Tag Archives: biofilm formation

Research Salmonella Biofilm Resistance

ASMOrg Salmonella

Salmonellosis is the second most common cause of food-borne illness worldwide. Contamination of surfaces in food processing environments may result in biofilm formation with a risk of food contamination. Effective decontamination of biofilm contaminated surfaces is challenging. Using the CDC biofilm reactor, the activity of sodium hypochlorite, sodium hydroxide and benzalkonium chloride were examined against an early (48 hours) and relatively mature (168 hours) Salmonella biofilm. All 3 agents result in reduction in viable counts of Salmonella, however only sodium hydroxide resulted in eradication of the early biofilm. None of the agents achieved eradication of mature biofilm, even at 90-minutes contact time. Studies of activity of chemical disinfection against biofilm should include assessment of activity against mature biofilm. The difficulty of eradication of established Salmonella biofilm serves to emphasise the priority of preventing access of Salmonella to post-cook areas of food production facilities.

Research – Listeria monocytogenes Biofilm Formation

Science Direct

The foodborne pathogen Listeria monocytogenes has the ability to produce biofilms in food-processing environments and then contaminate food products, which is a major concern for food safety. The biofilm forming behaviour of 143 L. monocytogenes strains was determined in four different media that were rich, moderate or poor in nutrients at 12°C, 20°C, 30°C and 37°C. The biofilm formation was mostly influenced by temperature, resulting in decreased biofilm formation with decreasing temperature. Biofilm formation was enhanced in nutrient-poor medium rather than in nutrient-rich medium, and especially in nutrient-poor medium significantly enhanced biofilm production was observed early in biofilm maturation underlining the effect of medium on biofilm formation rate. Also serotype had a significant effect on biofilm formation and was influenced by medium used because strains from both serotype 1/2b and 1/2a formed more biofilm than serotype 4b strains in nutrient-rich medium at 20°C, 30°C and 37°C, whereas in nutrient-poor medium the biofilm production levels of serotype 1/2a and 4b strains were rather similar and lower than serotype 1/2b strains. The strains used originated from various origins, including dairy, meat, industrial environment, human and animal, and the level of biofilm formation was not significantly affected by the origin of isolation, irrespective of medium used and temperature tested. A linear model was used to correlate crystal violet staining of biofilm production to the number of viable cells within the biofilm. This showed that crystal violet staining was poorly correlated to the number of viable cells in nutrient-poor medium, and LIVE/DEAD staining and DNase I treatment revealed that this could be attributed to the presence of non-viable cells and extracellular DNA in the biofilm matrix. The significant impact of intrinsic and extrinsic factors on biofilm production of L. monocytogenes underlined that niche-specific features determine the levels of biofilm produced, and insights in biofilm formation characteristics will allow us to further optimize strategies to control the biofilm formation of L. monocytogenes.

 

Research – Antimicrobials – Listeria – Sampling Plans and Clustering

Science Direct

The present study investigated the efficacy of sub-inhibitory concentrations (SICs, concentrations not inhibiting bacterial growth) and bactericidal concentrations (MBCs) of four, generally recognized as safe (GRAS), plant-derived antimicrobials (PDAs) in inhibiting Listeria monocytogenes (LM) biofilm formation and inactivating mature LM biofilms, at 37, 25 and 4°C on polystyrene plates and stainless-steel coupons. In addition, the effect of SICs of PDAs on the expression of LM genes critical for biofilm synthesis was determined by real-time quantitative PCR. The PDAs and their SICs used for inhibition of biofilm were trans-cinnamaldehyde (TC 0.50, 0.75 mM), carvacrol (CR 0.50, 0.65 mM), thymol (TY 0.33, 0.50 mM), and eugenol (EG 1.8, 2.5 mM), whereas the PDA concentrations used for inactivating mature biofilms were 5.0 and 10.0 mM (TC, CR), 3.3 and 5.0 mM (TY), 18.5 and 25.0 mM (EG). All PDAs inhibited biofilm synthesis and inactivated fully formed LM biofilms on both matrices at three temperatures tested (P<0.05). Real-time quantitative PCR data revealed that all PDAs down-regulated critical LM biofilm-associated genes (P<0.05). Results suggest that TC, CR, TY, and EG could potentially be used to control LM biofilms in food processing environments, although further studies under commercial settings are necessary.

Science Direct

As in many cases, pathogenic microorganisms contaminate the food material as clusters or group of individual cells; the effectiveness of sampling plans based on mixture distributions representing bacterial agglomeration was assessed. In general, sampling plans that do not take into account such consideration lead to higher probabilities of accepting defective lots. Since quite often no scientific data are available in order to determine the degree of over-dispersion or clustering of the target microorganisms, in this theoretical study we compare the variance-to-mean ratio and the reciprocal of the exponent k of the negative binomial distribution (NB) as measures of dispersion. The mixture Poisson-logarithmic (Plog) model is proposed as a special case of the NB distribution, where the bacterial clusters are Poisson distributed while the individuals in each cluster follow a logarithmic distribution. In order to describe microbial data characterised by an excess of zero counts (1−π), we assess the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) distributions as alternative statistical models. The Operating Characteristic (OC) curves generated on the basis of the zero-inflated distributions were compared for fixed values of the variance-to-mean ratio and the parameter π at any mean level of contamination and sample weight adopted. The results show that assuming fixed 1/k and π for the NB and ZIP distributions, respectively, both models converge to a Poisson distribution at the producer’s quality level. In contrast, the consumer’s quality level is highly affected by assuming fixed values of 1/k and π since it increases. The OC curves generated for the NB and ZIP distributions assuming fixed values of the variance-to-mean ratio at any mean level of contamination and sample weight adopted, reveal that both the consumer’s and producer’s quality level are affected, as they both increase. Within the ZINB distribution, a separate investigation is conducted to determine which parameters are mostly responsible for describing microbial over-dispersion. As a general conclusion, for the design of sampling plans based on any statistical distribution, OC curves that reflect microbial agglomeration should be constructed considering that variance is not constant but dependant on the level of microbial concentration of the lot.