Isolation and Characterization of Biosurfactant-Producing Bacteria from Amapaense Amazon Soils.

The objective of this research was to perform screening of biosurfactant-producing bacteria from Amapaense Amazon soils. Floodplain- and upland-forest soils of three municipalities of the Amapá state were isolated and identified. The isolates were cultured in nutrient broth with olive oil, and their extracts were evaluated according to drop collapse, oil dispersion, emulsification, and surface tension tests. From three hundred and eighteen isolates, the 43 bacteria were selected and identified by 16S rDNA gene sequencing, indicating the presence of three different genera, Serratia, Paenibacillus, and Citrobacter. The extracellular biosurfactant production pointed out the 15 most efficient bacteria that presented high emulsification capacity (E 24 > 48%) and stability (less than 10% of drop after 72 h) and great potential to reduce the surface tension (varying from 49.40 to 34.50 mN·m-1). Cluster analysis classified genetically related isolates in different groups, which can be connected to differences in the amount or the sort of biosurfactants. Isolates from Serratia genus presented better emulsification capacity and produced a more significant surface tension drop, indicating a promising potential for biotechnological applications.

Comparison between Thin-Layer Models and Non-Traditional Methods in the Modelling of Drying Kinetics of Crustacean Wastes

Abstract This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60oC. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R 2 adj >0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.