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Abstract In this study we aimed to compare the mineralogical, thermal, physicochemical, and biological characteristics of recent organic carbon-rich sediments (‘sapropels’) from three geographically distant Romanian lakes (Tekirghiol and Amara, SE Romania, and Ursu, Central Romania) with distinct hydrogeochemical origins, presently used for pelotherapy. The investigated lakes were classified as inland brackish Na-Cl-sulfated type (Amara), coastal moderately saline and inland hypersaline Na-Cl types (Tekirghiol and Ursu, respectively). The settled organic matter is largely composed of photosynthetic pigments derived from autochthonous phytoplankton. Kerogen was identified in the sapropel of coastal Tekirghiol Lake suggesting its incipient maturation stage. The mineral composition was fairly similar in all sapropels and mainly consisted of quartz, calcite, and aragonite. Smectite, illite, mixed layer smectite/illite appeared as major clay components. Potentially toxic elements were found in low concentrations. The physical properties (i.e., specific heat, thermal conductivity and retentivity) and cation exchange capacity are comparable to other peloids used for therapy. This study is the first comprehensive multi-approached investigation of the geochemical nature of recent sapropels in Romanian saline lakes and thus contributes to expanding our knowledge on the origin and physicochemical qualities of organic matter-rich peloids with therapeutic uses.

To improve food production via fermentation with co-cultures of microorganisms (e.g., multiple lactic acid bacteria-LAB strains), one must fully understand their metabolism and interaction patterns in various conditions. For example, LAB can bring added quality to bread by releasing several bioactive compounds when adding soy flour to wheat flour, thus revealing the great potential for functional food development. In the present work, the fermentation of three soy and wheat flour mixtures is studied using single cultures and co-cultures of Lactobacillus plantarum and Lactobacillus casei. Bio-chemical processes often require a significant amount of time to obtain the optimal amount of final product; creating a mathematical model can gain important information and aids in the optimization of the process. Consequently, mathematical modeling is used to optimize the fermentation process by following these LAB’s growth kinetics and viability. The present work uses both multiple regression and artificial neural networks (ANN) to obtain the necessary mathematical model, useful in both prediction and process optimization. The main objective is to find a model with optimal performances, evaluated using an ANOVA test. To validate each obtained model, the simulation results are compared with the experimental data.

Abstract The advanced biochemical characterisation of green, red lentil and wheat flours was performed by assessing their folic acid content as well as individual minerals, amino acids, fatty acids and volatile compounds. Moreover, a nutritionally improved wheat–lentil composite flour, with a content of 133.33 μg of folic acid/100 g, was proposed in order to assure the folic acid daily intake (200 μg) for an adult person. The wheat and lentil flours percentages used for the composite were calculated by using the equations for total material balance and folic acid content material balance. Bread was selected as model food for the composite flour due to its high daily intake (~ 250 g day−1) and to its great potential in biofortification. By this algorithm, two composite flours were developed, wheat–green lentil flour (22.21–77.79%) and wheat–red lentil flour (42.62–57.38%), their advanced biochemical characteristics being predicted based on the determined compositions of their constituents. The baking behaviour of the new developed wheat-lentils composite flours with optimised folic acid content was tested. In order to objectively compare the bread samples, texture profile analysis was considered the most relevant test. A good baking behaviour was observed for the wheat–red lentil bread, while for the wheat–green lentil composite flour, encouraging results were obtained.

Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction.