Farm to Fork Quantitative Risk Assessment of Listeria monocytogenes Contamination in Raw and Pasteurized Milk Cheese in Ireland

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in dairy products was undertaken in order to identify and appraise the relative effectiveness of control measures and intervention strategies implemented at primary production, processing, retail, and consumer practices. A systematic literature search retrieved 18 QRA models, most of them (9) investigated raw and pasteurized milk cheeses, with the majority covering long supply chains (4 farm-to-table and 3 processing-to-table scopes). On-farm contamination sources, either from shedding animals or from the broad environment, have been demonstrated by different QRA models to impact the risk of listeriosis, in particular for raw milk cheeses. Through scenarios and sensitivity analysis, QRA models demonstrated the importance of the modeled growth rate and lag phase duration and showed that the risk contribution of consumers' practices is greater than in retail conditions. Storage temperature was proven to be more determinant of the final risk than storage time. Despite the pathogen's known ability to reside in damp spots or niches, re-contamination and/or cross-contamination were modeled in only two QRA studies. Future QRA models in dairy products should entail the full farm-to-table scope, should represent cross-contamination and the use of novel technologies, and should estimate L. monocytogenes growth more accurately by means of better-informed kinetic parameters and realistic time-temperature trajectories.

Evaluating the technological properties of lactic acid bacteria in Wagyu cattle milk

This paper reveals the technological properties of lactic acid bacteria isolated from raw milk (colostrum and mature milk) of Wagyu cattle raised in Okayama Prefecture, Japan. Isolates were identified based on their physiological and biochemical characteristics as well as 16S rDNA sequence analysis. Streptococcus lutetiensis and Lactobacillus plantarum showed high acid and diacetyl-acetoin production in milk after 24 h of incubation at 40 and 30°C, respectively. These strains are thought to have potential for use as starter cultures and adjunct cultures for fermented dairy products.

From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a_w, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Overview of the Potential Impacts of Climate Change on the Microbial Safety of the Dairy Industry

Climate change is expected to affect many different sectors across the food supply chain. The current review paper presents an overview of the effects of climate change on the microbial safety of the dairy supply chain and suggest potential mitigation strategies to limit the impact. Raw milk, the common raw material of dairy products, is vulnerable to climate change, influenced by changes in average temperature and amount of precipitation. This would induce changes in the microbial profile and heat stress in lactating cows, increasing susceptibility to microbial infection and higher levels of microbial contamination. Moreover, climate change affects the entire dairy supply chain and necessitates adaptation of all the current food safety management programs. In particular, the review of current prerequisite programs might be needed as well as revisiting the current microbial specifications of the receiving dairy products and the introduction of new pretreatments with stringent processing regimes. The effects on microbial changes during distribution and consumer handling also would need to be quantified through the use of predictive models. The development of Quantitative Microbial Risk Assessment (QMRA) models, considering the whole farm-to-fork chain to evaluate risk mitigation strategies, will be a key step to prioritize actions towards a climate change-resilient dairy industry.

The effect of lauric arginate on the thermal inactivation of starved Listeria monocytogenes in sous-vide cooked ground beef

The aim of this study was to examine the efficacy of lauric arginate (LAE, 1000 ppm - 3000 ppm) as an assisting tool to reduce starved Listeria monocytogenes population in ground beef following sous-vide processing at different temperatures (55-62.5 °C). Ground beef mixed with LAE was vacuum sealed and a laboratory water bath was used for sous-vide cooking. Loglinear and Weibull models were fit to the survival microbial population and the D and Z-values were determined at 55-62.5 °C. Calculated D-values ranged from 33.62 to 3.22 min at temperature 55-62.5 °C. LAE at higher concentration is an effective antimicrobial to increase the inactivation of the pathogen in sous-vide cooking. With the addition of LAE, D-values at 55 and 62.5 °C determined by the Loglinear model decreased from 31.86 to 2.28 min (LAE 1000 ppm) and 16.71 to 0.56 min (LAE 3000 ppm), respectively; whereas the D-values at 55 to 62.5 °C determined by the Weibull model were 44.26 and 2.09 min (LAE 1000 ppm) and 22.71 and 1.60 min (LAE 3000 ppm), respectively. This study shows that sous-vide processing of ground beef supplemented with higher concentration of LAE effectively inactivates L. monocytogenes and thus, helps increase the microbiological safety and product quality.

An Assessment of Listeriosis Risk Associated with a Contaminated Production Lot of Frozen Vegetables Consumed under Alternative Consumer Handling Scenarios

Frozen foods do not support the growth of Listeria monocytogenes (LM) and should be handled appropriately for safety. However, consumer trends regarding preparation of some frozen foods may contribute to the risk of foodborne listeriosis, specifically when cooking instructions are not followed and frozen products are instead added directly to smoothies or salads. A quantitative microbial risk assessment model FFLLoRA (Frozen Food Listeria Lot Risk Assessment) was developed to assess the lot-level listeriosis risk due to LM contamination in frozen vegetables consumed as a ready-to-eat food. The model was designed to estimate listeriosis risk per serving and the number of illnesses per production lot of frozen vegetables contaminated with LM, considering individual facility factors such as lot size, prevalence of LM contamination, and consumer handling prior to consumption. A production lot of 1 million packages with 10 servings each was assumed. When at least half of the servings were cooked prior to consumption, the median risk of invasive listeriosis per serving in both the general and susceptible population was <1.0 × 10^-16 with the median (5th, 95th percentiles) predicted number of illnesses per lot as 0 (0, 0) and 0 (0, 1) under the exponential and Weibull-gamma dose-response functions, respectively. In scenarios in which all servings are consumed as ready-to-eat, the median predicted risk per serving was 1.8 × 10^-13 and 7.8 × 10^-12 in the general and susceptible populations, respectively. The median (5th, 95th percentile) number of illnesses was 0 (0, 0) and 0 (0, 6) for the exponential and Weibull-Gamma models, respectively. Classification tree analysis highlighted initial concentration of LM in the lot, temperature at which the product is thawed, and whether a serving is cooked as main predictors for illness from a lot. Overall, the FFLLoRA provides frozen food manufacturers with a tool to assess LM contamination and consumer behavior when managing rare and/or minimal contamination events in frozen foods.