Added value of experts' knowledge to improve a quantitative microbial exposure assessment model--Application to aseptic-UHT food products.

In a previous study, a quantitative microbial exposure assessment (QMEA) model applied to an aseptic-UHT food process was developed [Pujol, L., Albert, I., Magras, C., Johnson, N. B., Membré, J. M. Probabilistic exposure assessment model to estimate aseptic UHT product failure rate. 2015 International Journal of Food Microbiology. 192, 124-141]. It quantified Sterility Failure Rate (SFR) associated with Bacillus cereus and Geobacillus stearothermophilus per process module (nine modules in total from raw material reception to end-product storage). Previously, the probabilistic model inputs were set by experts (using knowledge and in-house data). However, only the variability dimension was taken into account. The model was then improved using expert elicitation knowledge in two ways. First, the model was refined by adding the uncertainty dimension to the probabilistic inputs, enabling to set a second order Monte Carlo analysis. The eight following inputs, and their impact on SFR, are presented in detail in this present study: D-value for each bacteria of interest (B. cereus and G. stearothermophilus) associated with the inactivation model for the UHT treatment step, i.e., two inputs; log reduction (decimal reduction) number associated with the inactivation model for the packaging sterilization step for each bacterium and each part of the packaging (product container and sealing component), i.e., four inputs; and bacterial spore air load of the aseptic tank and the filler cabinet rooms, i.e., two inputs. Second, the model was improved by leveraging expert knowledge to develop further the existing model. The proportion of bacteria in the product which settled on surface of pipes (between the UHT treatment and the aseptic tank on one hand, and between the aseptic tank and the filler cabinet on the other hand) leading to a possible biofilm formation for each bacterium, was better characterized. It was modeled as a function of the hygienic design level of the aseptic-UHT line: the experts provided the model structure and most of the model parameters values. Mean of SFR was estimated to 10×10(-8) (95% Confidence Interval=[0×10(-8); 350×10(-8)]) and 570×10(-8) (95% CI=[380×10(-8); 820×10(-8)]) for B. cereus and G. stearothermophilus, respectively. These estimations were more accurate (since the confidence interval was provided) than those given by the model with only variability (for which the estimates were 15×10(-8) and 580×10(-8) for B. cereus and G. stearothermophilus, respectively). The updated model outputs were also compared with those obtained when inputs were described by a generic distribution, without specific information related to the case-study. Results showed that using a generic distribution can lead to unrealistic estimations (e.g., 3,181,000 product units contaminated by G. stearothermophilus among 10(8) product units produced) and emphasized the added value of eliciting information from experts from the relevant specialist field knowledge.

Estimation and evaluation of management options to control and/or reduce the risk of not complying with commercial sterility.

In a previous study, a modular process risk model, from the raw material reception to the final product storage, was built to estimate the risk of a UHT-aseptic line of not complying with commercial sterility (Pujol et al., 2015). This present study was focused on demonstrating how the model (updated version with uncertainty and variability separated and 2(nd) order Monte Carlo procedure run) could be used to assess quantitatively the influence of management options. This assessment was done in three steps: pinpoint which process step had the highest influence on the risk, identify which management option(s) could be the most effective to control and/or reduce the risk, and finally evaluate quantitatively the influence of changing process setting(s) on the risk. For Bacillus cereus, it was identified that during post-process storage in an aseptic tank, there was potentially an air re-contamination due to filter efficiency loss (efficiency loss due to successive in-place sterilizations after cleaning operations), followed by B. cereus growth. Two options were then evaluated: i) reducing by one fifth of the number of filter sterilizations before renewing the filters, ii) designing new UHT-aseptic lines without an aseptic tank, i.e. without a storage period after the thermal process and before filling. Considering the uncertainty in the model, it was not possible to confirm whether these options had a significant influence on the risk associated with B. cereus. On the other hand, for Geobacillus stearothermophilus, combinations of heat-treatment time and temperature enabling the control or reduction in risk by a factor of ca. 100 were determined; for ease of operational implementation, they were presented graphically in the form of iso-risk curves. For instance, it was established that a heat treatment of 138°C for 31s (instead of 138°C for 25s) enabled a reduction in risk to 18×10(-8) (95% CI=[10; 34]×10(-8)), instead of 578×10(-8) (95% CI=[429; 754]×10(-8)) initially. In conclusion, a modular risk model, as the one exemplified here with a UHT-aseptic line, is a valuable tool in process design and operation, bringing definitive quantitative elements into the decision making process.

Probabilistic exposure assessment model to estimate aseptic-UHT product failure rate.

