In order to find out how real Listeria monocytogenes-carrying biofilms are in industrial settings, a total of 270 environmental samples belonging to work surfaces from fish (n = 123), meat (n = 75) and dairy industries (n = 72) were analysed in order to detect L. monocytogenes. 12 samples were positive for L. monocytogenes and a total of 18 different species were identified as accompanying microbiota in fish and meat industry. No L. monocytogenes was found in samples from dairy industry. Molecular characterisation combining results of AscI and ApaI macrorestriction PFGE assays yielded 7 different subtypes of L. monocytogenes sharing in 71.43% of cases the same serogroup (1/2a-3a). Results from dynamic numerical characterisation between L. monocytogenes monospecies biofilms on stainless steel (SS) using MATLAB-based tool BIOFILMDIVER demonstrated that except in isolate A1, in which a significant increase in the percentage of covered area (CA), average diffusion distance (ADD) and maximum diffusion distance (MDD) was observed after 120 h of culture, no significant differences were observed in the dynamics of the rest of the L. monocytogenes isolates. Quantitative dual-species biofilm association experiments performed on SS indicated that L. monocytogenes cell counts presented lower values in mixed-species cultures with certain species at 24 and 48 h compared with mono-species culture. However, they remained unaltered after 72 h except when co-cultured with Serratia fonticola which presented differences in all sampling times and was also the dominant species within the dual-species biofilm. When considering frequency of appearance of accompanying species, an ecological distribution was demonstrated as Escherichia coli appeared to be the most abundant in fish industry and Carnobacterium spp. in meat industry.

As the structure of biofilms plays a key role in their resistance and persistence, this work presents for the first time the numerical characterization of the temporal evolution of biofilm structures formed by three Listeria monocytogenes strains on two types of stainless-steel supports, AISI 304 SS No. 2B and AISI 316 SS No. 2R. Counting methods, motility tests, fluorescence microscopy and image analysis were combined to study the dynamic evolution of biofilm formation and structure. Image analysis was performed with several well-known parameters as well as a newly defined parameter to quantify spatio-temporal distribution. The results confirm the interstrain variability of L. monocytogenes species regarding biofilm structure and structure evolution. Two types of biofilm were observed: homogeneous or flat and heterogeneous or clustered. Differences in clusters and in attachment and detachment processes were due mainly to the topography and composition of the two surfaces although an effect due to motility was also found.

This work shows that the combination of two-dimensional (2D) and three-dimensional (3D) analyses of images acquired by confocal laser scanning microscopy facilitates the quantitative spatiotemporal characterization of architectures formed by Listeria monocytogenes biofilms. In particular, the analysis of structural features such as maximum thickness, biovolume, areal porosity and maximum diffusion distance allowed elucidating differences in biofilm formation of three L. monocytogenes strains (L1A1, CECT5873 and CECT4032). The analysis showed a common sequence for all strains. In the first phase, independent clusters evolve to interconnected clusters and honeycomb-like structures. Flat biofilms characterized the second phase. The structures disappear in the third phase. Nevertheless, the duration of the phases differed from strain to strain. L1A1 strain exhibited the slowest dynamics and the thickest biofilms while the strain CECT4032 presented the faster dynamics and the thinnest biofilms. Also, the number of dead cells varies significantly from strain to strain. From the results of the analysis, it can be concluded that 2D parameters are critical to differentiating morphological features while 3D parameters ease the interpretation and comparative study of the different phases during the life cycle of biofilms.

Food processes, bio-processes and bio-systems are coupled systems that may involve heat, mass and momentum transfer together with kinetic processes. This work illustrates, with a number of examples, how model-based techniquesi.e. simulation, optimization and control-offer the possibility to improve our knowledge about the system at hand and facilitate process design and optimisation even in real time. The contribution is mainly based on the authors experience and illustrates concepts with several examples such as biofilm formation, gluconic acid production, deep-fat frying of potato chips and the thermal processing of packaged foods.

Listeria monocytogenes is a food-borne pathogen that can persist in food processing plants by forming biofilms on abiotic surfaces. The benefits that bacteria can gain from living in a biofilm, i.e., protection from environmental factors and tolerance to biocides, have been linked to the biofilm structure. Different L. monocytogenes strains build biofilms with diverse structures, and the underlying mechanisms for that diversity are not yet fully known. This work combines quantitative image analysis, cell counts, nutrient uptake data and mathematical modeling to provide a mechanistic insight into the dynamics of the structure of biofilms formed by L. monocytogenes L1A1 (serotype 1/2a) strain. Confocal laser scanning microscopy (CLSM) and quantitative image analysis were used to characterize the structure of L1A1 biofilms throughout time. L1A1 forms flat, thick structures; damaged or dead cells start appearing early in deep layers of the biofilm and rapidly and massively loss biomass after 4 days. We proposed several reaction-diffusion models to explain the system dynamics. Model candidates describe biomass and nutrients evolution including mechanisms of growth and cell spreading, nutrients diffusion and uptake and biofilm decay. Data fitting was used to estimate unknown model parameters and to choose the most appropriate candidate model. Remarkably, standard reaction-diffusion models could not describe the biofilm dynamics. The selected model reveals that biofilm aging and glucose impaired uptake play a critical role in L1A1 L. monocytogenes biofilm life cycle.