Oral Session 4: Innovative Approaches for Ensuring Safety of Traditional Foods

Introduction: Next to GHP, HACCP plan and traceability, Quantitative Microbiological Risk Assessment (QMRA) coupled to predictive microbiology (PM) have emerged as useful tools to manage the safety of food products. Since 2003, the French dairy sector has developed risk-based tools to help dairy manufacturers in this purpose. The objective of this presentation is to provide a feedback on the use of these tools in France. 

Methodology: Stochastic QMRA models adapted to several cheese technologies and pathogenic bacteria were developed. Inputs are : the milk contamination level (issued from routine data statistical analysis), the physico-chemical parameters during process, challenge-tests results and cardinal values of the bacteria considered. Monte-Carlo simulations allow to consider variability and uncertainty. The outputs of the QMRA models are the contamination level in the products (prevalence, concentration, variability) at each step of the process, the risk of illness, and the probability of detecting non-conformities. A web-based interface (www.maisondulait.aqr.fr) was developed to enable dairy operators to perform simulations with their own data.

Results: The tools developed were applied in several contexts: to optimize sampling plans, to assess shelf life, to better understand the impact of raw milk contamination and process parameters, to validate food safety management options such as milk sorting, etc. Studies could be performed collectively (several operators together), or individually. Results are confronted to the risk of illness decrease. 

Conclusion and Relevance: The approach developed by the French Dairy Sector has now reached a mature stage, with French dairy companies being aware of the need of QMRA and PM to perform a risk-based approach. However, increasing the formation of future quality manager and inspectors from the food safety authorities is necessary to make them routine tools. Time and human resources are generally lacking for SME, next to the need of specific and rare competences in mathematical modelling. The need for outputs focused on sampling strategy was clearly observed. Finally, raising awareness on the risk-based approach and on the tools with concrete applications through a regular communication is of particularly great importance. 

Introduction: The international market for traditional African food and beverages is growing in Europe and American countries. The success of the use of these foods and beverage is attributed to several factors including globalization, the expansion of the number of immigrants from Southern countries to the Northern one, and the development of African restaurants in large western agglomerations. As a result, the international market for these products is becoming more and more attractive. One of these emerging product is a so called “folere drink” an artisanal soft drink from northern Cameroon. Unfortunately the quality of these products are not yet guarantee. This work thus aims at determining the shelf life of this local beverage during storage at variables temperature.

Methodology: Accelerated shelf life testing conducted at 4°C, 40°C, 50°C and 60°C were used to predict the shelf. A n-order mathematical expression were used to evaluate the shelf live and deterioration of “folere drink” an artisanal soft drink from northern Cameroon. 

Results and Conclusion: The used Arrhenius model indicate that folere drink can be store for 258 day at 4±1°C, 100days at 9±3°C, 50 day at 15±5°C, 21 days at 30±2°C, and only 6 days at 25±3°C. The calculated Q₁₀ values were found to be in the range of 1.5-2.0, which is in the range for lipid oxidation in beverages reported by the literature.

Introduction: Despite the robust food safety management system used in the food industry, foodborne disease outbreaks such as listeriosis are still on the rise. Ready-to-eat foods are widely consumed, posing food safety risk if food contaminated by Listeria monocytogenes is consumed. Application of predictive microbiology could assist the food industry to understand the behaviour of this organism in these categories food products to avert this problem.

Methodology: In this study, the presence of Listeria spp. in RTE beef lasagne (n=24) and egg noodles (n=24) stored for 6 days at 5 ± 1 ^oC was evaluated for 6 months. A pack of each meal was also contaminated with L. monocytogenes ATCC 19115, using a sterile syringe, to obtain an initial concentration of 6 log₁₀ cfu/g of the product in a challenge study. Literature review on existing predictive models for L. monocytogenes in RTE foods was also conducted, and some tertiary models (ComBase, FSSP, PMP and MicroHibro) were used to predict the growth of the pathogen and compared with the results from the challenge study.

Results: Listeria spp. was detected in all the beef lasagne and in 61% of the egg noodles. Strains of L. seeligeri and L. monocytogenes were detected in the RTE meals used in this study. Results of the challenge studies indicated that these food products support the growth of L. monocytogenes during chilled storage.  Growth of L. monocytogenes predicted by ComBase, PMP, MicroHibro & FSSP in beef lasagne and egg noodles agreed with the observed growth from the challenge study with a fail-safe prediction. RTE meals supports the growth of L. monocytogenes under chilled storage indicating a high food safety risk to the public if the product is consumed. 

Conclusion and Relevance: Predictive Microbiology is a field of Food Microbiology that can be explored by the food industry and food safety regulatory bodies, especially in Africa to assist in predicting the behaviour of foodborne pathogens during storage, thereby reducing the problem of food waste as result of product shelf life and at the same time protect public health. 

Introduction: The zero tolerance for Salmonella in ready-to-eat food poses a challenge for the meat industry, particularly in shelf-stable dry-fermented sausages (DFS). This study aimed to model the behaviour of Salmonella in the traditional catalan acid and low-acid DFS ("fuet") as a function of a_w and storage temperature in order to build a decision supporting tool to set a corrective storage leading to a safer product.

