RESUMO
Cassava starch nanoparticles (SNP) were produced using the nanoprecipitation method after modification of starch granules using ultrasound (US) or heat-moisture treatment (HMT). To produce SNP, cassava starches were gelatinized (95 °C/30 min) and precipitated after cooling, using absolute ethanol. SNPs were isolated using centrifugation and lyophilized. The nanoparticles produced from native starch and starches modified using US or HMT, named NSNP, USNP and HSNP, respectively, were characterized in terms of their main physical or functional properties. The SNP showed cluster plate formats, which were smooth for particles produced from native starch (NSNP) and rough for particles from starch modified with US (USNP) or HMT (HSNP), with smaller size ranges presented by HSNP (~63-674 nm) than by USNP (~123-1300 nm) or NSNP (~25-1450 nm). SNP had low surface charge values and a V-type crystalline structure. FTIR and thermal analyses confirmed the reduction of crystallinity. The SNP produced after physical pretreatments (US, HMT) showed an improvement in lipophilicity, with their oil absorption capacity in decreasing order being HSNP > USNP > NSNP, which was confirmed by the significant increase in contact angles from ~68.4° (NSNP) to ~76° (USNP; HSNP). A concentration of SNP higher than 4% may be required to produce stability with 20% oil content. The emulsions produced with HSNP showed stability during the storage (7 days at 20 °C), whereas the emulsions prepared with NSNP exhibited phase separation after preparation. The results suggested that dual physical modifications could be used for the production of starch nanoparticles as stabilizers for Pickering emulsions with stable characteristics.
RESUMO
For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.