSRA during daily production was made possible through the simplification of the model under the hypothesis that no bottle will be contaminated by more than one spore. This simplification allows the calculation of a two-dimensional performance objective that combines the spore concentration in the air and the exposure time, defining "acceptable combinations" according to an acceptable level of spoilage (ALOS; the proportion of spoiled bottles). The implementation of the model at the operational level was done through the representation of the simplified model as a two-dimensional diagram that defines acceptable and unacceptable areas. The innovative methodology employed here for defining and simplifying QMSRA models can be a blueprint for future studies aiming to quantify the risk of spoilage of other beverages with a similar scope.