Water Solubilization Using Nonionic Surfactants from Renewable Sources in Microemulsion Systems

In this study the effect of temperature, NaCl and oils (hydrocarbons: C8–C16) on the formation and solubilization capacity of the systems of oil/monoacylglycerols (MAG):ethoxylated fatty alcohols (CEO20)/propylene glycol (PG)/water was investigated. The effects of the surfactant mixture on the phase behavior and the concentration of water or oil in the systems were studied at three temperatures (50, 55, 60 °C) and with varied NaCl solutions (0.5; 2; 11%). Electrical conductivity measurement, FTIR spectroscopy and the DSC method were applied to determine the structure and type of the microemulsions formed. The dimension of the microemulsion droplets was characterized by dynamic light scattering. It has been stated that the concentration of CEO20 has a strong influence on the shape and extent of the microemulsion areas. Addition of a nonionic surfactant to the mixture with MAG promotes an increase in the area of microemulsion formation in the phase diagrams, and these areas of isotropic region did not change considerably depending on the temperature, NaCl solution and oil type. It was found that, depending on the concentration of the surfactant mixture, it was possible to obtain U-type microemulsions with dispersed particles size distribution ranging from 25 to 50 nm and consisting of about 30–32% of the water phase in the systems. The conditions under which the microemulsion region was found (electrolyte and temperature—insensitive, comparatively low oil and surfactant concentration) could be highly useful in detergency

Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logPBS, measure of purity), solubilizate (logPsol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R2val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research

Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logPBS, measure of purity), solubilizate (logPsol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R2val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research

Standardization of in vitro digestibility and DIAAS method based on the static INFOGEST protocol

International audienceThe FAO recommends the digestible indispensable amino acid score (DIAAS) as the measure for protein quality, for which the true ileal digestibility needs to be assessed in humans or pigs. However, due to high costs and ethical concerns, the FAO strongly encourages as well the development of validated in vitro methods, which complement the in vivo experiments. Method: Recently, an in vitro workflow, based on the validated static INFOGEST protocol, was developed and compared towards in vivo data. In parallel to the validation with in vivo data, the repeatability and reproducibility of the in vitro protocol were tested in an international ring trial (RT) with the aim to establish an international ISO standard method within the International Dairy Federation (IDF). Five different dairy products (skim milk powder, whole milk powder, whey protein isolate, yoghurt, and cheese) were analyzed in 32 different laboratories from 18 different countries, across 4 continents. Results: in vitro protein digestibilities based on Nitrogen, free R-NH2, and total amino acids as well as DIAAS values were calculated and compared to in vivo data, where available. Conclusion: The in vitro method is suited for quantification of digestibility and will be further implemented to other food matrices