Methodology: The survival of Salmonella during the storage of acid (with starter culture) and low-acid (without starter culture) DFS was evaluated through challenge testing with Salmonella inoculated in the meat batter at 6 Log cfu/g. Sausages were ripened to obtain 3 batches with different a_w. Storage was performed at 4, 8, 15 and 25ºC. Salmonella was periodically enumerated on chromogenic agar. The Weibull model was fitted to Log counts data to estimate inactivation kinetic parameters. The impact of temperature and a_w was evaluated using a polynomial approach. 

Results: Salmonella viability decreased during storage, the Weibull model satisfactorily fitted the data (R² adjusted>0.88). The effect of a_w and temperature was statistically significant, including a linear term for delta (δ) and a quadratic term for shape parameter (p).  Decreasing a_w and increasing temperature caused a decrease of δ. A common p was obtained for acid and low-acid DFS, while δ was product specific, indicating that starter culture affected the time required for the first Log reduction. Interestingly, a correlation was observed between the δ values obtained for low-acid and acid DFS: the time to 1 log-inactivation in acid sausages was 50% (4ºC) to 21% (25ºC) shorter than in low-acid sausages. Technologically, a corrective storage at room temperature for 1 week or less could be implemented as a valid post-lethality treatment before fermented sausages are shipped to the market.

Conclusion and Relevance: Based on technologically and commercially feasible time-temperature corrective storage periods, the developed models can be used to define post-lethality treatments to enhance Salmonella inactivation in DFS. The impact of starter culture, a_w and storage temperature in Salmonella inactivation has been quantified and a decision-supporting tool to design a corrective storage as risk mitigation strategy before commercial launching has been developed.

Introduction:  Almond, Honey and Argan Oil Spread, also known as Amlou, is a traditional recipe widely consumed in THE South of Morocco, and it is characterised by an artisanal production chain in which microbial contamination may be promoted. The aim of this study was to develop a model to predict the survival of E. coli ATCC 25922 in a medium matrix similar to Amlou as a function of argan oil, sugar concentration and peptone concentration. 

Methodology: Doehlert matrix design and response surface methodology were used to find a relationship between factors (argan oil, sugar concentration and peptone concentration), and kinetics parameters: death rate (DR) and survival period (SP). The data generated within different conditions were fitted using Baranyi function. Then, separate quadratics models were developed with decimal logarithms of SP and DR as a function of argan oil, sugar concentration and peptone concentrations to predict the evolution of E. coli in Amlou. 

Results: Sugar concentration was found to have a significant effect on DR but not on SP; while argan oil and peptone concentration have shown a significant effect on SP, and not on DR. For the death rate, the coefficient of determination (R²), root means square error (RMSE), and standard error of prediction percentage (SEP) obtained were 0.95, 0.0095 and 22.22%, respectively. Although the R² was 0.96, RMSE and SEP were 16.1 and 43.18%, a bit high for survival period. 

Conclusions and Relevance: The statistical indexes obtained suggested that the models built to predict DR and SP of E. coli under experimental conditions fit well to the data. The results obtained in the present work could be used by producers of an artisanal product to predict E. coli response in foods having a similar composition.

Introduction: Lactococcus lactis belongs to the lactic acid bacteria (LAB) group widely found in Mediterranean dairy products and generally considered as beneficial microorganisms. It is used to improve the quality of the food products by producing many beneficial substances. Exopolysaccharides (EPS) from LAB have attracted special attention as valuable bioactive because of their applications in industry. Bacterial EPS encompass a broad range of complex chemical structures and consequently exhibit different properties such as antioxidant, antibiofilm and antitumor activities. However, the low yield of EPS production severely hindered its application. The main objective of this work is to study the effect of medium components and culture conditions on cell growth and EPS production by L.  lactis A2.

Methodology:  Different values of maltose, yeast extract, pH, and cultivation time were evaluated in order to screen the factors influencing EPS production. The variables:  maltose concentration, yeast extract, and pH were selected for the central composite design (CCD). Five levels ( -α, -1, 0, 1, + α) are shown and a 2³ factorial design were conducted with six axial points (also called star points), and two replications of the center point (all the factors were at level 0), resulting in a total of 20 runs. 

Results: The results showed that the optimum medium for the growth of L. lactis A2 (OD 620 nm) was obtained at the composition of maltose (60 g/L), yeast extract (45 g/L), and initial pH (7). For EPS, maximum yields of 26.02 g/L were obtained at the optimal conditions of maltose (66 g/L), yeast extract (63 g/L), and initial pH (5.75). No correlation was shown between cell growth and EPS production.

Conclusion and Relevance: This study provided a high yield EPS by L. lactis A2, which suggests the use of the strain as a new source for the production of the bioactive polysaccharide.  The results impart a reference for large-scale extraction of EPS by L.  lactis A2 in industrial fermentation.