A Revista Ciência Agronômica é voltada para estudos relacionados com as Ciências Rurais e Recursos Naturais

Studies involving the use of microalgae are increasingly intensifying for the potential they present to produce biofuels, because they are a renewable energy source that does not compete directly with food production, and because they enable the obtaining of a fuel with less environmental impact when compared to fossil fuel. In this context, the use of microalgae is directly associated to its capacity to be produced on a large scale and to be extracted from the culture medium. Rheological studies are important for obtaining the information needed in the elaboration of projects and equipment that will be used in various operations existing in systems of production and extraction of algal biomass. In the evaluation of different levels of dry biomass concentration, studies have been conducted of the rheological behavior of cultures of Chlorella sp. BR001 and Scenedesmus sp. BR003. The Power Law model adjusted well to the data of shear stress as a function of strain rate. In all concentrations the cultures showed non-Newtonian behavior. It was observed to Scenedesmus sp. BR003 little effect of biomass concentration on the apparent viscosity and shear stress.

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.