Aseptic-Ultra-High-Temperature (UHT) products are manufactured to be free of microorganisms capable of growing in the food at normal non-refrigerated conditions at which the food is likely to be held during manufacture, distribution and storage. Two important phases within the process are widely recognised as critical in controlling microbial contamination: the sterilisation steps and the following aseptic steps. Of the microbial hazards, the pathogen spore formers Clostridium botulinum and Bacillus cereus are deemed the most pertinent to be controlled. In addition, due to a relatively high thermal resistance, Geobacillus stearothermophilus spores are considered a concern for spoilage of low acid aseptic-UHT products. A probabilistic exposure assessment model has been developed in order to assess the aseptic-UHT product failure rate associated with these three bacteria. It was a Modular Process Risk Model, based on nine modules. They described: i) the microbial contamination introduced by the raw materials, either from the product (i.e. milk, cocoa and dextrose powders and water) or the packaging (i.e. bottle and sealing component), ii) the sterilisation processes, of either the product or the packaging material, iii) the possible recontamination during subsequent processing of both product and packaging. The Sterility Failure Rate (SFR) was defined as the sum of bottles contaminated for each batch, divided by the total number of bottles produced per process line run (10(6) batches simulated per process line). The SFR associated with the three bacteria was estimated at the last step of the process (i.e. after Module 9) but also after each module, allowing for the identification of modules, and responsible contamination pathways, with higher or lower intermediate SFR. The model contained 42 controlled settings associated with factory environment, process line or product formulation, and more than 55 probabilistic inputs corresponding to inputs with variability conditional to a mean uncertainty. It was developed in @Risk and run through Monte Carlo simulations. Overall, the highest SFR was associated with G. stearothermophilus (380000 bottles contaminated in 10(11) bottles produced) and the lowest to C. botulinum (3 bottles contaminated in 10(11) bottles produced). Unsurprisingly, SFR due to G. stearothermophilus was due to its ability to survive the UHT treatment. More interestingly, it was identified that SFR due to B. cereus (17000 bottles contaminated in 10(11) bottles produced) was due to an airborne recontamination of the aseptic tank (49%) and a post-sterilisation packaging contamination (33%). A deeper analysis (sensitivity and scenario analyses) was done to investigate how the SFR due to B. cereus could be reduced by changing the process settings related to potential air recontamination source.

Potential application of quantitative microbiological risk assessment techniques to an aseptic-UHT process in the food industry.

Aseptic ultra-high-temperature (UHT)-type processed food products (e.g., milk or soup) are ready to eat products which are consumed extensively globally due to a combination of their comparative high quality and long shelf life, with no cold chain or other preservation requirements. Due to the inherent microbial vulnerability of aseptic-UHT product formulations, the safety and stability-related performance objectives (POs) required at the end of the manufacturing process are the most demanding found in the food industry. The key determinants to achieving sterility, and which also differentiates aseptic-UHT from in-pack sterilised products, are the challenges associated with the processes of aseptic filling and sealing. This is a complex process that has traditionally been run using deterministic or empirical process settings. Quantifying the risk of microbial contamination and recontamination along the aseptic-UHT process, using the scientifically based process quantitative microbial risk assessment (QMRA), offers the possibility to improve on the currently tolerable sterility failure rate (i.e., 1 defect per 10,000 units). In addition, benefits of applying QMRA are (i) to implement process settings in a transparent and scientific manner; (ii) to develop a uniform common structure whatever the production line, leading to a harmonisation of these process settings, and; (iii) to bring elements of a cost-benefit analysis of the management measures. The objective of this article is to explore how QMRA techniques and risk management metrics may be applied to aseptic-UHT-type processed food products. In particular, the aseptic-UHT process should benefit from a number of novel mathematical and statistical concepts that have been developed in the field of QMRA. Probabilistic techniques such as Monte Carlo simulation, Bayesian inference and sensitivity analysis, should help in assessing the compliance with safety and stability-related POs set at the end of the manufacturing process. The understanding of aseptic-UHT process contamination will be extended beyond the current "as-low-as-reasonably-achievable" targets to a risk-based framework, through which current sterility performance and future process designs can be optimised.

Establishing equivalence for microbial-growth-inhibitory effects ("iso-hurdle rules") by analyzing disparate listeria monocytogenes data with a gamma-type predictive model.

Preservative factors act as hurdles against microorganisms by inhibiting their growth; these are essential control measures for particular food-borne pathogens. Different combinations of hurdles can be quantified and compared to each other in terms of their inhibitory effect ("iso-hurdle"). We present here a methodology for establishing microbial iso-hurdle rules in three steps: (i) developing a predictive model based on existing but disparate data sets, (ii) building an experimental design focused on the iso-hurdles using the model output, and (iii) validating the model and the iso-hurdle rules with new data. The methodology is illustrated with Listeria monocytogenes. Existing data from industry, a public database, and the literature were collected and analyzed, after which a total of 650 growth rates were retained. A gamma-type model was developed for the factors temperature, pH, a(w), and acetic, lactic, and sorbic acids. Three iso-hurdle rules were assessed (40 logcount curves generated): salt replacement by addition of organic acids, sorbic acid replacement by addition of acetic and lactic acid, and sorbic acid replacement by addition of lactic/acetic acid and salt. For the three rules, the growth rates were equivalent in the whole experimental domain (γ from 0.1 to 0.5). The lag times were also equivalent in the case of mild inhibitory conditions (γ ≥ 0.2), while they were longer in the presence of salt than acids under stress conditions (γ < 0.2). This methodology allows an assessment of the equivalence of inhibitory effects without intensive data generation; it could be applied to develop milder formulations which guarantee microbial safety and stability.