Effects of Mannosylerythritol-Lipids-B on Cutibacterium acnes ATCC 6919.

Mannosylerythritol-lipids-B (MEL-B) are microbial-produced glycolipids with skincare properties, notably moisturizing, antimelanogenic, antimicrobial, and antiaging. Thus, there is a potential use of MEL-B in a formulation for treating acne-prone skin. This study investigated the antimicrobial effect of MEL-B against the Gram-positive bacteria Cutibacterium acnes. The broth macro dilution method was used to evaluate the growth of C. acnes (3-4 CFU/mL), in the absence (positive control) or presence of MEL-B (128, 192, 256, and 512 µg/mL). Additionally, the leakage of genetic materials was used to determine the potential drug-induced membrane disruption of glycolipids. The amount of DNA and RNA release was quantified spectrophotometrically at 260 nm. Macro dilution technique and membrane integrity experiments showed that MEL-B does not have antimicrobial activity against C. acnes. Indeed, MEL-B assisted C. acnes growth. Ultimately, MEL-B has been reported as a remarkably active compound for skincare formulations; however, preliminarily, it should be avoided for acneic skin.

Engineering modeling frameworks for microbial food safety at various scales.

The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such model-based approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer). A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.

Modeling the influence of propionic acid concentration and pH on the kinetics of Salmonella Typhimurium.

Salmonella Typhimurium is a foodborne pathogen often found in the poultry production chain. Antibiotics have been used to reduce S. Typhimurium contamination in poultry aviaries and improve chicken growth. However, antibiotics were banned in several countries. Alternatively, organic acids, such as propionic acid (PA), can control pathogens. This study determined the PA minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and mathematically modeled S. Typhimurium growth/inactivation kinetics under the influence of PA at different pH values (4.5, 5.5, and 6.5) which are within the pH range of the chicken gastrointestinal tract. The PA MIC against S. Typhimurium was pH-dependent, resulting in 5.0, 3.5 and 9.0 mM undissociated PA at pH 4.5, 5.5, and 6.5, respectively. The Baranyi and Roberts and the Weibull model fit growth and inactivation data well, respectively. Secondary models were proposed. The validated model predicted 3-log reduction of S. Typhimurium in 3 h at 68.2 mM of undissociated PA and pH 4.5. The models presented a good capacity to describe the kinetics of S. Typhimurium subjected to PA, representing a useful tool to predict PA antibacterial action depending on the pH.

Spectrum crispness sensory scale correlation with instrumental acoustic high-sampling rate and mechanical analyses.

Mechanical and acoustical instrumental tests can help to predict and compare the sensory crispness of food products. This study proposed a method to correlate crispness sensory analyses to instrumental parameters using the standard products of the Spectrum Crispness Sensory Scale. An acoustic system was developed with a high-sampling rate for characterizing food crispness. Force-displacement and acoustic signals were measured during penetration and compression tests of the products in the Spectrum Scale. A band-pass filter suppressed the texture analyzer's engine and gearbox noise. High correlations were obtained between acoustical and sensory parameters in both tests; however, the penetration test better differentiated the products. The high-sampling rate acoustical measurement system was capable of discriminate crispy products, although the trained sensory panel was still more sensitive to small differences.

From Culture-Medium-Based Models to Applications to Food: Predicting the Growth of B. cereus in Reconstituted Infant Formulae.

Predictive models of the growth of foodborne organisms are commonly based on data generated in laboratory medium. It is a crucial question how to apply the predictions to realistic food scenarios. A simple approach is to assume that the bias factor, i.e., the ratio between the maximum specific growth rate in culture medium and the food in question is constant in the region of interest of the studied environmental variables. In this study, we investigate the validity of this assumption using two well-known link functions, the square-root and the natural logarithm, both having advantageous properties when modeling the variation of the maximum specific growth rate with temperature. The main difference between the two approaches appears in terms of the respective residuals as the temperature decreases to its minimum. The model organism was Bacillus cereus. Three strains (B594, B596, and F4810/72) were grown in Reconstituted Infant Formulae, while one of them (F4810/72) was grown also in culture medium to calculate the bias factor. Their growth parameters were estimated using viable count measurements at temperatures ranging from 12 to 25°C. We utilized the fact that, if the bias factor is independent of the temperature, then the minimum growth temperature parameter of the square-root model of Ratkowsky et al. (1982) is the same for culture medium and food. We concluded, supported also by mathematical analysis, that the Ratkowsky model works well but its rearrangement for the natural logarithm of the specific growth rate is more appropriate for practical regression. On the other hand, when analyzing mixed culture data, available in the ComBase database, we observed a trend different from the one generated by pure cultures. This suggests that the identity of the strains dominating the growth of mixed cultures depends on the temperature. Such analysis can increase the accuracy of predictive models, based on culture medium, to food scenarios, bringing significant saving for the food industry.